-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/


-- | Amazon Machine Learning SDK.
--   
--   The types from this library are intended to be used with
--   <a>amazonka</a>, which provides mechanisms for specifying AuthN/AuthZ
--   information, sending requests, and receiving responses.
--   
--   Lenses are used for constructing and manipulating types, due to the
--   depth of nesting of AWS types and transparency regarding
--   de/serialisation into more palatable Haskell values. The provided
--   lenses should be compatible with any of the major lens libraries such
--   as <a>lens</a> or <a>lens-family-core</a>.
--   
--   See <a>Network.AWS.MachineLearning</a> or <a>the AWS documentation</a>
--   to get started.
@package amazonka-ml
@version 1.4.5


module Network.AWS.MachineLearning.Types

-- | API version <tt>2014-12-12</tt> of the Amazon Machine Learning SDK
--   configuration.
machineLearning :: Service

-- | Prism for InvalidTagException' errors.
_InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError

-- | An error on the server occurred when trying to process a request.
_InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError

-- | An error on the client occurred. Typically, the cause is an invalid
--   input value.
_InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError

-- | A second request to use or change an object was not allowed. This can
--   result from retrying a request using a parameter that was not present
--   in the original request.
_IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError

-- | Prism for TagLimitExceededException' errors.
_TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The exception is thrown when a predict request is made to an unmounted
--   <tt>MLModel</tt> .
_PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError

-- | A specified resource cannot be located.
_ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The subscriber exceeded the maximum number of operations. This
--   exception can occur when listing objects such as <tt>DataSource</tt> .
_LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The function used to train an <tt>MLModel</tt> . Training choices
--   supported by Amazon ML include the following:
--   
--   <ul>
--   <li><tt>SGD</tt> - Stochastic Gradient Descent. *
--   <tt>RandomForest</tt> - Random forest of decision trees.</li>
--   </ul>
data Algorithm
SGD :: Algorithm

-- | A list of the variables to use in searching or filtering
--   <tt>BatchPrediction</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>BatchPrediction</tt> creation date. * <tt>Status</tt> - Sets the
--   search criteria to <tt>BatchPrediction</tt> status. * <tt>Name</tt> -
--   Sets the search criteria to the contents of <tt>BatchPrediction</tt>
--   ____ <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>BatchPrediction</tt> creation. *
--   <tt>MLModelId</tt> - Sets the search criteria to the <tt>MLModel</tt>
--   used in the <tt>BatchPrediction</tt> . * <tt>DataSourceId</tt> - Sets
--   the search criteria to the <tt>DataSource</tt> used in the
--   <tt>BatchPrediction</tt> . * <tt>DataURI</tt> - Sets the search
--   criteria to the data file(s) used in the <tt>BatchPrediction</tt> .
--   The URL can identify either a file or an Amazon Simple Storage Service
--   (Amazon S3) bucket or directory.</li>
--   </ul>
data BatchPredictionFilterVariable
BatchCreatedAt :: BatchPredictionFilterVariable
BatchDataSourceId :: BatchPredictionFilterVariable
BatchDataURI :: BatchPredictionFilterVariable
BatchIAMUser :: BatchPredictionFilterVariable
BatchLastUpdatedAt :: BatchPredictionFilterVariable
BatchMLModelId :: BatchPredictionFilterVariable
BatchName :: BatchPredictionFilterVariable
BatchStatus :: BatchPredictionFilterVariable

-- | A list of the variables to use in searching or filtering
--   <tt>DataSource</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>DataSource</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>DataSource</tt> status. * <tt>Name</tt> - Sets the
--   search criteria to the contents of <tt>DataSource</tt> ____
--   <tt>Name</tt> . * <tt>DataUri</tt> - Sets the search criteria to the
--   URI of data files used to create the <tt>DataSource</tt> . The URI can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory. * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>DataSource</tt> creation.</li>
--   </ul>
data DataSourceFilterVariable
DataCreatedAt :: DataSourceFilterVariable
DataDATALOCATIONS3 :: DataSourceFilterVariable
DataIAMUser :: DataSourceFilterVariable
DataLastUpdatedAt :: DataSourceFilterVariable
DataName :: DataSourceFilterVariable
DataStatus :: DataSourceFilterVariable

-- | Contains the key values of <tt>DetailsMap</tt> :
--   <tt>PredictiveModelType</tt> - Indicates the type of the
--   <tt>MLModel</tt> . <tt>Algorithm</tt> - Indicates the algorithm that
--   was used for the <tt>MLModel</tt> .
data DetailsAttributes
Algorithm :: DetailsAttributes
PredictiveModelType :: DetailsAttributes

-- | Object status with the following possible values:
--   
--   <ul>
--   <li><tt>PENDING</tt> * <tt>INPROGRESS</tt> * <tt>FAILED</tt> *
--   <tt>COMPLETED</tt> * <tt>DELETED</tt></li>
--   </ul>
data EntityStatus
ESCompleted :: EntityStatus
ESDeleted :: EntityStatus
ESFailed :: EntityStatus
ESInprogress :: EntityStatus
ESPending :: EntityStatus

-- | A list of the variables to use in searching or filtering
--   <tt>Evaluation</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>Evaluation</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>Evaluation</tt> status. * <tt>Name</tt> - Sets the
--   search criteria to the contents of <tt>Evaluation</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked an evaluation. * <tt>MLModelId</tt> - Sets
--   the search criteria to the <tt>Predictor</tt> that was evaluated. *
--   <tt>DataSourceId</tt> - Sets the search criteria to the
--   <tt>DataSource</tt> used in evaluation. * <tt>DataUri</tt> - Sets the
--   search criteria to the data file(s) used in evaluation. The URL can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory.</li>
--   </ul>
data EvaluationFilterVariable
EvalCreatedAt :: EvaluationFilterVariable
EvalDataSourceId :: EvaluationFilterVariable
EvalDataURI :: EvaluationFilterVariable
EvalIAMUser :: EvaluationFilterVariable
EvalLastUpdatedAt :: EvaluationFilterVariable
EvalMLModelId :: EvaluationFilterVariable
EvalName :: EvaluationFilterVariable
EvalStatus :: EvaluationFilterVariable
data MLModelFilterVariable
MLMFVAlgorithm :: MLModelFilterVariable
MLMFVCreatedAt :: MLModelFilterVariable
MLMFVIAMUser :: MLModelFilterVariable
MLMFVLastUpdatedAt :: MLModelFilterVariable
MLMFVMLModelType :: MLModelFilterVariable
MLMFVName :: MLModelFilterVariable
MLMFVRealtimeEndpointStatus :: MLModelFilterVariable
MLMFVStatus :: MLModelFilterVariable
MLMFVTrainingDataSourceId :: MLModelFilterVariable
MLMFVTrainingDataURI :: MLModelFilterVariable
data MLModelType
Binary :: MLModelType
Multiclass :: MLModelType
Regression :: MLModelType
data RealtimeEndpointStatus
Failed :: RealtimeEndpointStatus
None :: RealtimeEndpointStatus
Ready :: RealtimeEndpointStatus
Updating :: RealtimeEndpointStatus

-- | The sort order specified in a listing condition. Possible values
--   include the following:
--   
--   <ul>
--   <li><tt>asc</tt> - Present the information in ascending order (from
--   A-Z). * <tt>dsc</tt> - Present the information in descending order
--   (from Z-A).</li>
--   </ul>
data SortOrder
Asc :: SortOrder
Dsc :: SortOrder
data TaggableResourceType
BatchPrediction :: TaggableResourceType
DataSource :: TaggableResourceType
Evaluation :: TaggableResourceType
MLModel :: TaggableResourceType

-- | Represents the output of a <tt>GetBatchPrediction</tt> operation.
--   
--   The content consists of the detailed metadata, the status, and the
--   data file information of a <tt>Batch Prediction</tt> .
--   
--   <i>See:</i> <a>batchPrediction</a> smart constructor.
data BatchPrediction

-- | Creates a value of <a>BatchPrediction</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>bpStatus</a> - The status of the <tt>BatchPrediction</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to generate
--   predictions for a batch of observations. * <tt>INPROGRESS</tt> - The
--   process is underway. * <tt>FAILED</tt> - The request to perform a
--   batch prediction did not run to completion. It is not usable. *
--   <tt>COMPLETED</tt> - The batch prediction process completed
--   successfully. * <tt>DELETED</tt> - The <tt>BatchPrediction</tt> is
--   marked as deleted. It is not usable.</li>
--   <li><a>bpLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>bpCreatedAt</a> - The time that the <tt>BatchPrediction</tt>
--   was created. The time is expressed in epoch time.</li>
--   <li><a>bpComputeTime</a> - Undocumented member.</li>
--   <li><a>bpInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>bpMLModelId</a> - The ID of the <tt>MLModel</tt> that generated
--   predictions for the <tt>BatchPrediction</tt> request.</li>
--   <li><a>bpBatchPredictionDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> that points to the group of observations to
--   predict.</li>
--   <li><a>bpTotalRecordCount</a> - Undocumented member.</li>
--   <li><a>bpStartedAt</a> - Undocumented member.</li>
--   <li><a>bpBatchPredictionId</a> - The ID assigned to the
--   <tt>BatchPrediction</tt> at creation. This value should be identical
--   to the value of the <tt>BatchPredictionID</tt> in the request.</li>
--   <li><a>bpFinishedAt</a> - Undocumented member.</li>
--   <li><a>bpInvalidRecordCount</a> - Undocumented member.</li>
--   <li><a>bpCreatedByIAMUser</a> - The AWS user account that invoked the
--   <tt>BatchPrediction</tt> . The account type can be either an AWS root
--   account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>bpName</a> - A user-supplied name or description of the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>bpMessage</a> - A description of the most recent details about
--   processing the batch prediction request.</li>
--   <li><a>bpOutputURI</a> - The location of an Amazon S3 bucket or
--   directory to receive the operation results. The following substrings
--   are not allowed in the <tt>s3 key</tt> portion of the
--   <tt>outputURI</tt> field: <tt>:</tt>, <tt>//</tt>, <tt>/./</tt>,
--   <tt>/../</tt>.</li>
--   </ul>
batchPrediction :: BatchPrediction

-- | The status of the <tt>BatchPrediction</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to generate predictions for a batch of
--   observations. * <tt>INPROGRESS</tt> - The process is underway. *
--   <tt>FAILED</tt> - The request to perform a batch prediction did not
--   run to completion. It is not usable. * <tt>COMPLETED</tt> - The batch
--   prediction process completed successfully. * <tt>DELETED</tt> - The
--   <tt>BatchPrediction</tt> is marked as deleted. It is not usable.
bpStatus :: Lens' BatchPrediction (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | The time that the <tt>BatchPrediction</tt> was created. The time is
--   expressed in epoch time.
bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | Undocumented member.
bpComputeTime :: Lens' BatchPrediction (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that generated predictions for the
--   <tt>BatchPrediction</tt> request.
bpMLModelId :: Lens' BatchPrediction (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that points to the group of
--   observations to predict.
bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text)

-- | Undocumented member.
bpTotalRecordCount :: Lens' BatchPrediction (Maybe Integer)

-- | Undocumented member.
bpStartedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | The ID assigned to the <tt>BatchPrediction</tt> at creation. This
--   value should be identical to the value of the
--   <tt>BatchPredictionID</tt> in the request.
bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text)

-- | Undocumented member.
bpFinishedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | Undocumented member.
bpInvalidRecordCount :: Lens' BatchPrediction (Maybe Integer)

-- | The AWS user account that invoked the <tt>BatchPrediction</tt> . The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text)

-- | A user-supplied name or description of the <tt>BatchPrediction</tt> .
bpName :: Lens' BatchPrediction (Maybe Text)

-- | A description of the most recent details about processing the batch
--   prediction request.
bpMessage :: Lens' BatchPrediction (Maybe Text)

-- | The location of an Amazon S3 bucket or directory to receive the
--   operation results. The following substrings are not allowed in the
--   <tt>s3 key</tt> portion of the <tt>outputURI</tt> field: <tt>:</tt>,
--   <tt>//</tt>, <tt>/./</tt>, <tt>/../</tt>.
bpOutputURI :: Lens' BatchPrediction (Maybe Text)

-- | Represents the output of the <tt>GetDataSource</tt> operation.
--   
--   The content consists of the detailed metadata and data file
--   information and the current status of the <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>dataSource</a> smart constructor.
data DataSource

-- | Creates a value of <a>DataSource</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dsStatus</a> - The current status of the <tt>DataSource</tt> .
--   This element can have one of the following values: * PENDING - Amazon
--   Machine Learning (Amazon ML) submitted a request to create a
--   <tt>DataSource</tt> . * INPROGRESS - The creation process is underway.
--   * FAILED - The request to create a <tt>DataSource</tt> did not run to
--   completion. It is not usable. * COMPLETED - The creation process
--   completed successfully. * DELETED - The <tt>DataSource</tt> is marked
--   as deleted. It is not usable.</li>
--   <li><a>dsNumberOfFiles</a> - The number of data files referenced by
--   the <tt>DataSource</tt> .</li>
--   <li><a>dsLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>dsCreatedAt</a> - The time that the <tt>DataSource</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>dsComputeTime</a> - Undocumented member.</li>
--   <li><a>dsDataSourceId</a> - The ID that is assigned to the
--   <tt>DataSource</tt> during creation.</li>
--   <li><a>dsRDSMetadata</a> - Undocumented member.</li>
--   <li><a>dsDataSizeInBytes</a> - The total number of observations
--   contained in the data files that the <tt>DataSource</tt>
--   references.</li>
--   <li><a>dsStartedAt</a> - Undocumented member.</li>
--   <li><a>dsFinishedAt</a> - Undocumented member.</li>
--   <li><a>dsCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>DataSource</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>dsName</a> - A user-supplied name or description of the
--   <tt>DataSource</tt> .</li>
--   <li><a>dsDataLocationS3</a> - The location and name of the data in
--   Amazon Simple Storage Service (Amazon S3) that is used by a
--   <tt>DataSource</tt> .</li>
--   <li><a>dsComputeStatistics</a> - The parameter is <tt>true</tt> if
--   statistics need to be generated from the observation data.</li>
--   <li><a>dsMessage</a> - A description of the most recent details about
--   creating the <tt>DataSource</tt> .</li>
--   <li><a>dsRedshiftMetadata</a> - Undocumented member.</li>
--   <li><a>dsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement requirement used when this
--   <tt>DataSource</tt> was created.</li>
--   <li><a>dsRoleARN</a> - Undocumented member.</li>
--   </ul>
dataSource :: DataSource

-- | The current status of the <tt>DataSource</tt> . This element can have
--   one of the following values: * PENDING - Amazon Machine Learning
--   (Amazon ML) submitted a request to create a <tt>DataSource</tt> . *
--   INPROGRESS - The creation process is underway. * FAILED - The request
--   to create a <tt>DataSource</tt> did not run to completion. It is not
--   usable. * COMPLETED - The creation process completed successfully. *
--   DELETED - The <tt>DataSource</tt> is marked as deleted. It is not
--   usable.
dsStatus :: Lens' DataSource (Maybe EntityStatus)

-- | The number of data files referenced by the <tt>DataSource</tt> .
dsNumberOfFiles :: Lens' DataSource (Maybe Integer)

-- | The time of the most recent edit to the <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime)

-- | The time that the <tt>DataSource</tt> was created. The time is
--   expressed in epoch time.
dsCreatedAt :: Lens' DataSource (Maybe UTCTime)

-- | Undocumented member.
dsComputeTime :: Lens' DataSource (Maybe Integer)

-- | The ID that is assigned to the <tt>DataSource</tt> during creation.
dsDataSourceId :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata)

-- | The total number of observations contained in the data files that the
--   <tt>DataSource</tt> references.
dsDataSizeInBytes :: Lens' DataSource (Maybe Integer)

-- | Undocumented member.
dsStartedAt :: Lens' DataSource (Maybe UTCTime)

-- | Undocumented member.
dsFinishedAt :: Lens' DataSource (Maybe UTCTime)

-- | The AWS user account from which the <tt>DataSource</tt> was created.
--   The account type can be either an AWS root account or an AWS Identity
--   and Access Management (IAM) user account.
dsCreatedByIAMUser :: Lens' DataSource (Maybe Text)

-- | A user-supplied name or description of the <tt>DataSource</tt> .
dsName :: Lens' DataSource (Maybe Text)

-- | The location and name of the data in Amazon Simple Storage Service
--   (Amazon S3) that is used by a <tt>DataSource</tt> .
dsDataLocationS3 :: Lens' DataSource (Maybe Text)

-- | The parameter is <tt>true</tt> if statistics need to be generated from
--   the observation data.
dsComputeStatistics :: Lens' DataSource (Maybe Bool)

-- | A description of the most recent details about creating the
--   <tt>DataSource</tt> .
dsMessage :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata)

-- | A JSON string that represents the splitting and rearrangement
--   requirement used when this <tt>DataSource</tt> was created.
dsDataRearrangement :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRoleARN :: Lens' DataSource (Maybe Text)

-- | Represents the output of <tt>GetEvaluation</tt> operation.
--   
--   The content consists of the detailed metadata and data file
--   information and the current status of the <tt>Evaluation</tt> .
--   
--   <i>See:</i> <a>evaluation</a> smart constructor.
data Evaluation

-- | Creates a value of <a>Evaluation</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>eStatus</a> - The status of the evaluation. This element can
--   have one of the following values: * <tt>PENDING</tt> - Amazon Machine
--   Learning (Amazon ML) submitted a request to evaluate an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The evaluation is underway.
--   * <tt>FAILED</tt> - The request to evaluate an <tt>MLModel</tt> did
--   not run to completion. It is not usable. * <tt>COMPLETED</tt> - The
--   evaluation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>Evaluation</tt> is marked as deleted. It is not usable.</li>
--   <li><a>ePerformanceMetrics</a> - Measurements of how well the
--   <tt>MLModel</tt> performed, using observations referenced by the
--   <tt>DataSource</tt> . One of the following metrics is returned, based
--   on the type of the <tt>MLModel</tt> : * BinaryAUC: A binary
--   <tt>MLModel</tt> uses the Area Under the Curve (AUC) technique to
--   measure performance. * RegressionRMSE: A regression <tt>MLModel</tt>
--   uses the Root Mean Square Error (RMSE) technique to measure
--   performance. RMSE measures the difference between predicted and actual
--   values for a single variable. * MulticlassAvgFScore: A multiclass
--   <tt>MLModel</tt> uses the F1 score technique to measure performance.
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .</li>
--   <li><a>eLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>Evaluation</tt> . The time is expressed in epoch time.</li>
--   <li><a>eCreatedAt</a> - The time that the <tt>Evaluation</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>eComputeTime</a> - Undocumented member.</li>
--   <li><a>eInputDataLocationS3</a> - The location and name of the data in
--   Amazon Simple Storage Server (Amazon S3) that is used in the
--   evaluation.</li>
--   <li><a>eMLModelId</a> - The ID of the <tt>MLModel</tt> that is the
--   focus of the evaluation.</li>
--   <li><a>eStartedAt</a> - Undocumented member.</li>
--   <li><a>eFinishedAt</a> - Undocumented member.</li>
--   <li><a>eCreatedByIAMUser</a> - The AWS user account that invoked the
--   evaluation. The account type can be either an AWS root account or an
--   AWS Identity and Access Management (IAM) user account.</li>
--   <li><a>eName</a> - A user-supplied name or description of the
--   <tt>Evaluation</tt> .</li>
--   <li><a>eEvaluationId</a> - The ID that is assigned to the
--   <tt>Evaluation</tt> at creation.</li>
--   <li><a>eMessage</a> - A description of the most recent details about
--   evaluating the <tt>MLModel</tt> .</li>
--   <li><a>eEvaluationDataSourceId</a> - The ID of the <tt>DataSource</tt>
--   that is used to evaluate the <tt>MLModel</tt> .</li>
--   </ul>
evaluation :: Evaluation

-- | The status of the evaluation. This element can have one of the
--   following values: * <tt>PENDING</tt> - Amazon Machine Learning (Amazon
--   ML) submitted a request to evaluate an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The evaluation is underway. * <tt>FAILED</tt> -
--   The request to evaluate an <tt>MLModel</tt> did not run to completion.
--   It is not usable. * <tt>COMPLETED</tt> - The evaluation process
--   completed successfully. * <tt>DELETED</tt> - The <tt>Evaluation</tt>
--   is marked as deleted. It is not usable.
eStatus :: Lens' Evaluation (Maybe EntityStatus)

-- | Measurements of how well the <tt>MLModel</tt> performed, using
--   observations referenced by the <tt>DataSource</tt> . One of the
--   following metrics is returned, based on the type of the
--   <tt>MLModel</tt> : * BinaryAUC: A binary <tt>MLModel</tt> uses the
--   Area Under the Curve (AUC) technique to measure performance. *
--   RegressionRMSE: A regression <tt>MLModel</tt> uses the Root Mean
--   Square Error (RMSE) technique to measure performance. RMSE measures
--   the difference between predicted and actual values for a single
--   variable. * MulticlassAvgFScore: A multiclass <tt>MLModel</tt> uses
--   the F1 score technique to measure performance. For more information
--   about performance metrics, please see the <a>Amazon Machine Learning
--   Developer Guide</a> .
ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics)

-- | The time of the most recent edit to the <tt>Evaluation</tt> . The time
--   is expressed in epoch time.
eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime)

-- | The time that the <tt>Evaluation</tt> was created. The time is
--   expressed in epoch time.
eCreatedAt :: Lens' Evaluation (Maybe UTCTime)

-- | Undocumented member.
eComputeTime :: Lens' Evaluation (Maybe Integer)

-- | The location and name of the data in Amazon Simple Storage Server
--   (Amazon S3) that is used in the evaluation.
eInputDataLocationS3 :: Lens' Evaluation (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that is the focus of the evaluation.
eMLModelId :: Lens' Evaluation (Maybe Text)

-- | Undocumented member.
eStartedAt :: Lens' Evaluation (Maybe UTCTime)

-- | Undocumented member.
eFinishedAt :: Lens' Evaluation (Maybe UTCTime)

-- | The AWS user account that invoked the evaluation. The account type can
--   be either an AWS root account or an AWS Identity and Access Management
--   (IAM) user account.
eCreatedByIAMUser :: Lens' Evaluation (Maybe Text)

-- | A user-supplied name or description of the <tt>Evaluation</tt> .
eName :: Lens' Evaluation (Maybe Text)

-- | The ID that is assigned to the <tt>Evaluation</tt> at creation.
eEvaluationId :: Lens' Evaluation (Maybe Text)

-- | A description of the most recent details about evaluating the
--   <tt>MLModel</tt> .
eMessage :: Lens' Evaluation (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that is used to evaluate the
--   <tt>MLModel</tt> .
eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text)

-- | Represents the output of a <tt>GetMLModel</tt> operation.
--   
--   The content consists of the detailed metadata and the current status
--   of the <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>mLModel</a> smart constructor.
data MLModel

-- | Creates a value of <a>MLModel</a> with the minimum fields required to
--   make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>mlmStatus</a> - The current status of an <tt>MLModel</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to create an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The creation process is
--   underway. * <tt>FAILED</tt> - The request to create an
--   <tt>MLModel</tt> didn't run to completion. The model isn't usable. *
--   <tt>COMPLETED</tt> - The creation process completed successfully. *
--   <tt>DELETED</tt> - The <tt>MLModel</tt> is marked as deleted. It isn't
--   usable.</li>
--   <li><a>mlmLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>MLModel</tt> . The time is expressed in epoch time.</li>
--   <li><a>mlmTrainingParameters</a> - A list of the training parameters
--   in the <tt>MLModel</tt> . The list is implemented as a map of
--   key-value pairs. The following is the current set of training
--   parameters: * <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed
--   size of the model. Depending on the input data, the size of the model
--   might affect its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   * <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm, which controls overfitting the data by penalizing large
--   coefficients. This parameter tends to drive coefficients to zero,
--   resulting in sparse feature set. If you use this parameter, start by
--   specifying a small value, such as <tt>1.0E-08</tt> . The value is a
--   double that ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The
--   default is to not use L1 normalization. This parameter can't be used
--   when <tt>L2</tt> is specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm, which controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.</li>
--   <li><a>mlmScoreThresholdLastUpdatedAt</a> - The time of the most
--   recent edit to the <tt>ScoreThreshold</tt> . The time is expressed in
--   epoch time.</li>
--   <li><a>mlmCreatedAt</a> - The time that the <tt>MLModel</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>mlmComputeTime</a> - Undocumented member.</li>
--   <li><a>mlmInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>mlmMLModelId</a> - The ID assigned to the <tt>MLModel</tt> at
--   creation.</li>
--   <li><a>mlmSizeInBytes</a> - Undocumented member.</li>
--   <li><a>mlmStartedAt</a> - Undocumented member.</li>
--   <li><a>mlmScoreThreshold</a> - Undocumented member.</li>
--   <li><a>mlmFinishedAt</a> - Undocumented member.</li>
--   <li><a>mlmAlgorithm</a> - The algorithm used to train the
--   <tt>MLModel</tt> . The following algorithm is supported: *
--   <tt>SGD</tt> -- Stochastic gradient descent. The goal of <tt>SGD</tt>
--   is to minimize the gradient of the loss function.</li>
--   <li><a>mlmCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>MLModel</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>mlmName</a> - A user-supplied name or description of the
--   <tt>MLModel</tt> .</li>
--   <li><a>mlmEndpointInfo</a> - The current endpoint of the
--   <tt>MLModel</tt> .</li>
--   <li><a>mlmTrainingDataSourceId</a> - The ID of the training
--   <tt>DataSource</tt> . The <tt>CreateMLModel</tt> operation uses the
--   <tt>TrainingDataSourceId</tt> .</li>
--   <li><a>mlmMessage</a> - A description of the most recent details about
--   accessing the <tt>MLModel</tt> .</li>
--   <li><a>mlmMLModelType</a> - Identifies the <tt>MLModel</tt> category.
--   The following are the available types: * <tt>REGRESSION</tt> -
--   Produces a numeric result. For example, "What price should a house be
--   listed at?" * <tt>BINARY</tt> - Produces one of two possible results.
--   For example, "Is this a child-friendly web site?". *
--   <tt>MULTICLASS</tt> - Produces one of several possible results. For
--   example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".</li>
--   </ul>
mLModel :: MLModel

-- | The current status of an <tt>MLModel</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to create an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The creation process is underway. *
--   <tt>FAILED</tt> - The request to create an <tt>MLModel</tt> didn't run
--   to completion. The model isn't usable. * <tt>COMPLETED</tt> - The
--   creation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>MLModel</tt> is marked as deleted. It isn't usable.
mlmStatus :: Lens' MLModel (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>MLModel</tt> . The time is
--   expressed in epoch time.
mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

-- | A list of the training parameters in the <tt>MLModel</tt> . The list
--   is implemented as a map of key-value pairs. The following is the
--   current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   * <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm, which controls overfitting the data by penalizing large
--   coefficients. This parameter tends to drive coefficients to zero,
--   resulting in sparse feature set. If you use this parameter, start by
--   specifying a small value, such as <tt>1.0E-08</tt> . The value is a
--   double that ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The
--   default is to not use L1 normalization. This parameter can't be used
--   when <tt>L2</tt> is specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm, which controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.
mlmTrainingParameters :: Lens' MLModel (HashMap Text Text)

-- | The time of the most recent edit to the <tt>ScoreThreshold</tt> . The
--   time is expressed in epoch time.
mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

-- | The time that the <tt>MLModel</tt> was created. The time is expressed
--   in epoch time.
mlmCreatedAt :: Lens' MLModel (Maybe UTCTime)

-- | Undocumented member.
mlmComputeTime :: Lens' MLModel (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text)

-- | The ID assigned to the <tt>MLModel</tt> at creation.
mlmMLModelId :: Lens' MLModel (Maybe Text)

-- | Undocumented member.
mlmSizeInBytes :: Lens' MLModel (Maybe Integer)

-- | Undocumented member.
mlmStartedAt :: Lens' MLModel (Maybe UTCTime)

-- | Undocumented member.
mlmScoreThreshold :: Lens' MLModel (Maybe Double)

-- | Undocumented member.
mlmFinishedAt :: Lens' MLModel (Maybe UTCTime)

-- | The algorithm used to train the <tt>MLModel</tt> . The following
--   algorithm is supported: * <tt>SGD</tt> -- Stochastic gradient descent.
--   The goal of <tt>SGD</tt> is to minimize the gradient of the loss
--   function.
mlmAlgorithm :: Lens' MLModel (Maybe Algorithm)

-- | The AWS user account from which the <tt>MLModel</tt> was created. The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
mlmCreatedByIAMUser :: Lens' MLModel (Maybe Text)

-- | A user-supplied name or description of the <tt>MLModel</tt> .
mlmName :: Lens' MLModel (Maybe Text)

-- | The current endpoint of the <tt>MLModel</tt> .
mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo)

-- | The ID of the training <tt>DataSource</tt> . The
--   <tt>CreateMLModel</tt> operation uses the
--   <tt>TrainingDataSourceId</tt> .
mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text)

-- | A description of the most recent details about accessing the
--   <tt>MLModel</tt> .
mlmMessage :: Lens' MLModel (Maybe Text)

-- | Identifies the <tt>MLModel</tt> category. The following are the
--   available types: * <tt>REGRESSION</tt> - Produces a numeric result.
--   For example, "What price should a house be listed at?" *
--   <tt>BINARY</tt> - Produces one of two possible results. For example,
--   "Is this a child-friendly web site?". * <tt>MULTICLASS</tt> - Produces
--   one of several possible results. For example, "Is this a HIGH-, LOW-,
--   or MEDIUM-risk trade?".
mlmMLModelType :: Lens' MLModel (Maybe MLModelType)

-- | Measurements of how well the <tt>MLModel</tt> performed on known
--   observations. One of the following metrics is returned, based on the
--   type of the <tt>MLModel</tt> :
--   
--   <ul>
--   <li>BinaryAUC: The binary <tt>MLModel</tt> uses the Area Under the
--   Curve (AUC) technique to measure performance.</li>
--   <li>RegressionRMSE: The regression <tt>MLModel</tt> uses the Root Mean
--   Square Error (RMSE) technique to measure performance. RMSE measures
--   the difference between predicted and actual values for a single
--   variable.</li>
--   <li>MulticlassAvgFScore: The multiclass <tt>MLModel</tt> uses the F1
--   score technique to measure performance.</li>
--   </ul>
--   
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .
--   
--   <i>See:</i> <a>performanceMetrics</a> smart constructor.
data PerformanceMetrics

-- | Creates a value of <a>PerformanceMetrics</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pmProperties</a> - Undocumented member.</li>
--   </ul>
performanceMetrics :: PerformanceMetrics

-- | Undocumented member.
pmProperties :: Lens' PerformanceMetrics (HashMap Text Text)

-- | The output from a <tt>Predict</tt> operation:
--   
--   <ul>
--   <li><tt>Details</tt> - Contains the following attributes:
--   <tt>DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY |
--   MULTICLASS</tt> <tt>DetailsAttributes.ALGORITHM - SGD</tt></li>
--   <li><tt>PredictedLabel</tt> - Present for either a <tt>BINARY</tt> or
--   <tt>MULTICLASS</tt> <tt>MLModel</tt> request.</li>
--   <li><tt>PredictedScores</tt> - Contains the raw classification score
--   corresponding to each label.</li>
--   <li><tt>PredictedValue</tt> - Present for a <tt>REGRESSION</tt>
--   <tt>MLModel</tt> request.</li>
--   </ul>
--   
--   <i>See:</i> <a>prediction</a> smart constructor.
data Prediction

-- | Creates a value of <a>Prediction</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pPredictedValue</a> - The prediction value for
--   <tt>REGRESSION</tt> <tt>MLModel</tt> .</li>
--   <li><a>pPredictedLabel</a> - The prediction label for either a
--   <tt>BINARY</tt> or <tt>MULTICLASS</tt> <tt>MLModel</tt> .</li>
--   <li><a>pPredictedScores</a> - Undocumented member.</li>
--   <li><a>pDetails</a> - Undocumented member.</li>
--   </ul>
prediction :: Prediction

-- | The prediction value for <tt>REGRESSION</tt> <tt>MLModel</tt> .
pPredictedValue :: Lens' Prediction (Maybe Double)

-- | The prediction label for either a <tt>BINARY</tt> or
--   <tt>MULTICLASS</tt> <tt>MLModel</tt> .
pPredictedLabel :: Lens' Prediction (Maybe Text)

-- | Undocumented member.
pPredictedScores :: Lens' Prediction (HashMap Text Double)

-- | Undocumented member.
pDetails :: Lens' Prediction (HashMap DetailsAttributes Text)

-- | The data specification of an Amazon Relational Database Service
--   (Amazon RDS) <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>rdsDataSpec</a> smart constructor.
data RDSDataSpec

-- | Creates a value of <a>RDSDataSpec</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdsDataSchemaURI</a> - The Amazon S3 location of the
--   <tt>DataSchema</tt> .</li>
--   <li><a>rdsdsDataSchema</a> - A JSON string that represents the schema
--   for an Amazon RDS <tt>DataSource</tt> . The <tt>DataSchema</tt>
--   defines the structure of the observation data in the data file(s)
--   referenced in the <tt>DataSource</tt> . A <tt>DataSchema</tt> is not
--   required if you specify a <tt>DataSchemaUri</tt> Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>rdsdsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>rdsdsDatabaseInformation</a> - Describes the
--   <tt>DatabaseName</tt> and <tt>InstanceIdentifier</tt> of an Amazon RDS
--   database.</li>
--   <li><a>rdsdsSelectSqlQuery</a> - The query that is used to retrieve
--   the observation data for the <tt>DataSource</tt> .</li>
--   <li><a>rdsdsDatabaseCredentials</a> - The AWS Identity and Access
--   Management (IAM) credentials that are used connect to the Amazon RDS
--   database.</li>
--   <li><a>rdsdsS3StagingLocation</a> - The Amazon S3 location for staging
--   Amazon RDS data. The data retrieved from Amazon RDS using
--   <tt>SelectSqlQuery</tt> is stored in this location.</li>
--   <li><a>rdsdsResourceRole</a> - The role
--   (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute
--   Cloud (Amazon EC2) instance to carry out the copy operation from
--   Amazon RDS to an Amazon S3 task. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   <li><a>rdsdsServiceRole</a> - The role (DataPipelineDefaultRole)
--   assumed by AWS Data Pipeline service to monitor the progress of the
--   copy task from Amazon RDS to Amazon S3. For more information, see
--   <a>Role templates</a> for data pipelines.</li>
--   <li><a>rdsdsSubnetId</a> - The subnet ID to be used to access a
--   VPC-based RDS DB instance. This attribute is used by Data Pipeline to
--   carry out the copy task from Amazon RDS to Amazon S3.</li>
--   <li><a>rdsdsSecurityGroupIds</a> - The security group IDs to be used
--   to access a VPC-based RDS DB instance. Ensure that there are
--   appropriate ingress rules set up to allow access to the RDS DB
--   instance. This attribute is used by Data Pipeline to carry out the
--   copy operation from Amazon RDS to an Amazon S3 task.</li>
--   </ul>
rdsDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec

-- | The Amazon S3 location of the <tt>DataSchema</tt> .
rdsdsDataSchemaURI :: Lens' RDSDataSpec (Maybe Text)

-- | A JSON string that represents the schema for an Amazon RDS
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . A <tt>DataSchema</tt> is not required if you
--   specify a <tt>DataSchemaUri</tt> Define your <tt>DataSchema</tt> as a
--   series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text)

-- | Describes the <tt>DatabaseName</tt> and <tt>InstanceIdentifier</tt> of
--   an Amazon RDS database.
rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase

-- | The query that is used to retrieve the observation data for the
--   <tt>DataSource</tt> .
rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text

-- | The AWS Identity and Access Management (IAM) credentials that are used
--   connect to the Amazon RDS database.
rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials

-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved
--   from Amazon RDS using <tt>SelectSqlQuery</tt> is stored in this
--   location.
rdsdsS3StagingLocation :: Lens' RDSDataSpec Text

-- | The role (DataPipelineDefaultResourceRole) assumed by an Amazon
--   Elastic Compute Cloud (Amazon EC2) instance to carry out the copy
--   operation from Amazon RDS to an Amazon S3 task. For more information,
--   see <a>Role templates</a> for data pipelines.
rdsdsResourceRole :: Lens' RDSDataSpec Text

-- | The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
--   service to monitor the progress of the copy task from Amazon RDS to
--   Amazon S3. For more information, see <a>Role templates</a> for data
--   pipelines.
rdsdsServiceRole :: Lens' RDSDataSpec Text

-- | The subnet ID to be used to access a VPC-based RDS DB instance. This
--   attribute is used by Data Pipeline to carry out the copy task from
--   Amazon RDS to Amazon S3.
rdsdsSubnetId :: Lens' RDSDataSpec Text

-- | The security group IDs to be used to access a VPC-based RDS DB
--   instance. Ensure that there are appropriate ingress rules set up to
--   allow access to the RDS DB instance. This attribute is used by Data
--   Pipeline to carry out the copy operation from Amazon RDS to an Amazon
--   S3 task.
rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text]

-- | The database details of an Amazon RDS database.
--   
--   <i>See:</i> <a>rdsDatabase</a> smart constructor.
data RDSDatabase

-- | Creates a value of <a>RDSDatabase</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdInstanceIdentifier</a> - The ID of an RDS DB instance.</li>
--   <li><a>rdsdDatabaseName</a> - Undocumented member.</li>
--   </ul>
rdsDatabase :: Text -> Text -> RDSDatabase

-- | The ID of an RDS DB instance.
rdsdInstanceIdentifier :: Lens' RDSDatabase Text

-- | Undocumented member.
rdsdDatabaseName :: Lens' RDSDatabase Text

-- | The database credentials to connect to a database on an RDS DB
--   instance.
--   
--   <i>See:</i> <a>rdsDatabaseCredentials</a> smart constructor.
data RDSDatabaseCredentials

-- | Creates a value of <a>RDSDatabaseCredentials</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdcUsername</a> - Undocumented member.</li>
--   <li><a>rdsdcPassword</a> - Undocumented member.</li>
--   </ul>
rdsDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials

-- | Undocumented member.
rdsdcUsername :: Lens' RDSDatabaseCredentials Text

-- | Undocumented member.
rdsdcPassword :: Lens' RDSDatabaseCredentials Text

-- | The datasource details that are specific to Amazon RDS.
--   
--   <i>See:</i> <a>rdsMetadata</a> smart constructor.
data RDSMetadata

-- | Creates a value of <a>RDSMetadata</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rmSelectSqlQuery</a> - The SQL query that is supplied during
--   <tt>CreateDataSourceFromRDS</tt> . Returns only if <tt>Verbose</tt> is
--   true in <tt>GetDataSourceInput</tt> .</li>
--   <li><a>rmDataPipelineId</a> - The ID of the Data Pipeline instance
--   that is used to carry to copy data from Amazon RDS to Amazon S3. You
--   can use the ID to find details about the instance in the Data Pipeline
--   console.</li>
--   <li><a>rmDatabase</a> - The database details required to connect to an
--   Amazon RDS.</li>
--   <li><a>rmDatabaseUserName</a> - Undocumented member.</li>
--   <li><a>rmResourceRole</a> - The role (DataPipelineDefaultResourceRole)
--   assumed by an Amazon EC2 instance to carry out the copy task from
--   Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   <li><a>rmServiceRole</a> - The role (DataPipelineDefaultRole) assumed
--   by the Data Pipeline service to monitor the progress of the copy task
--   from Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   </ul>
rdsMetadata :: RDSMetadata

-- | The SQL query that is supplied during <tt>CreateDataSourceFromRDS</tt>
--   . Returns only if <tt>Verbose</tt> is true in
--   <tt>GetDataSourceInput</tt> .
rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text)

-- | The ID of the Data Pipeline instance that is used to carry to copy
--   data from Amazon RDS to Amazon S3. You can use the ID to find details
--   about the instance in the Data Pipeline console.
rmDataPipelineId :: Lens' RDSMetadata (Maybe Text)

-- | The database details required to connect to an Amazon RDS.
rmDatabase :: Lens' RDSMetadata (Maybe RDSDatabase)

-- | Undocumented member.
rmDatabaseUserName :: Lens' RDSMetadata (Maybe Text)

-- | The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2
--   instance to carry out the copy task from Amazon RDS to Amazon S3. For
--   more information, see <a>Role templates</a> for data pipelines.
rmResourceRole :: Lens' RDSMetadata (Maybe Text)

-- | The role (DataPipelineDefaultRole) assumed by the Data Pipeline
--   service to monitor the progress of the copy task from Amazon RDS to
--   Amazon S3. For more information, see <a>Role templates</a> for data
--   pipelines.
rmServiceRole :: Lens' RDSMetadata (Maybe Text)

-- | Describes the real-time endpoint information for an <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>realtimeEndpointInfo</a> smart constructor.
data RealtimeEndpointInfo

-- | Creates a value of <a>RealtimeEndpointInfo</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>reiCreatedAt</a> - The time that the request to create the
--   real-time endpoint for the <tt>MLModel</tt> was received. The time is
--   expressed in epoch time.</li>
--   <li><a>reiEndpointURL</a> - The URI that specifies where to send
--   real-time prediction requests for the <tt>MLModel</tt> .</li>
--   <li><a>reiEndpointStatus</a> - The current status of the real-time
--   endpoint for the <tt>MLModel</tt> . This element can have one of the
--   following values: * <tt>NONE</tt> - Endpoint does not exist or was
--   previously deleted. * <tt>READY</tt> - Endpoint is ready to be used
--   for real-time predictions. * <tt>UPDATING</tt> - Updating/creating the
--   endpoint.</li>
--   <li><a>reiPeakRequestsPerSecond</a> - The maximum processing rate for
--   the real-time endpoint for <tt>MLModel</tt> , measured in incoming
--   requests per second.</li>
--   </ul>
realtimeEndpointInfo :: RealtimeEndpointInfo

-- | The time that the request to create the real-time endpoint for the
--   <tt>MLModel</tt> was received. The time is expressed in epoch time.
reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime)

-- | The URI that specifies where to send real-time prediction requests for
--   the <tt>MLModel</tt> .
reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text)

-- | The current status of the real-time endpoint for the <tt>MLModel</tt>
--   . This element can have one of the following values: * <tt>NONE</tt> -
--   Endpoint does not exist or was previously deleted. * <tt>READY</tt> -
--   Endpoint is ready to be used for real-time predictions. *
--   <tt>UPDATING</tt> - Updating/creating the endpoint.
reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus)

-- | The maximum processing rate for the real-time endpoint for
--   <tt>MLModel</tt> , measured in incoming requests per second.
reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int)

-- | Describes the data specification of an Amazon Redshift
--   <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>redshiftDataSpec</a> smart constructor.
data RedshiftDataSpec

-- | Creates a value of <a>RedshiftDataSpec</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rDataSchemaURI</a> - Describes the schema location for an
--   Amazon Redshift <tt>DataSource</tt> .</li>
--   <li><a>rDataSchema</a> - A JSON string that represents the schema for
--   an Amazon Redshift <tt>DataSource</tt> . The <tt>DataSchema</tt>
--   defines the structure of the observation data in the data file(s)
--   referenced in the <tt>DataSource</tt> . A <tt>DataSchema</tt> is not
--   required if you specify a <tt>DataSchemaUri</tt> . Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>rDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>rDatabaseInformation</a> - Describes the <tt>DatabaseName</tt>
--   and <tt>ClusterIdentifier</tt> for an Amazon Redshift
--   <tt>DataSource</tt> .</li>
--   <li><a>rSelectSqlQuery</a> - Describes the SQL Query to execute on an
--   Amazon Redshift database for an Amazon Redshift <tt>DataSource</tt>
--   .</li>
--   <li><a>rDatabaseCredentials</a> - Describes AWS Identity and Access
--   Management (IAM) credentials that are used connect to the Amazon
--   Redshift database.</li>
--   <li><a>rS3StagingLocation</a> - Describes an Amazon S3 location to
--   store the result set of the <tt>SelectSqlQuery</tt> query.</li>
--   </ul>
redshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec

-- | Describes the schema location for an Amazon Redshift
--   <tt>DataSource</tt> .
rDataSchemaURI :: Lens' RedshiftDataSpec (Maybe Text)

-- | A JSON string that represents the schema for an Amazon Redshift
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . A <tt>DataSchema</tt> is not required if you
--   specify a <tt>DataSchemaUri</tt> . Define your <tt>DataSchema</tt> as
--   a series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
rDataSchema :: Lens' RedshiftDataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text)

-- | Describes the <tt>DatabaseName</tt> and <tt>ClusterIdentifier</tt> for
--   an Amazon Redshift <tt>DataSource</tt> .
rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase

-- | Describes the SQL Query to execute on an Amazon Redshift database for
--   an Amazon Redshift <tt>DataSource</tt> .
rSelectSqlQuery :: Lens' RedshiftDataSpec Text

-- | Describes AWS Identity and Access Management (IAM) credentials that
--   are used connect to the Amazon Redshift database.
rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials

-- | Describes an Amazon S3 location to store the result set of the
--   <tt>SelectSqlQuery</tt> query.
rS3StagingLocation :: Lens' RedshiftDataSpec Text

-- | Describes the database details required to connect to an Amazon
--   Redshift database.
--   
--   <i>See:</i> <a>redshiftDatabase</a> smart constructor.
data RedshiftDatabase

-- | Creates a value of <a>RedshiftDatabase</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdDatabaseName</a> - Undocumented member.</li>
--   <li><a>rdClusterIdentifier</a> - Undocumented member.</li>
--   </ul>
redshiftDatabase :: Text -> Text -> RedshiftDatabase

-- | Undocumented member.
rdDatabaseName :: Lens' RedshiftDatabase Text

-- | Undocumented member.
rdClusterIdentifier :: Lens' RedshiftDatabase Text

-- | Describes the database credentials for connecting to a database on an
--   Amazon Redshift cluster.
--   
--   <i>See:</i> <a>redshiftDatabaseCredentials</a> smart constructor.
data RedshiftDatabaseCredentials

-- | Creates a value of <a>RedshiftDatabaseCredentials</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdcUsername</a> - Undocumented member.</li>
--   <li><a>rdcPassword</a> - Undocumented member.</li>
--   </ul>
redshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials

-- | Undocumented member.
rdcUsername :: Lens' RedshiftDatabaseCredentials Text

-- | Undocumented member.
rdcPassword :: Lens' RedshiftDatabaseCredentials Text

-- | Describes the <tt>DataSource</tt> details specific to Amazon Redshift.
--   
--   <i>See:</i> <a>redshiftMetadata</a> smart constructor.
data RedshiftMetadata

-- | Creates a value of <a>RedshiftMetadata</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>redSelectSqlQuery</a> - The SQL query that is specified during
--   <tt>CreateDataSourceFromRedshift</tt> . Returns only if
--   <tt>Verbose</tt> is true in GetDataSourceInput.</li>
--   <li><a>redRedshiftDatabase</a> - Undocumented member.</li>
--   <li><a>redDatabaseUserName</a> - Undocumented member.</li>
--   </ul>
redshiftMetadata :: RedshiftMetadata

-- | The SQL query that is specified during
--   <tt>CreateDataSourceFromRedshift</tt> . Returns only if
--   <tt>Verbose</tt> is true in GetDataSourceInput.
redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text)

-- | Undocumented member.
redRedshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase)

-- | Undocumented member.
redDatabaseUserName :: Lens' RedshiftMetadata (Maybe Text)

-- | Describes the data specification of a <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>s3DataSpec</a> smart constructor.
data S3DataSpec

-- | Creates a value of <a>S3DataSpec</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>sdsDataSchema</a> - A JSON string that represents the schema
--   for an Amazon S3 <tt>DataSource</tt> . The <tt>DataSchema</tt> defines
--   the structure of the observation data in the data file(s) referenced
--   in the <tt>DataSource</tt> . You must provide either the
--   <tt>DataSchema</tt> or the <tt>DataSchemaLocationS3</tt> . Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>sdsDataSchemaLocationS3</a> - Describes the schema location in
--   Amazon S3. You must provide either the <tt>DataSchema</tt> or the
--   <tt>DataSchemaLocationS3</tt> .</li>
--   <li><a>sdsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>sdsDataLocationS3</a> - The location of the data file(s) used
--   by a <tt>DataSource</tt> . The URI specifies a data file or an Amazon
--   Simple Storage Service (Amazon S3) directory or bucket containing data
--   files.</li>
--   </ul>
s3DataSpec :: Text -> S3DataSpec

-- | A JSON string that represents the schema for an Amazon S3
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . You must provide either the <tt>DataSchema</tt>
--   or the <tt>DataSchemaLocationS3</tt> . Define your <tt>DataSchema</tt>
--   as a series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
sdsDataSchema :: Lens' S3DataSpec (Maybe Text)

-- | Describes the schema location in Amazon S3. You must provide either
--   the <tt>DataSchema</tt> or the <tt>DataSchemaLocationS3</tt> .
sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text)

-- | The location of the data file(s) used by a <tt>DataSource</tt> . The
--   URI specifies a data file or an Amazon Simple Storage Service (Amazon
--   S3) directory or bucket containing data files.
sdsDataLocationS3 :: Lens' S3DataSpec Text

-- | A custom key-value pair associated with an ML object, such as an ML
--   model.
--   
--   <i>See:</i> <a>tag</a> smart constructor.
data Tag

-- | Creates a value of <a>Tag</a> with the minimum fields required to make
--   a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>tagValue</a> - An optional string, typically used to describe
--   or define the tag. Valid characters include Unicode letters, digits,
--   white space, _, ., /, =, +, -, %, and @.</li>
--   <li><a>tagKey</a> - A unique identifier for the tag. Valid characters
--   include Unicode letters, digits, white space, _, ., /, =, +, -, %, and
--   @.</li>
--   </ul>
tag :: Tag

-- | An optional string, typically used to describe or define the tag.
--   Valid characters include Unicode letters, digits, white space, _, .,
--   /, =, +, -, %, and @.
tagValue :: Lens' Tag (Maybe Text)

-- | A unique identifier for the tag. Valid characters include Unicode
--   letters, digits, white space, _, ., /, =, +, -, %, and @.
tagKey :: Lens' Tag (Maybe Text)


-- | Updates the <tt>BatchPredictionName</tt> of a <tt>BatchPrediction</tt>
--   .
--   
--   You can use the <tt>GetBatchPrediction</tt> operation to view the
--   contents of the updated data element.
module Network.AWS.MachineLearning.UpdateBatchPrediction

-- | Creates a value of <a>UpdateBatchPrediction</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ubpBatchPredictionId</a> - The ID assigned to the
--   <tt>BatchPrediction</tt> during creation.</li>
--   <li><a>ubpBatchPredictionName</a> - A new user-supplied name or
--   description of the <tt>BatchPrediction</tt> .</li>
--   </ul>
updateBatchPrediction :: Text -> Text -> UpdateBatchPrediction

-- | <i>See:</i> <a>updateBatchPrediction</a> smart constructor.
data UpdateBatchPrediction

-- | The ID assigned to the <tt>BatchPrediction</tt> during creation.
ubpBatchPredictionId :: Lens' UpdateBatchPrediction Text

-- | A new user-supplied name or description of the
--   <tt>BatchPrediction</tt> .
ubpBatchPredictionName :: Lens' UpdateBatchPrediction Text

-- | Creates a value of <a>UpdateBatchPredictionResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ubprsBatchPredictionId</a> - The ID assigned to the
--   <tt>BatchPrediction</tt> during creation. This value should be
--   identical to the value of the <tt>BatchPredictionId</tt> in the
--   request.</li>
--   <li><a>ubprsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
updateBatchPredictionResponse :: Int -> UpdateBatchPredictionResponse

-- | Represents the output of an <tt>UpdateBatchPrediction</tt> operation.
--   
--   You can see the updated content by using the
--   <tt>GetBatchPrediction</tt> operation.
--   
--   <i>See:</i> <a>updateBatchPredictionResponse</a> smart constructor.
data UpdateBatchPredictionResponse

-- | The ID assigned to the <tt>BatchPrediction</tt> during creation. This
--   value should be identical to the value of the
--   <tt>BatchPredictionId</tt> in the request.
ubprsBatchPredictionId :: Lens' UpdateBatchPredictionResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
ubprsResponseStatus :: Lens' UpdateBatchPredictionResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance Data.Data.Data Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Data.Data.Data Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateBatchPrediction.UpdateBatchPredictionResponse


-- | Updates the <tt>DataSourceName</tt> of a <tt>DataSource</tt> .
--   
--   You can use the <tt>GetDataSource</tt> operation to view the contents
--   of the updated data element.
module Network.AWS.MachineLearning.UpdateDataSource

-- | Creates a value of <a>UpdateDataSource</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>udsDataSourceId</a> - The ID assigned to the
--   <tt>DataSource</tt> during creation.</li>
--   <li><a>udsDataSourceName</a> - A new user-supplied name or description
--   of the <tt>DataSource</tt> that will replace the current
--   description.</li>
--   </ul>
updateDataSource :: Text -> Text -> UpdateDataSource

-- | <i>See:</i> <a>updateDataSource</a> smart constructor.
data UpdateDataSource

-- | The ID assigned to the <tt>DataSource</tt> during creation.
udsDataSourceId :: Lens' UpdateDataSource Text

-- | A new user-supplied name or description of the <tt>DataSource</tt>
--   that will replace the current description.
udsDataSourceName :: Lens' UpdateDataSource Text

-- | Creates a value of <a>UpdateDataSourceResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>udsrsDataSourceId</a> - The ID assigned to the
--   <tt>DataSource</tt> during creation. This value should be identical to
--   the value of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>udsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
updateDataSourceResponse :: Int -> UpdateDataSourceResponse

-- | Represents the output of an <tt>UpdateDataSource</tt> operation.
--   
--   You can see the updated content by using the
--   <tt>GetBatchPrediction</tt> operation.
--   
--   <i>See:</i> <a>updateDataSourceResponse</a> smart constructor.
data UpdateDataSourceResponse

-- | The ID assigned to the <tt>DataSource</tt> during creation. This value
--   should be identical to the value of the <tt>DataSourceID</tt> in the
--   request.
udsrsDataSourceId :: Lens' UpdateDataSourceResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
udsrsResponseStatus :: Lens' UpdateDataSourceResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance Data.Data.Data Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Data.Data.Data Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateDataSource.UpdateDataSourceResponse


-- | Updates the <tt>EvaluationName</tt> of an <tt>Evaluation</tt> .
--   
--   You can use the <tt>GetEvaluation</tt> operation to view the contents
--   of the updated data element.
module Network.AWS.MachineLearning.UpdateEvaluation

-- | Creates a value of <a>UpdateEvaluation</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ueEvaluationId</a> - The ID assigned to the <tt>Evaluation</tt>
--   during creation.</li>
--   <li><a>ueEvaluationName</a> - A new user-supplied name or description
--   of the <tt>Evaluation</tt> that will replace the current content.</li>
--   </ul>
updateEvaluation :: Text -> Text -> UpdateEvaluation

-- | <i>See:</i> <a>updateEvaluation</a> smart constructor.
data UpdateEvaluation

-- | The ID assigned to the <tt>Evaluation</tt> during creation.
ueEvaluationId :: Lens' UpdateEvaluation Text

-- | A new user-supplied name or description of the <tt>Evaluation</tt>
--   that will replace the current content.
ueEvaluationName :: Lens' UpdateEvaluation Text

-- | Creates a value of <a>UpdateEvaluationResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>uersEvaluationId</a> - The ID assigned to the
--   <tt>Evaluation</tt> during creation. This value should be identical to
--   the value of the <tt>Evaluation</tt> in the request.</li>
--   <li><a>uersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
updateEvaluationResponse :: Int -> UpdateEvaluationResponse

-- | Represents the output of an <tt>UpdateEvaluation</tt> operation.
--   
--   You can see the updated content by using the <tt>GetEvaluation</tt>
--   operation.
--   
--   <i>See:</i> <a>updateEvaluationResponse</a> smart constructor.
data UpdateEvaluationResponse

-- | The ID assigned to the <tt>Evaluation</tt> during creation. This value
--   should be identical to the value of the <tt>Evaluation</tt> in the
--   request.
uersEvaluationId :: Lens' UpdateEvaluationResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
uersResponseStatus :: Lens' UpdateEvaluationResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance Data.Data.Data Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Data.Data.Data Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateEvaluation.UpdateEvaluationResponse


-- | Updates the <tt>MLModelName</tt> and the <tt>ScoreThreshold</tt> of an
--   <tt>MLModel</tt> .
--   
--   You can use the <tt>GetMLModel</tt> operation to view the contents of
--   the updated data element.
module Network.AWS.MachineLearning.UpdateMLModel

-- | Creates a value of <a>UpdateMLModel</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>umlmMLModelName</a> - A user-supplied name or description of
--   the <tt>MLModel</tt> .</li>
--   <li><a>umlmScoreThreshold</a> - The <tt>ScoreThreshold</tt> used in
--   binary classification <tt>MLModel</tt> that marks the boundary between
--   a positive prediction and a negative prediction. Output values greater
--   than or equal to the <tt>ScoreThreshold</tt> receive a positive result
--   from the <tt>MLModel</tt> , such as <tt>true</tt> . Output values less
--   than the <tt>ScoreThreshold</tt> receive a negative response from the
--   <tt>MLModel</tt> , such as <tt>false</tt> .</li>
--   <li><a>umlmMLModelId</a> - The ID assigned to the <tt>MLModel</tt>
--   during creation.</li>
--   </ul>
updateMLModel :: Text -> UpdateMLModel

-- | <i>See:</i> <a>updateMLModel</a> smart constructor.
data UpdateMLModel

-- | A user-supplied name or description of the <tt>MLModel</tt> .
umlmMLModelName :: Lens' UpdateMLModel (Maybe Text)

-- | The <tt>ScoreThreshold</tt> used in binary classification
--   <tt>MLModel</tt> that marks the boundary between a positive prediction
--   and a negative prediction. Output values greater than or equal to the
--   <tt>ScoreThreshold</tt> receive a positive result from the
--   <tt>MLModel</tt> , such as <tt>true</tt> . Output values less than the
--   <tt>ScoreThreshold</tt> receive a negative response from the
--   <tt>MLModel</tt> , such as <tt>false</tt> .
umlmScoreThreshold :: Lens' UpdateMLModel (Maybe Double)

-- | The ID assigned to the <tt>MLModel</tt> during creation.
umlmMLModelId :: Lens' UpdateMLModel Text

-- | Creates a value of <a>UpdateMLModelResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>umlmrsMLModelId</a> - The ID assigned to the <tt>MLModel</tt>
--   during creation. This value should be identical to the value of the
--   <tt>MLModelID</tt> in the request.</li>
--   <li><a>umlmrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
updateMLModelResponse :: Int -> UpdateMLModelResponse

-- | Represents the output of an <tt>UpdateMLModel</tt> operation.
--   
--   You can see the updated content by using the <tt>GetMLModel</tt>
--   operation.
--   
--   <i>See:</i> <a>updateMLModelResponse</a> smart constructor.
data UpdateMLModelResponse

-- | The ID assigned to the <tt>MLModel</tt> during creation. This value
--   should be identical to the value of the <tt>MLModelID</tt> in the
--   request.
umlmrsMLModelId :: Lens' UpdateMLModelResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
umlmrsResponseStatus :: Lens' UpdateMLModelResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance Data.Data.Data Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Data.Data.Data Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Show.Show Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Read.Read Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance GHC.Classes.Eq Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.UpdateMLModel.UpdateMLModelResponse


-- | Generates a prediction for the observation using the specified <tt>ML
--   Model</tt> .
module Network.AWS.MachineLearning.Predict

-- | Creates a value of <a>Predict</a> with the minimum fields required to
--   make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pMLModelId</a> - A unique identifier of the <tt>MLModel</tt>
--   .</li>
--   <li><a>pRecord</a> - Undocumented member.</li>
--   <li><a>pPredictEndpoint</a> - Undocumented member.</li>
--   </ul>
predict :: Text -> Text -> Predict

-- | <i>See:</i> <a>predict</a> smart constructor.
data Predict

-- | A unique identifier of the <tt>MLModel</tt> .
pMLModelId :: Lens' Predict Text

-- | Undocumented member.
pRecord :: Lens' Predict (HashMap Text Text)

-- | Undocumented member.
pPredictEndpoint :: Lens' Predict Text

-- | Creates a value of <a>PredictResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>prsPrediction</a> - Undocumented member.</li>
--   <li><a>prsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
predictResponse :: Int -> PredictResponse

-- | <i>See:</i> <a>predictResponse</a> smart constructor.
data PredictResponse

-- | Undocumented member.
prsPrediction :: Lens' PredictResponse (Maybe Prediction)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
prsResponseStatus :: Lens' PredictResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.Predict.PredictResponse
instance Data.Data.Data Network.AWS.MachineLearning.Predict.PredictResponse
instance GHC.Show.Show Network.AWS.MachineLearning.Predict.PredictResponse
instance GHC.Read.Read Network.AWS.MachineLearning.Predict.PredictResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.Predict.PredictResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.Predict.Predict
instance Data.Data.Data Network.AWS.MachineLearning.Predict.Predict
instance GHC.Show.Show Network.AWS.MachineLearning.Predict.Predict
instance GHC.Read.Read Network.AWS.MachineLearning.Predict.Predict
instance GHC.Classes.Eq Network.AWS.MachineLearning.Predict.Predict
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.Predict.Predict
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.Predict.Predict
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.Predict.Predict
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.Predict.Predict
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.Predict.Predict
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.Predict.Predict
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.Predict.Predict
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.Predict.PredictResponse


-- | Returns an <tt>MLModel</tt> that includes detailed metadata, data
--   source information, and the current status of the <tt>MLModel</tt> .
--   
--   <tt>GetMLModel</tt> provides results in normal or verbose format.
module Network.AWS.MachineLearning.GetMLModel

-- | Creates a value of <a>GetMLModel</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gmlmVerbose</a> - Specifies whether the <tt>GetMLModel</tt>
--   operation should return <tt>Recipe</tt> . If true, <tt>Recipe</tt> is
--   returned. If false, <tt>Recipe</tt> is not returned.</li>
--   <li><a>gmlmMLModelId</a> - The ID assigned to the <tt>MLModel</tt> at
--   creation.</li>
--   </ul>
getMLModel :: Text -> GetMLModel

-- | <i>See:</i> <a>getMLModel</a> smart constructor.
data GetMLModel

-- | Specifies whether the <tt>GetMLModel</tt> operation should return
--   <tt>Recipe</tt> . If true, <tt>Recipe</tt> is returned. If false,
--   <tt>Recipe</tt> is not returned.
gmlmVerbose :: Lens' GetMLModel (Maybe Bool)

-- | The ID assigned to the <tt>MLModel</tt> at creation.
gmlmMLModelId :: Lens' GetMLModel Text

-- | Creates a value of <a>GetMLModelResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gmlmrsStatus</a> - The current status of the <tt>MLModel</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to describe
--   a <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The request is
--   processing. * <tt>FAILED</tt> - The request did not run to completion.
--   The ML model isn't usable. * <tt>COMPLETED</tt> - The request
--   completed successfully. * <tt>DELETED</tt> - The <tt>MLModel</tt> is
--   marked as deleted. It isn't usable.</li>
--   <li><a>gmlmrsLastUpdatedAt</a> - The time of the most recent edit to
--   the <tt>MLModel</tt> . The time is expressed in epoch time.</li>
--   <li><a>gmlmrsTrainingParameters</a> - A list of the training
--   parameters in the <tt>MLModel</tt> . The list is implemented as a map
--   of key-value pairs. The following is the current set of training
--   parameters: * <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed
--   size of the model. Depending on the input data, the size of the model
--   might affect its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling data improves a model's ability to find the optimal
--   solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   We strongly recommend that you shuffle your data. *
--   <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to zero, resulting in a
--   sparse feature set. If you use this parameter, start by specifying a
--   small value, such as <tt>1.0E-08</tt> . The value is a double that
--   ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not
--   use L1 normalization. This parameter can't be used when <tt>L2</tt> is
--   specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.</li>
--   <li><a>gmlmrsScoreThresholdLastUpdatedAt</a> - The time of the most
--   recent edit to the <tt>ScoreThreshold</tt> . The time is expressed in
--   epoch time.</li>
--   <li><a>gmlmrsCreatedAt</a> - The time that the <tt>MLModel</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>gmlmrsComputeTime</a> - The approximate CPU time in
--   milliseconds that Amazon Machine Learning spent processing the
--   <tt>MLModel</tt> , normalized and scaled on computation resources.
--   <tt>ComputeTime</tt> is only available if the <tt>MLModel</tt> is in
--   the <tt>COMPLETED</tt> state.</li>
--   <li><a>gmlmrsRecipe</a> - The recipe to use when training the
--   <tt>MLModel</tt> . The <tt>Recipe</tt> provides detailed information
--   about the observation data to use during training, and manipulations
--   to perform on the observation data during training.</li>
--   <li><a>gmlmrsInputDataLocationS3</a> - The location of the data file
--   or directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>gmlmrsMLModelId</a> - The MLModel ID, which is same as the
--   <tt>MLModelId</tt> in the request.</li>
--   <li><a>gmlmrsSizeInBytes</a> - Undocumented member.</li>
--   <li><a>gmlmrsSchema</a> - The schema used by all of the data files
--   referenced by the <tt>DataSource</tt> .</li>
--   <li><a>gmlmrsStartedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>MLModel</tt> as <tt>INPROGRESS</tt> .
--   <tt>StartedAt</tt> isn't available if the <tt>MLModel</tt> is in the
--   <tt>PENDING</tt> state.</li>
--   <li><a>gmlmrsScoreThreshold</a> - The scoring threshold is used in
--   binary classification <tt>MLModel</tt> models. It marks the boundary
--   between a positive prediction and a negative prediction. Output values
--   greater than or equal to the threshold receive a positive result from
--   the MLModel, such as <tt>true</tt> . Output values less than the
--   threshold receive a negative response from the MLModel, such as
--   <tt>false</tt> .</li>
--   <li><a>gmlmrsFinishedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>MLModel</tt> as <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> . <tt>FinishedAt</tt> is only available when the
--   <tt>MLModel</tt> is in the <tt>COMPLETED</tt> or <tt>FAILED</tt>
--   state.</li>
--   <li><a>gmlmrsCreatedByIAMUser</a> - The AWS user account from which
--   the <tt>MLModel</tt> was created. The account type can be either an
--   AWS root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>gmlmrsName</a> - A user-supplied name or description of the
--   <tt>MLModel</tt> .</li>
--   <li><a>gmlmrsLogURI</a> - A link to the file that contains logs of the
--   <tt>CreateMLModel</tt> operation.</li>
--   <li><a>gmlmrsEndpointInfo</a> - The current endpoint of the
--   <tt>MLModel</tt></li>
--   <li><a>gmlmrsTrainingDataSourceId</a> - The ID of the training
--   <tt>DataSource</tt> .</li>
--   <li><a>gmlmrsMessage</a> - A description of the most recent details
--   about accessing the <tt>MLModel</tt> .</li>
--   <li><a>gmlmrsMLModelType</a> - Identifies the <tt>MLModel</tt>
--   category. The following are the available types: * REGRESSION --
--   Produces a numeric result. For example, "What price should a house be
--   listed at?" * BINARY -- Produces one of two possible results. For
--   example, "Is this an e-commerce website?" * MULTICLASS -- Produces one
--   of several possible results. For example, "Is this a HIGH, LOW or
--   MEDIUM risk trade?"</li>
--   <li><a>gmlmrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
getMLModelResponse :: Int -> GetMLModelResponse

-- | Represents the output of a <tt>GetMLModel</tt> operation, and provides
--   detailed information about a <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>getMLModelResponse</a> smart constructor.
data GetMLModelResponse

-- | The current status of the <tt>MLModel</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to describe a <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The request is processing. * <tt>FAILED</tt> -
--   The request did not run to completion. The ML model isn't usable. *
--   <tt>COMPLETED</tt> - The request completed successfully. *
--   <tt>DELETED</tt> - The <tt>MLModel</tt> is marked as deleted. It isn't
--   usable.
gmlmrsStatus :: Lens' GetMLModelResponse (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>MLModel</tt> . The time is
--   expressed in epoch time.
gmlmrsLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | A list of the training parameters in the <tt>MLModel</tt> . The list
--   is implemented as a map of key-value pairs. The following is the
--   current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling data improves a model's ability to find the optimal
--   solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   We strongly recommend that you shuffle your data. *
--   <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to zero, resulting in a
--   sparse feature set. If you use this parameter, start by specifying a
--   small value, such as <tt>1.0E-08</tt> . The value is a double that
--   ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not
--   use L1 normalization. This parameter can't be used when <tt>L2</tt> is
--   specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.
gmlmrsTrainingParameters :: Lens' GetMLModelResponse (HashMap Text Text)

-- | The time of the most recent edit to the <tt>ScoreThreshold</tt> . The
--   time is expressed in epoch time.
gmlmrsScoreThresholdLastUpdatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | The time that the <tt>MLModel</tt> was created. The time is expressed
--   in epoch time.
gmlmrsCreatedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | The approximate CPU time in milliseconds that Amazon Machine Learning
--   spent processing the <tt>MLModel</tt> , normalized and scaled on
--   computation resources. <tt>ComputeTime</tt> is only available if the
--   <tt>MLModel</tt> is in the <tt>COMPLETED</tt> state.
gmlmrsComputeTime :: Lens' GetMLModelResponse (Maybe Integer)

-- | The recipe to use when training the <tt>MLModel</tt> . The
--   <tt>Recipe</tt> provides detailed information about the observation
--   data to use during training, and manipulations to perform on the
--   observation data during training.
gmlmrsRecipe :: Lens' GetMLModelResponse (Maybe Text)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
gmlmrsInputDataLocationS3 :: Lens' GetMLModelResponse (Maybe Text)

-- | The MLModel ID, which is same as the <tt>MLModelId</tt> in the
--   request.
gmlmrsMLModelId :: Lens' GetMLModelResponse (Maybe Text)

-- | Undocumented member.
gmlmrsSizeInBytes :: Lens' GetMLModelResponse (Maybe Integer)

-- | The schema used by all of the data files referenced by the
--   <tt>DataSource</tt> .
gmlmrsSchema :: Lens' GetMLModelResponse (Maybe Text)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>MLModel</tt> as <tt>INPROGRESS</tt> . <tt>StartedAt</tt> isn't
--   available if the <tt>MLModel</tt> is in the <tt>PENDING</tt> state.
gmlmrsStartedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | The scoring threshold is used in binary classification
--   <tt>MLModel</tt> models. It marks the boundary between a positive
--   prediction and a negative prediction. Output values greater than or
--   equal to the threshold receive a positive result from the MLModel,
--   such as <tt>true</tt> . Output values less than the threshold receive
--   a negative response from the MLModel, such as <tt>false</tt> .
gmlmrsScoreThreshold :: Lens' GetMLModelResponse (Maybe Double)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>MLModel</tt> as <tt>COMPLETED</tt> or <tt>FAILED</tt> .
--   <tt>FinishedAt</tt> is only available when the <tt>MLModel</tt> is in
--   the <tt>COMPLETED</tt> or <tt>FAILED</tt> state.
gmlmrsFinishedAt :: Lens' GetMLModelResponse (Maybe UTCTime)

-- | The AWS user account from which the <tt>MLModel</tt> was created. The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
gmlmrsCreatedByIAMUser :: Lens' GetMLModelResponse (Maybe Text)

-- | A user-supplied name or description of the <tt>MLModel</tt> .
gmlmrsName :: Lens' GetMLModelResponse (Maybe Text)

-- | A link to the file that contains logs of the <tt>CreateMLModel</tt>
--   operation.
gmlmrsLogURI :: Lens' GetMLModelResponse (Maybe Text)

-- | The current endpoint of the <tt>MLModel</tt>
gmlmrsEndpointInfo :: Lens' GetMLModelResponse (Maybe RealtimeEndpointInfo)

-- | The ID of the training <tt>DataSource</tt> .
gmlmrsTrainingDataSourceId :: Lens' GetMLModelResponse (Maybe Text)

-- | A description of the most recent details about accessing the
--   <tt>MLModel</tt> .
gmlmrsMessage :: Lens' GetMLModelResponse (Maybe Text)

-- | Identifies the <tt>MLModel</tt> category. The following are the
--   available types: * REGRESSION -- Produces a numeric result. For
--   example, "What price should a house be listed at?" * BINARY --
--   Produces one of two possible results. For example, "Is this an
--   e-commerce website?" * MULTICLASS -- Produces one of several possible
--   results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
gmlmrsMLModelType :: Lens' GetMLModelResponse (Maybe MLModelType)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
gmlmrsResponseStatus :: Lens' GetMLModelResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance Data.Data.Data Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Show.Show Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Read.Read Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Data.Data.Data Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance GHC.Show.Show Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance GHC.Read.Read Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.GetMLModel.GetMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetMLModel.GetMLModelResponse


-- | Returns an <tt>Evaluation</tt> that includes metadata as well as the
--   current status of the <tt>Evaluation</tt> .
module Network.AWS.MachineLearning.GetEvaluation

-- | Creates a value of <a>GetEvaluation</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>geEvaluationId</a> - The ID of the <tt>Evaluation</tt> to
--   retrieve. The evaluation of each <tt>MLModel</tt> is recorded and
--   cataloged. The ID provides the means to access the information.</li>
--   </ul>
getEvaluation :: Text -> GetEvaluation

-- | <i>See:</i> <a>getEvaluation</a> smart constructor.
data GetEvaluation

-- | The ID of the <tt>Evaluation</tt> to retrieve. The evaluation of each
--   <tt>MLModel</tt> is recorded and cataloged. The ID provides the means
--   to access the information.
geEvaluationId :: Lens' GetEvaluation Text

-- | Creates a value of <a>GetEvaluationResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gersStatus</a> - The status of the evaluation. This element can
--   have one of the following values: * <tt>PENDING</tt> - Amazon Machine
--   Language (Amazon ML) submitted a request to evaluate an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The evaluation is underway.
--   * <tt>FAILED</tt> - The request to evaluate an <tt>MLModel</tt> did
--   not run to completion. It is not usable. * <tt>COMPLETED</tt> - The
--   evaluation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>Evaluation</tt> is marked as deleted. It is not usable.</li>
--   <li><a>gersPerformanceMetrics</a> - Measurements of how well the
--   <tt>MLModel</tt> performed using observations referenced by the
--   <tt>DataSource</tt> . One of the following metric is returned based on
--   the type of the <tt>MLModel</tt> : * BinaryAUC: A binary
--   <tt>MLModel</tt> uses the Area Under the Curve (AUC) technique to
--   measure performance. * RegressionRMSE: A regression <tt>MLModel</tt>
--   uses the Root Mean Square Error (RMSE) technique to measure
--   performance. RMSE measures the difference between predicted and actual
--   values for a single variable. * MulticlassAvgFScore: A multiclass
--   <tt>MLModel</tt> uses the F1 score technique to measure performance.
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .</li>
--   <li><a>gersLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>Evaluation</tt> . The time is expressed in epoch time.</li>
--   <li><a>gersCreatedAt</a> - The time that the <tt>Evaluation</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>gersComputeTime</a> - The approximate CPU time in milliseconds
--   that Amazon Machine Learning spent processing the <tt>Evaluation</tt>
--   , normalized and scaled on computation resources. <tt>ComputeTime</tt>
--   is only available if the <tt>Evaluation</tt> is in the
--   <tt>COMPLETED</tt> state.</li>
--   <li><a>gersInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>gersMLModelId</a> - The ID of the <tt>MLModel</tt> that was the
--   focus of the evaluation.</li>
--   <li><a>gersStartedAt</a> - The epoch time when Amazon Machine Learning
--   marked the <tt>Evaluation</tt> as <tt>INPROGRESS</tt> .
--   <tt>StartedAt</tt> isn't available if the <tt>Evaluation</tt> is in
--   the <tt>PENDING</tt> state.</li>
--   <li><a>gersFinishedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>Evaluation</tt> as <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> . <tt>FinishedAt</tt> is only available when the
--   <tt>Evaluation</tt> is in the <tt>COMPLETED</tt> or <tt>FAILED</tt>
--   state.</li>
--   <li><a>gersCreatedByIAMUser</a> - The AWS user account that invoked
--   the evaluation. The account type can be either an AWS root account or
--   an AWS Identity and Access Management (IAM) user account.</li>
--   <li><a>gersName</a> - A user-supplied name or description of the
--   <tt>Evaluation</tt> .</li>
--   <li><a>gersLogURI</a> - A link to the file that contains logs of the
--   <tt>CreateEvaluation</tt> operation.</li>
--   <li><a>gersEvaluationId</a> - The evaluation ID which is same as the
--   <tt>EvaluationId</tt> in the request.</li>
--   <li><a>gersMessage</a> - A description of the most recent details
--   about evaluating the <tt>MLModel</tt> .</li>
--   <li><a>gersEvaluationDataSourceId</a> - The <tt>DataSource</tt> used
--   for this evaluation.</li>
--   <li><a>gersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
getEvaluationResponse :: Int -> GetEvaluationResponse

-- | Represents the output of a <tt>GetEvaluation</tt> operation and
--   describes an <tt>Evaluation</tt> .
--   
--   <i>See:</i> <a>getEvaluationResponse</a> smart constructor.
data GetEvaluationResponse

-- | The status of the evaluation. This element can have one of the
--   following values: * <tt>PENDING</tt> - Amazon Machine Language (Amazon
--   ML) submitted a request to evaluate an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The evaluation is underway. * <tt>FAILED</tt> -
--   The request to evaluate an <tt>MLModel</tt> did not run to completion.
--   It is not usable. * <tt>COMPLETED</tt> - The evaluation process
--   completed successfully. * <tt>DELETED</tt> - The <tt>Evaluation</tt>
--   is marked as deleted. It is not usable.
gersStatus :: Lens' GetEvaluationResponse (Maybe EntityStatus)

-- | Measurements of how well the <tt>MLModel</tt> performed using
--   observations referenced by the <tt>DataSource</tt> . One of the
--   following metric is returned based on the type of the <tt>MLModel</tt>
--   : * BinaryAUC: A binary <tt>MLModel</tt> uses the Area Under the Curve
--   (AUC) technique to measure performance. * RegressionRMSE: A regression
--   <tt>MLModel</tt> uses the Root Mean Square Error (RMSE) technique to
--   measure performance. RMSE measures the difference between predicted
--   and actual values for a single variable. * MulticlassAvgFScore: A
--   multiclass <tt>MLModel</tt> uses the F1 score technique to measure
--   performance. For more information about performance metrics, please
--   see the <a>Amazon Machine Learning Developer Guide</a> .
gersPerformanceMetrics :: Lens' GetEvaluationResponse (Maybe PerformanceMetrics)

-- | The time of the most recent edit to the <tt>Evaluation</tt> . The time
--   is expressed in epoch time.
gersLastUpdatedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)

-- | The time that the <tt>Evaluation</tt> was created. The time is
--   expressed in epoch time.
gersCreatedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)

-- | The approximate CPU time in milliseconds that Amazon Machine Learning
--   spent processing the <tt>Evaluation</tt> , normalized and scaled on
--   computation resources. <tt>ComputeTime</tt> is only available if the
--   <tt>Evaluation</tt> is in the <tt>COMPLETED</tt> state.
gersComputeTime :: Lens' GetEvaluationResponse (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
gersInputDataLocationS3 :: Lens' GetEvaluationResponse (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that was the focus of the evaluation.
gersMLModelId :: Lens' GetEvaluationResponse (Maybe Text)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>Evaluation</tt> as <tt>INPROGRESS</tt> . <tt>StartedAt</tt> isn't
--   available if the <tt>Evaluation</tt> is in the <tt>PENDING</tt> state.
gersStartedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>Evaluation</tt> as <tt>COMPLETED</tt> or <tt>FAILED</tt> .
--   <tt>FinishedAt</tt> is only available when the <tt>Evaluation</tt> is
--   in the <tt>COMPLETED</tt> or <tt>FAILED</tt> state.
gersFinishedAt :: Lens' GetEvaluationResponse (Maybe UTCTime)

-- | The AWS user account that invoked the evaluation. The account type can
--   be either an AWS root account or an AWS Identity and Access Management
--   (IAM) user account.
gersCreatedByIAMUser :: Lens' GetEvaluationResponse (Maybe Text)

-- | A user-supplied name or description of the <tt>Evaluation</tt> .
gersName :: Lens' GetEvaluationResponse (Maybe Text)

-- | A link to the file that contains logs of the <tt>CreateEvaluation</tt>
--   operation.
gersLogURI :: Lens' GetEvaluationResponse (Maybe Text)

-- | The evaluation ID which is same as the <tt>EvaluationId</tt> in the
--   request.
gersEvaluationId :: Lens' GetEvaluationResponse (Maybe Text)

-- | A description of the most recent details about evaluating the
--   <tt>MLModel</tt> .
gersMessage :: Lens' GetEvaluationResponse (Maybe Text)

-- | The <tt>DataSource</tt> used for this evaluation.
gersEvaluationDataSourceId :: Lens' GetEvaluationResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
gersResponseStatus :: Lens' GetEvaluationResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance Data.Data.Data Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Show.Show Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Read.Read Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Data.Data.Data Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Show.Show Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Read.Read Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.GetEvaluation.GetEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetEvaluation.GetEvaluationResponse


-- | Returns a <tt>DataSource</tt> that includes metadata and data file
--   information, as well as the current status of the <tt>DataSource</tt>
--   .
--   
--   <tt>GetDataSource</tt> provides results in normal or verbose format.
--   The verbose format adds the schema description and the list of files
--   pointed to by the DataSource to the normal format.
module Network.AWS.MachineLearning.GetDataSource

-- | Creates a value of <a>GetDataSource</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gdsVerbose</a> - Specifies whether the <tt>GetDataSource</tt>
--   operation should return <tt>DataSourceSchema</tt> . If true,
--   <tt>DataSourceSchema</tt> is returned. If false,
--   <tt>DataSourceSchema</tt> is not returned.</li>
--   <li><a>gdsDataSourceId</a> - The ID assigned to the
--   <tt>DataSource</tt> at creation.</li>
--   </ul>
getDataSource :: Text -> GetDataSource

-- | <i>See:</i> <a>getDataSource</a> smart constructor.
data GetDataSource

-- | Specifies whether the <tt>GetDataSource</tt> operation should return
--   <tt>DataSourceSchema</tt> . If true, <tt>DataSourceSchema</tt> is
--   returned. If false, <tt>DataSourceSchema</tt> is not returned.
gdsVerbose :: Lens' GetDataSource (Maybe Bool)

-- | The ID assigned to the <tt>DataSource</tt> at creation.
gdsDataSourceId :: Lens' GetDataSource Text

-- | Creates a value of <a>GetDataSourceResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gdsrsStatus</a> - The current status of the <tt>DataSource</tt>
--   . This element can have one of the following values: *
--   <tt>PENDING</tt> - Amazon ML submitted a request to create a
--   <tt>DataSource</tt> . * <tt>INPROGRESS</tt> - The creation process is
--   underway. * <tt>FAILED</tt> - The request to create a
--   <tt>DataSource</tt> did not run to completion. It is not usable. *
--   <tt>COMPLETED</tt> - The creation process completed successfully. *
--   <tt>DELETED</tt> - The <tt>DataSource</tt> is marked as deleted. It is
--   not usable.</li>
--   <li><a>gdsrsNumberOfFiles</a> - The number of data files referenced by
--   the <tt>DataSource</tt> .</li>
--   <li><a>gdsrsLastUpdatedAt</a> - The time of the most recent edit to
--   the <tt>DataSource</tt> . The time is expressed in epoch time.</li>
--   <li><a>gdsrsCreatedAt</a> - The time that the <tt>DataSource</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>gdsrsComputeTime</a> - The approximate CPU time in milliseconds
--   that Amazon Machine Learning spent processing the <tt>DataSource</tt>
--   , normalized and scaled on computation resources. <tt>ComputeTime</tt>
--   is only available if the <tt>DataSource</tt> is in the
--   <tt>COMPLETED</tt> state and the <tt>ComputeStatistics</tt> is set to
--   true.</li>
--   <li><a>gdsrsDataSourceId</a> - The ID assigned to the
--   <tt>DataSource</tt> at creation. This value should be identical to the
--   value of the <tt>DataSourceId</tt> in the request.</li>
--   <li><a>gdsrsRDSMetadata</a> - Undocumented member.</li>
--   <li><a>gdsrsDataSizeInBytes</a> - The total size of observations in
--   the data files.</li>
--   <li><a>gdsrsDataSourceSchema</a> - The schema used by all of the data
--   files of this <tt>DataSource</tt> .</li>
--   <li><a>gdsrsStartedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>DataSource</tt> as <tt>INPROGRESS</tt> .
--   <tt>StartedAt</tt> isn't available if the <tt>DataSource</tt> is in
--   the <tt>PENDING</tt> state.</li>
--   <li><a>gdsrsFinishedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>DataSource</tt> as <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> . <tt>FinishedAt</tt> is only available when the
--   <tt>DataSource</tt> is in the <tt>COMPLETED</tt> or <tt>FAILED</tt>
--   state.</li>
--   <li><a>gdsrsCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>DataSource</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>gdsrsName</a> - A user-supplied name or description of the
--   <tt>DataSource</tt> .</li>
--   <li><a>gdsrsLogURI</a> - A link to the file containing logs of
--   <tt>CreateDataSourceFrom*</tt> operations.</li>
--   <li><a>gdsrsDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>gdsrsComputeStatistics</a> - The parameter is <tt>true</tt> if
--   statistics need to be generated from the observation data.</li>
--   <li><a>gdsrsMessage</a> - The user-supplied description of the most
--   recent details about creating the <tt>DataSource</tt> .</li>
--   <li><a>gdsrsRedshiftMetadata</a> - Undocumented member.</li>
--   <li><a>gdsrsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement requirement used when this
--   <tt>DataSource</tt> was created.</li>
--   <li><a>gdsrsRoleARN</a> - Undocumented member.</li>
--   <li><a>gdsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
getDataSourceResponse :: Int -> GetDataSourceResponse

-- | Represents the output of a <tt>GetDataSource</tt> operation and
--   describes a <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>getDataSourceResponse</a> smart constructor.
data GetDataSourceResponse

-- | The current status of the <tt>DataSource</tt> . This element can have
--   one of the following values: * <tt>PENDING</tt> - Amazon ML submitted
--   a request to create a <tt>DataSource</tt> . * <tt>INPROGRESS</tt> -
--   The creation process is underway. * <tt>FAILED</tt> - The request to
--   create a <tt>DataSource</tt> did not run to completion. It is not
--   usable. * <tt>COMPLETED</tt> - The creation process completed
--   successfully. * <tt>DELETED</tt> - The <tt>DataSource</tt> is marked
--   as deleted. It is not usable.
gdsrsStatus :: Lens' GetDataSourceResponse (Maybe EntityStatus)

-- | The number of data files referenced by the <tt>DataSource</tt> .
gdsrsNumberOfFiles :: Lens' GetDataSourceResponse (Maybe Integer)

-- | The time of the most recent edit to the <tt>DataSource</tt> . The time
--   is expressed in epoch time.
gdsrsLastUpdatedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)

-- | The time that the <tt>DataSource</tt> was created. The time is
--   expressed in epoch time.
gdsrsCreatedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)

-- | The approximate CPU time in milliseconds that Amazon Machine Learning
--   spent processing the <tt>DataSource</tt> , normalized and scaled on
--   computation resources. <tt>ComputeTime</tt> is only available if the
--   <tt>DataSource</tt> is in the <tt>COMPLETED</tt> state and the
--   <tt>ComputeStatistics</tt> is set to true.
gdsrsComputeTime :: Lens' GetDataSourceResponse (Maybe Integer)

-- | The ID assigned to the <tt>DataSource</tt> at creation. This value
--   should be identical to the value of the <tt>DataSourceId</tt> in the
--   request.
gdsrsDataSourceId :: Lens' GetDataSourceResponse (Maybe Text)

-- | Undocumented member.
gdsrsRDSMetadata :: Lens' GetDataSourceResponse (Maybe RDSMetadata)

-- | The total size of observations in the data files.
gdsrsDataSizeInBytes :: Lens' GetDataSourceResponse (Maybe Integer)

-- | The schema used by all of the data files of this <tt>DataSource</tt> .
gdsrsDataSourceSchema :: Lens' GetDataSourceResponse (Maybe Text)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>DataSource</tt> as <tt>INPROGRESS</tt> . <tt>StartedAt</tt> isn't
--   available if the <tt>DataSource</tt> is in the <tt>PENDING</tt> state.
gdsrsStartedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>DataSource</tt> as <tt>COMPLETED</tt> or <tt>FAILED</tt> .
--   <tt>FinishedAt</tt> is only available when the <tt>DataSource</tt> is
--   in the <tt>COMPLETED</tt> or <tt>FAILED</tt> state.
gdsrsFinishedAt :: Lens' GetDataSourceResponse (Maybe UTCTime)

-- | The AWS user account from which the <tt>DataSource</tt> was created.
--   The account type can be either an AWS root account or an AWS Identity
--   and Access Management (IAM) user account.
gdsrsCreatedByIAMUser :: Lens' GetDataSourceResponse (Maybe Text)

-- | A user-supplied name or description of the <tt>DataSource</tt> .
gdsrsName :: Lens' GetDataSourceResponse (Maybe Text)

-- | A link to the file containing logs of <tt>CreateDataSourceFrom*</tt>
--   operations.
gdsrsLogURI :: Lens' GetDataSourceResponse (Maybe Text)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
gdsrsDataLocationS3 :: Lens' GetDataSourceResponse (Maybe Text)

-- | The parameter is <tt>true</tt> if statistics need to be generated from
--   the observation data.
gdsrsComputeStatistics :: Lens' GetDataSourceResponse (Maybe Bool)

-- | The user-supplied description of the most recent details about
--   creating the <tt>DataSource</tt> .
gdsrsMessage :: Lens' GetDataSourceResponse (Maybe Text)

-- | Undocumented member.
gdsrsRedshiftMetadata :: Lens' GetDataSourceResponse (Maybe RedshiftMetadata)

-- | A JSON string that represents the splitting and rearrangement
--   requirement used when this <tt>DataSource</tt> was created.
gdsrsDataRearrangement :: Lens' GetDataSourceResponse (Maybe Text)

-- | Undocumented member.
gdsrsRoleARN :: Lens' GetDataSourceResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
gdsrsResponseStatus :: Lens' GetDataSourceResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance Data.Data.Data Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Show.Show Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Read.Read Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Data.Data.Data Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance GHC.Show.Show Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance GHC.Read.Read Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.GetDataSource.GetDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetDataSource.GetDataSourceResponse


-- | Returns a <tt>BatchPrediction</tt> that includes detailed metadata,
--   status, and data file information for a <tt>Batch Prediction</tt>
--   request.
module Network.AWS.MachineLearning.GetBatchPrediction

-- | Creates a value of <a>GetBatchPrediction</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gbpBatchPredictionId</a> - An ID assigned to the
--   <tt>BatchPrediction</tt> at creation.</li>
--   </ul>
getBatchPrediction :: Text -> GetBatchPrediction

-- | <i>See:</i> <a>getBatchPrediction</a> smart constructor.
data GetBatchPrediction

-- | An ID assigned to the <tt>BatchPrediction</tt> at creation.
gbpBatchPredictionId :: Lens' GetBatchPrediction Text

-- | Creates a value of <a>GetBatchPredictionResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>gbprsStatus</a> - The status of the <tt>BatchPrediction</tt> ,
--   which can be one of the following values: * <tt>PENDING</tt> - Amazon
--   Machine Learning (Amazon ML) submitted a request to generate batch
--   predictions. * <tt>INPROGRESS</tt> - The batch predictions are in
--   progress. * <tt>FAILED</tt> - The request to perform a batch
--   prediction did not run to completion. It is not usable. *
--   <tt>COMPLETED</tt> - The batch prediction process completed
--   successfully. * <tt>DELETED</tt> - The <tt>BatchPrediction</tt> is
--   marked as deleted. It is not usable.</li>
--   <li><a>gbprsLastUpdatedAt</a> - The time of the most recent edit to
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>gbprsCreatedAt</a> - The time when the <tt>BatchPrediction</tt>
--   was created. The time is expressed in epoch time.</li>
--   <li><a>gbprsComputeTime</a> - The approximate CPU time in milliseconds
--   that Amazon Machine Learning spent processing the
--   <tt>BatchPrediction</tt> , normalized and scaled on computation
--   resources. <tt>ComputeTime</tt> is only available if the
--   <tt>BatchPrediction</tt> is in the <tt>COMPLETED</tt> state.</li>
--   <li><a>gbprsInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>gbprsMLModelId</a> - The ID of the <tt>MLModel</tt> that
--   generated predictions for the <tt>BatchPrediction</tt> request.</li>
--   <li><a>gbprsBatchPredictionDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> that was used to create the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>gbprsTotalRecordCount</a> - The number of total records that
--   Amazon Machine Learning saw while processing the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>gbprsStartedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>BatchPrediction</tt> as <tt>INPROGRESS</tt> .
--   <tt>StartedAt</tt> isn't available if the <tt>BatchPrediction</tt> is
--   in the <tt>PENDING</tt> state.</li>
--   <li><a>gbprsBatchPredictionId</a> - An ID assigned to the
--   <tt>BatchPrediction</tt> at creation. This value should be identical
--   to the value of the <tt>BatchPredictionID</tt> in the request.</li>
--   <li><a>gbprsFinishedAt</a> - The epoch time when Amazon Machine
--   Learning marked the <tt>BatchPrediction</tt> as <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> . <tt>FinishedAt</tt> is only available when the
--   <tt>BatchPrediction</tt> is in the <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> state.</li>
--   <li><a>gbprsInvalidRecordCount</a> - The number of invalid records
--   that Amazon Machine Learning saw while processing the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>gbprsCreatedByIAMUser</a> - The AWS user account that invoked
--   the <tt>BatchPrediction</tt> . The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>gbprsName</a> - A user-supplied name or description of the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>gbprsLogURI</a> - A link to the file that contains logs of the
--   <tt>CreateBatchPrediction</tt> operation.</li>
--   <li><a>gbprsMessage</a> - A description of the most recent details
--   about processing the batch prediction request.</li>
--   <li><a>gbprsOutputURI</a> - The location of an Amazon S3 bucket or
--   directory to receive the operation results.</li>
--   <li><a>gbprsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
getBatchPredictionResponse :: Int -> GetBatchPredictionResponse

-- | Represents the output of a <tt>GetBatchPrediction</tt> operation and
--   describes a <tt>BatchPrediction</tt> .
--   
--   <i>See:</i> <a>getBatchPredictionResponse</a> smart constructor.
data GetBatchPredictionResponse

-- | The status of the <tt>BatchPrediction</tt> , which can be one of the
--   following values: * <tt>PENDING</tt> - Amazon Machine Learning (Amazon
--   ML) submitted a request to generate batch predictions. *
--   <tt>INPROGRESS</tt> - The batch predictions are in progress. *
--   <tt>FAILED</tt> - The request to perform a batch prediction did not
--   run to completion. It is not usable. * <tt>COMPLETED</tt> - The batch
--   prediction process completed successfully. * <tt>DELETED</tt> - The
--   <tt>BatchPrediction</tt> is marked as deleted. It is not usable.
gbprsStatus :: Lens' GetBatchPredictionResponse (Maybe EntityStatus)

-- | The time of the most recent edit to <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
gbprsLastUpdatedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)

-- | The time when the <tt>BatchPrediction</tt> was created. The time is
--   expressed in epoch time.
gbprsCreatedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)

-- | The approximate CPU time in milliseconds that Amazon Machine Learning
--   spent processing the <tt>BatchPrediction</tt> , normalized and scaled
--   on computation resources. <tt>ComputeTime</tt> is only available if
--   the <tt>BatchPrediction</tt> is in the <tt>COMPLETED</tt> state.
gbprsComputeTime :: Lens' GetBatchPredictionResponse (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
gbprsInputDataLocationS3 :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that generated predictions for the
--   <tt>BatchPrediction</tt> request.
gbprsMLModelId :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that was used to create the
--   <tt>BatchPrediction</tt> .
gbprsBatchPredictionDataSourceId :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The number of total records that Amazon Machine Learning saw while
--   processing the <tt>BatchPrediction</tt> .
gbprsTotalRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>BatchPrediction</tt> as <tt>INPROGRESS</tt> . <tt>StartedAt</tt>
--   isn't available if the <tt>BatchPrediction</tt> is in the
--   <tt>PENDING</tt> state.
gbprsStartedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)

-- | An ID assigned to the <tt>BatchPrediction</tt> at creation. This value
--   should be identical to the value of the <tt>BatchPredictionID</tt> in
--   the request.
gbprsBatchPredictionId :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The epoch time when Amazon Machine Learning marked the
--   <tt>BatchPrediction</tt> as <tt>COMPLETED</tt> or <tt>FAILED</tt> .
--   <tt>FinishedAt</tt> is only available when the
--   <tt>BatchPrediction</tt> is in the <tt>COMPLETED</tt> or
--   <tt>FAILED</tt> state.
gbprsFinishedAt :: Lens' GetBatchPredictionResponse (Maybe UTCTime)

-- | The number of invalid records that Amazon Machine Learning saw while
--   processing the <tt>BatchPrediction</tt> .
gbprsInvalidRecordCount :: Lens' GetBatchPredictionResponse (Maybe Integer)

-- | The AWS user account that invoked the <tt>BatchPrediction</tt> . The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
gbprsCreatedByIAMUser :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | A user-supplied name or description of the <tt>BatchPrediction</tt> .
gbprsName :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | A link to the file that contains logs of the
--   <tt>CreateBatchPrediction</tt> operation.
gbprsLogURI :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | A description of the most recent details about processing the batch
--   prediction request.
gbprsMessage :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | The location of an Amazon S3 bucket or directory to receive the
--   operation results.
gbprsOutputURI :: Lens' GetBatchPredictionResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
gbprsResponseStatus :: Lens' GetBatchPredictionResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance Data.Data.Data Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Show.Show Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Read.Read Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Data.Data.Data Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Show.Show Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Read.Read Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance GHC.Classes.Eq Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.GetBatchPrediction.GetBatchPredictionResponse


-- | Describes one or more of the tags for your Amazon ML object.
module Network.AWS.MachineLearning.DescribeTags

-- | Creates a value of <a>DescribeTags</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dtResourceId</a> - The ID of the ML object. For example,
--   <tt>exampleModelId</tt> .</li>
--   <li><a>dtResourceType</a> - The type of the ML object.</li>
--   </ul>
describeTags :: Text -> TaggableResourceType -> DescribeTags

-- | <i>See:</i> <a>describeTags</a> smart constructor.
data DescribeTags

-- | The ID of the ML object. For example, <tt>exampleModelId</tt> .
dtResourceId :: Lens' DescribeTags Text

-- | The type of the ML object.
dtResourceType :: Lens' DescribeTags TaggableResourceType

-- | Creates a value of <a>DescribeTagsResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dtrsResourceId</a> - The ID of the tagged ML object.</li>
--   <li><a>dtrsResourceType</a> - The type of the tagged ML object.</li>
--   <li><a>dtrsTags</a> - A list of tags associated with the ML
--   object.</li>
--   <li><a>dtrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeTagsResponse :: Int -> DescribeTagsResponse

-- | Amazon ML returns the following elements.
--   
--   <i>See:</i> <a>describeTagsResponse</a> smart constructor.
data DescribeTagsResponse

-- | The ID of the tagged ML object.
dtrsResourceId :: Lens' DescribeTagsResponse (Maybe Text)

-- | The type of the tagged ML object.
dtrsResourceType :: Lens' DescribeTagsResponse (Maybe TaggableResourceType)

-- | A list of tags associated with the ML object.
dtrsTags :: Lens' DescribeTagsResponse [Tag]

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dtrsResponseStatus :: Lens' DescribeTagsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Data.Data.Data Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeTags.DescribeTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeTags.DescribeTagsResponse


-- | Returns a list of <tt>MLModel</tt> that match the search criteria in
--   the request.
--   
--   This operation returns paginated results.
module Network.AWS.MachineLearning.DescribeMLModels

-- | Creates a value of <a>DescribeMLModels</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dmlmEQ</a> - The equal to operator. The <tt>MLModel</tt>
--   results will have <tt>FilterVariable</tt> values that exactly match
--   the value specified with <tt>EQ</tt> .</li>
--   <li><a>dmlmGE</a> - The greater than or equal to operator. The
--   <tt>MLModel</tt> results will have <tt>FilterVariable</tt> values that
--   are greater than or equal to the value specified with <tt>GE</tt>
--   .</li>
--   <li><a>dmlmPrefix</a> - A string that is found at the beginning of a
--   variable, such as <tt>Name</tt> or <tt>Id</tt> . For example, an
--   <tt>MLModel</tt> could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>MLModel</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday</li>
--   <li><a>dmlmGT</a> - The greater than operator. The <tt>MLModel</tt>
--   results will have <tt>FilterVariable</tt> values that are greater than
--   the value specified with <tt>GT</tt> .</li>
--   <li><a>dmlmNE</a> - The not equal to operator. The <tt>MLModel</tt>
--   results will have <tt>FilterVariable</tt> values not equal to the
--   value specified with <tt>NE</tt> .</li>
--   <li><a>dmlmNextToken</a> - The ID of the page in the paginated
--   results.</li>
--   <li><a>dmlmSortOrder</a> - A two-value parameter that determines the
--   sequence of the resulting list of <tt>MLModel</tt> . * <tt>asc</tt> -
--   Arranges the list in ascending order (A-Z, 0-9). * <tt>dsc</tt> -
--   Arranges the list in descending order (Z-A, 9-0). Results are sorted
--   by <tt>FilterVariable</tt> .</li>
--   <li><a>dmlmLimit</a> - The number of pages of information to include
--   in the result. The range of acceptable values is <tt>1</tt> through
--   <tt>100</tt> . The default value is <tt>100</tt> .</li>
--   <li><a>dmlmLT</a> - The less than operator. The <tt>MLModel</tt>
--   results will have <tt>FilterVariable</tt> values that are less than
--   the value specified with <tt>LT</tt> .</li>
--   <li><a>dmlmFilterVariable</a> - Use one of the following variables to
--   filter a list of <tt>MLModel</tt> : * <tt>CreatedAt</tt> - Sets the
--   search criteria to <tt>MLModel</tt> creation date. * <tt>Status</tt> -
--   Sets the search criteria to <tt>MLModel</tt> status. * <tt>Name</tt> -
--   Sets the search criteria to the contents of <tt>MLModel</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked the <tt>MLModel</tt> creation. *
--   <tt>TrainingDataSourceId</tt> - Sets the search criteria to the
--   <tt>DataSource</tt> used to train one or more <tt>MLModel</tt> . *
--   <tt>RealtimeEndpointStatus</tt> - Sets the search criteria to the
--   <tt>MLModel</tt> real-time endpoint status. * <tt>MLModelType</tt> -
--   Sets the search criteria to <tt>MLModel</tt> type: binary, regression,
--   or multi-class. * <tt>Algorithm</tt> - Sets the search criteria to the
--   algorithm that the <tt>MLModel</tt> uses. * <tt>TrainingDataURI</tt> -
--   Sets the search criteria to the data file(s) used in training a
--   <tt>MLModel</tt> . The URL can identify either a file or an Amazon
--   Simple Storage Service (Amazon S3) bucket or directory.</li>
--   <li><a>dmlmLE</a> - The less than or equal to operator. The
--   <tt>MLModel</tt> results will have <tt>FilterVariable</tt> values that
--   are less than or equal to the value specified with <tt>LE</tt> .</li>
--   </ul>
describeMLModels :: DescribeMLModels

-- | <i>See:</i> <a>describeMLModels</a> smart constructor.
data DescribeMLModels

-- | The equal to operator. The <tt>MLModel</tt> results will have
--   <tt>FilterVariable</tt> values that exactly match the value specified
--   with <tt>EQ</tt> .
dmlmEQ :: Lens' DescribeMLModels (Maybe Text)

-- | The greater than or equal to operator. The <tt>MLModel</tt> results
--   will have <tt>FilterVariable</tt> values that are greater than or
--   equal to the value specified with <tt>GE</tt> .
dmlmGE :: Lens' DescribeMLModels (Maybe Text)

-- | A string that is found at the beginning of a variable, such as
--   <tt>Name</tt> or <tt>Id</tt> . For example, an <tt>MLModel</tt> could
--   have the <tt>Name</tt> <tt>2014-09-09-HolidayGiftMailer</tt> . To
--   search for this <tt>MLModel</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday
dmlmPrefix :: Lens' DescribeMLModels (Maybe Text)

-- | The greater than operator. The <tt>MLModel</tt> results will have
--   <tt>FilterVariable</tt> values that are greater than the value
--   specified with <tt>GT</tt> .
dmlmGT :: Lens' DescribeMLModels (Maybe Text)

-- | The not equal to operator. The <tt>MLModel</tt> results will have
--   <tt>FilterVariable</tt> values not equal to the value specified with
--   <tt>NE</tt> .
dmlmNE :: Lens' DescribeMLModels (Maybe Text)

-- | The ID of the page in the paginated results.
dmlmNextToken :: Lens' DescribeMLModels (Maybe Text)

-- | A two-value parameter that determines the sequence of the resulting
--   list of <tt>MLModel</tt> . * <tt>asc</tt> - Arranges the list in
--   ascending order (A-Z, 0-9). * <tt>dsc</tt> - Arranges the list in
--   descending order (Z-A, 9-0). Results are sorted by
--   <tt>FilterVariable</tt> .
dmlmSortOrder :: Lens' DescribeMLModels (Maybe SortOrder)

-- | The number of pages of information to include in the result. The range
--   of acceptable values is <tt>1</tt> through <tt>100</tt> . The default
--   value is <tt>100</tt> .
dmlmLimit :: Lens' DescribeMLModels (Maybe Natural)

-- | The less than operator. The <tt>MLModel</tt> results will have
--   <tt>FilterVariable</tt> values that are less than the value specified
--   with <tt>LT</tt> .
dmlmLT :: Lens' DescribeMLModels (Maybe Text)

-- | Use one of the following variables to filter a list of
--   <tt>MLModel</tt> : * <tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>MLModel</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>MLModel</tt> status. * <tt>Name</tt> - Sets the search
--   criteria to the contents of <tt>MLModel</tt> ____ <tt>Name</tt> . *
--   <tt>IAMUser</tt> - Sets the search criteria to the user account that
--   invoked the <tt>MLModel</tt> creation. * <tt>TrainingDataSourceId</tt>
--   - Sets the search criteria to the <tt>DataSource</tt> used to train
--   one or more <tt>MLModel</tt> . * <tt>RealtimeEndpointStatus</tt> -
--   Sets the search criteria to the <tt>MLModel</tt> real-time endpoint
--   status. * <tt>MLModelType</tt> - Sets the search criteria to
--   <tt>MLModel</tt> type: binary, regression, or multi-class. *
--   <tt>Algorithm</tt> - Sets the search criteria to the algorithm that
--   the <tt>MLModel</tt> uses. * <tt>TrainingDataURI</tt> - Sets the
--   search criteria to the data file(s) used in training a
--   <tt>MLModel</tt> . The URL can identify either a file or an Amazon
--   Simple Storage Service (Amazon S3) bucket or directory.
dmlmFilterVariable :: Lens' DescribeMLModels (Maybe MLModelFilterVariable)

-- | The less than or equal to operator. The <tt>MLModel</tt> results will
--   have <tt>FilterVariable</tt> values that are less than or equal to the
--   value specified with <tt>LE</tt> .
dmlmLE :: Lens' DescribeMLModels (Maybe Text)

-- | Creates a value of <a>DescribeMLModelsResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dmlmsrsResults</a> - A list of <tt>MLModel</tt> that meet the
--   search criteria.</li>
--   <li><a>dmlmsrsNextToken</a> - The ID of the next page in the paginated
--   results that indicates at least one more page follows.</li>
--   <li><a>dmlmsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeMLModelsResponse :: Int -> DescribeMLModelsResponse

-- | Represents the output of a <tt>DescribeMLModels</tt> operation. The
--   content is essentially a list of <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>describeMLModelsResponse</a> smart constructor.
data DescribeMLModelsResponse

-- | A list of <tt>MLModel</tt> that meet the search criteria.
dmlmsrsResults :: Lens' DescribeMLModelsResponse [MLModel]

-- | The ID of the next page in the paginated results that indicates at
--   least one more page follows.
dmlmsrsNextToken :: Lens' DescribeMLModelsResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dmlmsrsResponseStatus :: Lens' DescribeMLModelsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Data.Data.Data Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Network.AWS.Pager.AWSPager Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModels
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeMLModels.DescribeMLModelsResponse


-- | Returns a list of <tt>DescribeEvaluations</tt> that match the search
--   criteria in the request.
--   
--   This operation returns paginated results.
module Network.AWS.MachineLearning.DescribeEvaluations

-- | Creates a value of <a>DescribeEvaluations</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>deEQ</a> - The equal to operator. The <tt>Evaluation</tt>
--   results will have <tt>FilterVariable</tt> values that exactly match
--   the value specified with <tt>EQ</tt> .</li>
--   <li><a>deGE</a> - The greater than or equal to operator. The
--   <tt>Evaluation</tt> results will have <tt>FilterVariable</tt> values
--   that are greater than or equal to the value specified with <tt>GE</tt>
--   .</li>
--   <li><a>dePrefix</a> - A string that is found at the beginning of a
--   variable, such as <tt>Name</tt> or <tt>Id</tt> . For example, an
--   <tt>Evaluation</tt> could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>Evaluation</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday</li>
--   <li><a>deGT</a> - The greater than operator. The <tt>Evaluation</tt>
--   results will have <tt>FilterVariable</tt> values that are greater than
--   the value specified with <tt>GT</tt> .</li>
--   <li><a>deNE</a> - The not equal to operator. The <tt>Evaluation</tt>
--   results will have <tt>FilterVariable</tt> values not equal to the
--   value specified with <tt>NE</tt> .</li>
--   <li><a>deNextToken</a> - The ID of the page in the paginated
--   results.</li>
--   <li><a>deSortOrder</a> - A two-value parameter that determines the
--   sequence of the resulting list of <tt>Evaluation</tt> . * <tt>asc</tt>
--   - Arranges the list in ascending order (A-Z, 0-9). * <tt>dsc</tt> -
--   Arranges the list in descending order (Z-A, 9-0). Results are sorted
--   by <tt>FilterVariable</tt> .</li>
--   <li><a>deLimit</a> - The maximum number of <tt>Evaluation</tt> to
--   include in the result.</li>
--   <li><a>deLT</a> - The less than operator. The <tt>Evaluation</tt>
--   results will have <tt>FilterVariable</tt> values that are less than
--   the value specified with <tt>LT</tt> .</li>
--   <li><a>deFilterVariable</a> - Use one of the following variable to
--   filter a list of <tt>Evaluation</tt> objects: * <tt>CreatedAt</tt> -
--   Sets the search criteria to the <tt>Evaluation</tt> creation date. *
--   <tt>Status</tt> - Sets the search criteria to the <tt>Evaluation</tt>
--   status. * <tt>Name</tt> - Sets the search criteria to the contents of
--   <tt>Evaluation</tt> ____ <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the
--   search criteria to the user account that invoked an
--   <tt>Evaluation</tt> . * <tt>MLModelId</tt> - Sets the search criteria
--   to the <tt>MLModel</tt> that was evaluated. * <tt>DataSourceId</tt> -
--   Sets the search criteria to the <tt>DataSource</tt> used in
--   <tt>Evaluation</tt> . * <tt>DataUri</tt> - Sets the search criteria to
--   the data file(s) used in <tt>Evaluation</tt> . The URL can identify
--   either a file or an Amazon Simple Storage Solution (Amazon S3) bucket
--   or directory.</li>
--   <li><a>deLE</a> - The less than or equal to operator. The
--   <tt>Evaluation</tt> results will have <tt>FilterVariable</tt> values
--   that are less than or equal to the value specified with <tt>LE</tt>
--   .</li>
--   </ul>
describeEvaluations :: DescribeEvaluations

-- | <i>See:</i> <a>describeEvaluations</a> smart constructor.
data DescribeEvaluations

-- | The equal to operator. The <tt>Evaluation</tt> results will have
--   <tt>FilterVariable</tt> values that exactly match the value specified
--   with <tt>EQ</tt> .
deEQ :: Lens' DescribeEvaluations (Maybe Text)

-- | The greater than or equal to operator. The <tt>Evaluation</tt> results
--   will have <tt>FilterVariable</tt> values that are greater than or
--   equal to the value specified with <tt>GE</tt> .
deGE :: Lens' DescribeEvaluations (Maybe Text)

-- | A string that is found at the beginning of a variable, such as
--   <tt>Name</tt> or <tt>Id</tt> . For example, an <tt>Evaluation</tt>
--   could have the <tt>Name</tt> <tt>2014-09-09-HolidayGiftMailer</tt> .
--   To search for this <tt>Evaluation</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday
dePrefix :: Lens' DescribeEvaluations (Maybe Text)

-- | The greater than operator. The <tt>Evaluation</tt> results will have
--   <tt>FilterVariable</tt> values that are greater than the value
--   specified with <tt>GT</tt> .
deGT :: Lens' DescribeEvaluations (Maybe Text)

-- | The not equal to operator. The <tt>Evaluation</tt> results will have
--   <tt>FilterVariable</tt> values not equal to the value specified with
--   <tt>NE</tt> .
deNE :: Lens' DescribeEvaluations (Maybe Text)

-- | The ID of the page in the paginated results.
deNextToken :: Lens' DescribeEvaluations (Maybe Text)

-- | A two-value parameter that determines the sequence of the resulting
--   list of <tt>Evaluation</tt> . * <tt>asc</tt> - Arranges the list in
--   ascending order (A-Z, 0-9). * <tt>dsc</tt> - Arranges the list in
--   descending order (Z-A, 9-0). Results are sorted by
--   <tt>FilterVariable</tt> .
deSortOrder :: Lens' DescribeEvaluations (Maybe SortOrder)

-- | The maximum number of <tt>Evaluation</tt> to include in the result.
deLimit :: Lens' DescribeEvaluations (Maybe Natural)

-- | The less than operator. The <tt>Evaluation</tt> results will have
--   <tt>FilterVariable</tt> values that are less than the value specified
--   with <tt>LT</tt> .
deLT :: Lens' DescribeEvaluations (Maybe Text)

-- | Use one of the following variable to filter a list of
--   <tt>Evaluation</tt> objects: * <tt>CreatedAt</tt> - Sets the search
--   criteria to the <tt>Evaluation</tt> creation date. * <tt>Status</tt> -
--   Sets the search criteria to the <tt>Evaluation</tt> status. *
--   <tt>Name</tt> - Sets the search criteria to the contents of
--   <tt>Evaluation</tt> ____ <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the
--   search criteria to the user account that invoked an
--   <tt>Evaluation</tt> . * <tt>MLModelId</tt> - Sets the search criteria
--   to the <tt>MLModel</tt> that was evaluated. * <tt>DataSourceId</tt> -
--   Sets the search criteria to the <tt>DataSource</tt> used in
--   <tt>Evaluation</tt> . * <tt>DataUri</tt> - Sets the search criteria to
--   the data file(s) used in <tt>Evaluation</tt> . The URL can identify
--   either a file or an Amazon Simple Storage Solution (Amazon S3) bucket
--   or directory.
deFilterVariable :: Lens' DescribeEvaluations (Maybe EvaluationFilterVariable)

-- | The less than or equal to operator. The <tt>Evaluation</tt> results
--   will have <tt>FilterVariable</tt> values that are less than or equal
--   to the value specified with <tt>LE</tt> .
deLE :: Lens' DescribeEvaluations (Maybe Text)

-- | Creates a value of <a>DescribeEvaluationsResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>desrsResults</a> - A list of <tt>Evaluation</tt> that meet the
--   search criteria.</li>
--   <li><a>desrsNextToken</a> - The ID of the next page in the paginated
--   results that indicates at least one more page follows.</li>
--   <li><a>desrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeEvaluationsResponse :: Int -> DescribeEvaluationsResponse

-- | Represents the query results from a <tt>DescribeEvaluations</tt>
--   operation. The content is essentially a list of <tt>Evaluation</tt> .
--   
--   <i>See:</i> <a>describeEvaluationsResponse</a> smart constructor.
data DescribeEvaluationsResponse

-- | A list of <tt>Evaluation</tt> that meet the search criteria.
desrsResults :: Lens' DescribeEvaluationsResponse [Evaluation]

-- | The ID of the next page in the paginated results that indicates at
--   least one more page follows.
desrsNextToken :: Lens' DescribeEvaluationsResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
desrsResponseStatus :: Lens' DescribeEvaluationsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Data.Data.Data Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Network.AWS.Pager.AWSPager Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluations
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeEvaluations.DescribeEvaluationsResponse


-- | Returns a list of <tt>DataSource</tt> that match the search criteria
--   in the request.
--   
--   This operation returns paginated results.
module Network.AWS.MachineLearning.DescribeDataSources

-- | Creates a value of <a>DescribeDataSources</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ddsEQ</a> - The equal to operator. The <tt>DataSource</tt>
--   results will have <tt>FilterVariable</tt> values that exactly match
--   the value specified with <tt>EQ</tt> .</li>
--   <li><a>ddsGE</a> - The greater than or equal to operator. The
--   <tt>DataSource</tt> results will have <tt>FilterVariable</tt> values
--   that are greater than or equal to the value specified with <tt>GE</tt>
--   .</li>
--   <li><a>ddsPrefix</a> - A string that is found at the beginning of a
--   variable, such as <tt>Name</tt> or <tt>Id</tt> . For example, a
--   <tt>DataSource</tt> could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>DataSource</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday</li>
--   <li><a>ddsGT</a> - The greater than operator. The <tt>DataSource</tt>
--   results will have <tt>FilterVariable</tt> values that are greater than
--   the value specified with <tt>GT</tt> .</li>
--   <li><a>ddsNE</a> - The not equal to operator. The <tt>DataSource</tt>
--   results will have <tt>FilterVariable</tt> values not equal to the
--   value specified with <tt>NE</tt> .</li>
--   <li><a>ddsNextToken</a> - The ID of the page in the paginated
--   results.</li>
--   <li><a>ddsSortOrder</a> - A two-value parameter that determines the
--   sequence of the resulting list of <tt>DataSource</tt> . * <tt>asc</tt>
--   - Arranges the list in ascending order (A-Z, 0-9). * <tt>dsc</tt> -
--   Arranges the list in descending order (Z-A, 9-0). Results are sorted
--   by <tt>FilterVariable</tt> .</li>
--   <li><a>ddsLimit</a> - The maximum number of <tt>DataSource</tt> to
--   include in the result.</li>
--   <li><a>ddsLT</a> - The less than operator. The <tt>DataSource</tt>
--   results will have <tt>FilterVariable</tt> values that are less than
--   the value specified with <tt>LT</tt> .</li>
--   <li><a>ddsFilterVariable</a> - Use one of the following variables to
--   filter a list of <tt>DataSource</tt> : * <tt>CreatedAt</tt> - Sets the
--   search criteria to <tt>DataSource</tt> creation dates. *
--   <tt>Status</tt> - Sets the search criteria to <tt>DataSource</tt>
--   statuses. * <tt>Name</tt> - Sets the search criteria to the contents
--   of <tt>DataSource</tt> ____ <tt>Name</tt> . * <tt>DataUri</tt> - Sets
--   the search criteria to the URI of data files used to create the
--   <tt>DataSource</tt> . The URI can identify either a file or an Amazon
--   Simple Storage Service (Amazon S3) bucket or directory. *
--   <tt>IAMUser</tt> - Sets the search criteria to the user account that
--   invoked the <tt>DataSource</tt> creation.</li>
--   <li><a>ddsLE</a> - The less than or equal to operator. The
--   <tt>DataSource</tt> results will have <tt>FilterVariable</tt> values
--   that are less than or equal to the value specified with <tt>LE</tt>
--   .</li>
--   </ul>
describeDataSources :: DescribeDataSources

-- | <i>See:</i> <a>describeDataSources</a> smart constructor.
data DescribeDataSources

-- | The equal to operator. The <tt>DataSource</tt> results will have
--   <tt>FilterVariable</tt> values that exactly match the value specified
--   with <tt>EQ</tt> .
ddsEQ :: Lens' DescribeDataSources (Maybe Text)

-- | The greater than or equal to operator. The <tt>DataSource</tt> results
--   will have <tt>FilterVariable</tt> values that are greater than or
--   equal to the value specified with <tt>GE</tt> .
ddsGE :: Lens' DescribeDataSources (Maybe Text)

-- | A string that is found at the beginning of a variable, such as
--   <tt>Name</tt> or <tt>Id</tt> . For example, a <tt>DataSource</tt>
--   could have the <tt>Name</tt> <tt>2014-09-09-HolidayGiftMailer</tt> .
--   To search for this <tt>DataSource</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday
ddsPrefix :: Lens' DescribeDataSources (Maybe Text)

-- | The greater than operator. The <tt>DataSource</tt> results will have
--   <tt>FilterVariable</tt> values that are greater than the value
--   specified with <tt>GT</tt> .
ddsGT :: Lens' DescribeDataSources (Maybe Text)

-- | The not equal to operator. The <tt>DataSource</tt> results will have
--   <tt>FilterVariable</tt> values not equal to the value specified with
--   <tt>NE</tt> .
ddsNE :: Lens' DescribeDataSources (Maybe Text)

-- | The ID of the page in the paginated results.
ddsNextToken :: Lens' DescribeDataSources (Maybe Text)

-- | A two-value parameter that determines the sequence of the resulting
--   list of <tt>DataSource</tt> . * <tt>asc</tt> - Arranges the list in
--   ascending order (A-Z, 0-9). * <tt>dsc</tt> - Arranges the list in
--   descending order (Z-A, 9-0). Results are sorted by
--   <tt>FilterVariable</tt> .
ddsSortOrder :: Lens' DescribeDataSources (Maybe SortOrder)

-- | The maximum number of <tt>DataSource</tt> to include in the result.
ddsLimit :: Lens' DescribeDataSources (Maybe Natural)

-- | The less than operator. The <tt>DataSource</tt> results will have
--   <tt>FilterVariable</tt> values that are less than the value specified
--   with <tt>LT</tt> .
ddsLT :: Lens' DescribeDataSources (Maybe Text)

-- | Use one of the following variables to filter a list of
--   <tt>DataSource</tt> : * <tt>CreatedAt</tt> - Sets the search criteria
--   to <tt>DataSource</tt> creation dates. * <tt>Status</tt> - Sets the
--   search criteria to <tt>DataSource</tt> statuses. * <tt>Name</tt> -
--   Sets the search criteria to the contents of <tt>DataSource</tt> ____
--   <tt>Name</tt> . * <tt>DataUri</tt> - Sets the search criteria to the
--   URI of data files used to create the <tt>DataSource</tt> . The URI can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory. * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>DataSource</tt> creation.
ddsFilterVariable :: Lens' DescribeDataSources (Maybe DataSourceFilterVariable)

-- | The less than or equal to operator. The <tt>DataSource</tt> results
--   will have <tt>FilterVariable</tt> values that are less than or equal
--   to the value specified with <tt>LE</tt> .
ddsLE :: Lens' DescribeDataSources (Maybe Text)

-- | Creates a value of <a>DescribeDataSourcesResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ddssrsResults</a> - A list of <tt>DataSource</tt> that meet the
--   search criteria.</li>
--   <li><a>ddssrsNextToken</a> - An ID of the next page in the paginated
--   results that indicates at least one more page follows.</li>
--   <li><a>ddssrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeDataSourcesResponse :: Int -> DescribeDataSourcesResponse

-- | Represents the query results from a <a>DescribeDataSources</a>
--   operation. The content is essentially a list of <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>describeDataSourcesResponse</a> smart constructor.
data DescribeDataSourcesResponse

-- | A list of <tt>DataSource</tt> that meet the search criteria.
ddssrsResults :: Lens' DescribeDataSourcesResponse [DataSource]

-- | An ID of the next page in the paginated results that indicates at
--   least one more page follows.
ddssrsNextToken :: Lens' DescribeDataSourcesResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
ddssrsResponseStatus :: Lens' DescribeDataSourcesResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Data.Data.Data Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Network.AWS.Pager.AWSPager Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSources
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeDataSources.DescribeDataSourcesResponse


-- | Returns a list of <tt>BatchPrediction</tt> operations that match the
--   search criteria in the request.
--   
--   This operation returns paginated results.
module Network.AWS.MachineLearning.DescribeBatchPredictions

-- | Creates a value of <a>DescribeBatchPredictions</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dbpEQ</a> - The equal to operator. The <tt>BatchPrediction</tt>
--   results will have <tt>FilterVariable</tt> values that exactly match
--   the value specified with <tt>EQ</tt> .</li>
--   <li><a>dbpGE</a> - The greater than or equal to operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values that are greater than or equal to the value specified with
--   <tt>GE</tt> .</li>
--   <li><a>dbpPrefix</a> - A string that is found at the beginning of a
--   variable, such as <tt>Name</tt> or <tt>Id</tt> . For example, a
--   <tt>Batch Prediction</tt> operation could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>BatchPrediction</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday</li>
--   <li><a>dbpGT</a> - The greater than operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values that are greater than the value specified with <tt>GT</tt>
--   .</li>
--   <li><a>dbpNE</a> - The not equal to operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values not equal to the value specified with <tt>NE</tt> .</li>
--   <li><a>dbpNextToken</a> - An ID of the page in the paginated
--   results.</li>
--   <li><a>dbpSortOrder</a> - A two-value parameter that determines the
--   sequence of the resulting list of <tt>MLModel</tt> s. * <tt>asc</tt> -
--   Arranges the list in ascending order (A-Z, 0-9). * <tt>dsc</tt> -
--   Arranges the list in descending order (Z-A, 9-0). Results are sorted
--   by <tt>FilterVariable</tt> .</li>
--   <li><a>dbpLimit</a> - The number of pages of information to include in
--   the result. The range of acceptable values is <tt>1</tt> through
--   <tt>100</tt> . The default value is <tt>100</tt> .</li>
--   <li><a>dbpLT</a> - The less than operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values that are less than the value specified with <tt>LT</tt> .</li>
--   <li><a>dbpFilterVariable</a> - Use one of the following variables to
--   filter a list of <tt>BatchPrediction</tt> : * <tt>CreatedAt</tt> -
--   Sets the search criteria to the <tt>BatchPrediction</tt> creation
--   date. * <tt>Status</tt> - Sets the search criteria to the
--   <tt>BatchPrediction</tt> status. * <tt>Name</tt> - Sets the search
--   criteria to the contents of the <tt>BatchPrediction</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked the <tt>BatchPrediction</tt> creation. *
--   <tt>MLModelId</tt> - Sets the search criteria to the <tt>MLModel</tt>
--   used in the <tt>BatchPrediction</tt> . * <tt>DataSourceId</tt> - Sets
--   the search criteria to the <tt>DataSource</tt> used in the
--   <tt>BatchPrediction</tt> . * <tt>DataURI</tt> - Sets the search
--   criteria to the data file(s) used in the <tt>BatchPrediction</tt> .
--   The URL can identify either a file or an Amazon Simple Storage
--   Solution (Amazon S3) bucket or directory.</li>
--   <li><a>dbpLE</a> - The less than or equal to operator. The
--   <tt>BatchPrediction</tt> results will have <tt>FilterVariable</tt>
--   values that are less than or equal to the value specified with
--   <tt>LE</tt> .</li>
--   </ul>
describeBatchPredictions :: DescribeBatchPredictions

-- | <i>See:</i> <a>describeBatchPredictions</a> smart constructor.
data DescribeBatchPredictions

-- | The equal to operator. The <tt>BatchPrediction</tt> results will have
--   <tt>FilterVariable</tt> values that exactly match the value specified
--   with <tt>EQ</tt> .
dbpEQ :: Lens' DescribeBatchPredictions (Maybe Text)

-- | The greater than or equal to operator. The <tt>BatchPrediction</tt>
--   results will have <tt>FilterVariable</tt> values that are greater than
--   or equal to the value specified with <tt>GE</tt> .
dbpGE :: Lens' DescribeBatchPredictions (Maybe Text)

-- | A string that is found at the beginning of a variable, such as
--   <tt>Name</tt> or <tt>Id</tt> . For example, a <tt>Batch
--   Prediction</tt> operation could have the <tt>Name</tt>
--   <tt>2014-09-09-HolidayGiftMailer</tt> . To search for this
--   <tt>BatchPrediction</tt> , select <tt>Name</tt> for the
--   <tt>FilterVariable</tt> and any of the following strings for the
--   <tt>Prefix</tt> : * 2014-09 * 2014-09-09 * 2014-09-09-Holiday
dbpPrefix :: Lens' DescribeBatchPredictions (Maybe Text)

-- | The greater than operator. The <tt>BatchPrediction</tt> results will
--   have <tt>FilterVariable</tt> values that are greater than the value
--   specified with <tt>GT</tt> .
dbpGT :: Lens' DescribeBatchPredictions (Maybe Text)

-- | The not equal to operator. The <tt>BatchPrediction</tt> results will
--   have <tt>FilterVariable</tt> values not equal to the value specified
--   with <tt>NE</tt> .
dbpNE :: Lens' DescribeBatchPredictions (Maybe Text)

-- | An ID of the page in the paginated results.
dbpNextToken :: Lens' DescribeBatchPredictions (Maybe Text)

-- | A two-value parameter that determines the sequence of the resulting
--   list of <tt>MLModel</tt> s. * <tt>asc</tt> - Arranges the list in
--   ascending order (A-Z, 0-9). * <tt>dsc</tt> - Arranges the list in
--   descending order (Z-A, 9-0). Results are sorted by
--   <tt>FilterVariable</tt> .
dbpSortOrder :: Lens' DescribeBatchPredictions (Maybe SortOrder)

-- | The number of pages of information to include in the result. The range
--   of acceptable values is <tt>1</tt> through <tt>100</tt> . The default
--   value is <tt>100</tt> .
dbpLimit :: Lens' DescribeBatchPredictions (Maybe Natural)

-- | The less than operator. The <tt>BatchPrediction</tt> results will have
--   <tt>FilterVariable</tt> values that are less than the value specified
--   with <tt>LT</tt> .
dbpLT :: Lens' DescribeBatchPredictions (Maybe Text)

-- | Use one of the following variables to filter a list of
--   <tt>BatchPrediction</tt> : * <tt>CreatedAt</tt> - Sets the search
--   criteria to the <tt>BatchPrediction</tt> creation date. *
--   <tt>Status</tt> - Sets the search criteria to the
--   <tt>BatchPrediction</tt> status. * <tt>Name</tt> - Sets the search
--   criteria to the contents of the <tt>BatchPrediction</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked the <tt>BatchPrediction</tt> creation. *
--   <tt>MLModelId</tt> - Sets the search criteria to the <tt>MLModel</tt>
--   used in the <tt>BatchPrediction</tt> . * <tt>DataSourceId</tt> - Sets
--   the search criteria to the <tt>DataSource</tt> used in the
--   <tt>BatchPrediction</tt> . * <tt>DataURI</tt> - Sets the search
--   criteria to the data file(s) used in the <tt>BatchPrediction</tt> .
--   The URL can identify either a file or an Amazon Simple Storage
--   Solution (Amazon S3) bucket or directory.
dbpFilterVariable :: Lens' DescribeBatchPredictions (Maybe BatchPredictionFilterVariable)

-- | The less than or equal to operator. The <tt>BatchPrediction</tt>
--   results will have <tt>FilterVariable</tt> values that are less than or
--   equal to the value specified with <tt>LE</tt> .
dbpLE :: Lens' DescribeBatchPredictions (Maybe Text)

-- | Creates a value of <a>DescribeBatchPredictionsResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dbpsrsResults</a> - A list of <tt>BatchPrediction</tt> objects
--   that meet the search criteria.</li>
--   <li><a>dbpsrsNextToken</a> - The ID of the next page in the paginated
--   results that indicates at least one more page follows.</li>
--   <li><a>dbpsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
describeBatchPredictionsResponse :: Int -> DescribeBatchPredictionsResponse

-- | Represents the output of a <tt>DescribeBatchPredictions</tt>
--   operation. The content is essentially a list of
--   <tt>BatchPrediction</tt> s.
--   
--   <i>See:</i> <a>describeBatchPredictionsResponse</a> smart constructor.
data DescribeBatchPredictionsResponse

-- | A list of <tt>BatchPrediction</tt> objects that meet the search
--   criteria.
dbpsrsResults :: Lens' DescribeBatchPredictionsResponse [BatchPrediction]

-- | The ID of the next page in the paginated results that indicates at
--   least one more page follows.
dbpsrsNextToken :: Lens' DescribeBatchPredictionsResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dbpsrsResponseStatus :: Lens' DescribeBatchPredictionsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Data.Data.Data Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Show.Show Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Read.Read Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance GHC.Classes.Eq Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Network.AWS.Pager.AWSPager Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictions
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DescribeBatchPredictions.DescribeBatchPredictionsResponse


module Network.AWS.MachineLearning.Waiters

-- | Polls <a>DescribeMLModels</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
mLModelAvailable :: Wait DescribeMLModels

-- | Polls <a>DescribeBatchPredictions</a> every 30 seconds until a
--   successful state is reached. An error is returned after 60 failed
--   checks.
batchPredictionAvailable :: Wait DescribeBatchPredictions

-- | Polls <a>DescribeDataSources</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
dataSourceAvailable :: Wait DescribeDataSources

-- | Polls <a>DescribeEvaluations</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
evaluationAvailable :: Wait DescribeEvaluations


-- | Deletes the specified tags associated with an ML object. After this
--   operation is complete, you can't recover deleted tags.
--   
--   If you specify a tag that doesn't exist, Amazon ML ignores it.
module Network.AWS.MachineLearning.DeleteTags

-- | Creates a value of <a>DeleteTags</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dTagKeys</a> - One or more tags to delete.</li>
--   <li><a>dResourceId</a> - The ID of the tagged ML object. For example,
--   <tt>exampleModelId</tt> .</li>
--   <li><a>dResourceType</a> - The type of the tagged ML object.</li>
--   </ul>
deleteTags :: Text -> TaggableResourceType -> DeleteTags

-- | <i>See:</i> <a>deleteTags</a> smart constructor.
data DeleteTags

-- | One or more tags to delete.
dTagKeys :: Lens' DeleteTags [Text]

-- | The ID of the tagged ML object. For example, <tt>exampleModelId</tt> .
dResourceId :: Lens' DeleteTags Text

-- | The type of the tagged ML object.
dResourceType :: Lens' DeleteTags TaggableResourceType

-- | Creates a value of <a>DeleteTagsResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>drsResourceId</a> - The ID of the ML object from which tags
--   were deleted.</li>
--   <li><a>drsResourceType</a> - The type of the ML object from which tags
--   were deleted.</li>
--   <li><a>drsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteTagsResponse :: Int -> DeleteTagsResponse

-- | Amazon ML returns the following elements.
--   
--   <i>See:</i> <a>deleteTagsResponse</a> smart constructor.
data DeleteTagsResponse

-- | The ID of the ML object from which tags were deleted.
drsResourceId :: Lens' DeleteTagsResponse (Maybe Text)

-- | The type of the ML object from which tags were deleted.
drsResourceType :: Lens' DeleteTagsResponse (Maybe TaggableResourceType)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
drsResponseStatus :: Lens' DeleteTagsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Data.Data.Data Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteTags.DeleteTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteTags.DeleteTagsResponse


-- | Deletes a real time endpoint of an <tt>MLModel</tt> .
module Network.AWS.MachineLearning.DeleteRealtimeEndpoint

-- | Creates a value of <a>DeleteRealtimeEndpoint</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dreMLModelId</a> - The ID assigned to the <tt>MLModel</tt>
--   during creation.</li>
--   </ul>
deleteRealtimeEndpoint :: Text -> DeleteRealtimeEndpoint

-- | <i>See:</i> <a>deleteRealtimeEndpoint</a> smart constructor.
data DeleteRealtimeEndpoint

-- | The ID assigned to the <tt>MLModel</tt> during creation.
dreMLModelId :: Lens' DeleteRealtimeEndpoint Text

-- | Creates a value of <a>DeleteRealtimeEndpointResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>drersRealtimeEndpointInfo</a> - The endpoint information of the
--   <tt>MLModel</tt></li>
--   <li><a>drersMLModelId</a> - A user-supplied ID that uniquely
--   identifies the <tt>MLModel</tt> . This value should be identical to
--   the value of the <tt>MLModelId</tt> in the request.</li>
--   <li><a>drersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteRealtimeEndpointResponse :: Int -> DeleteRealtimeEndpointResponse

-- | Represents the output of an <tt>DeleteRealtimeEndpoint</tt> operation.
--   
--   The result contains the <tt>MLModelId</tt> and the endpoint
--   information for the <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>deleteRealtimeEndpointResponse</a> smart constructor.
data DeleteRealtimeEndpointResponse

-- | The endpoint information of the <tt>MLModel</tt>
drersRealtimeEndpointInfo :: Lens' DeleteRealtimeEndpointResponse (Maybe RealtimeEndpointInfo)

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
--   This value should be identical to the value of the <tt>MLModelId</tt>
--   in the request.
drersMLModelId :: Lens' DeleteRealtimeEndpointResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
drersResponseStatus :: Lens' DeleteRealtimeEndpointResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Data.Data.Data Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpoint
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteRealtimeEndpoint.DeleteRealtimeEndpointResponse


-- | Assigns the <tt>DELETED</tt> status to an <tt>MLModel</tt> , rendering
--   it unusable.
--   
--   After using the <tt>DeleteMLModel</tt> operation, you can use the
--   <tt>GetMLModel</tt> operation to verify that the status of the
--   <tt>MLModel</tt> changed to DELETED.
--   
--   <b>Caution:</b> The result of the <tt>DeleteMLModel</tt> operation is
--   irreversible.
module Network.AWS.MachineLearning.DeleteMLModel

-- | Creates a value of <a>DeleteMLModel</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dmlmMLModelId</a> - A user-supplied ID that uniquely identifies
--   the <tt>MLModel</tt> .</li>
--   </ul>
deleteMLModel :: Text -> DeleteMLModel

-- | <i>See:</i> <a>deleteMLModel</a> smart constructor.
data DeleteMLModel

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
dmlmMLModelId :: Lens' DeleteMLModel Text

-- | Creates a value of <a>DeleteMLModelResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dmlmrsMLModelId</a> - A user-supplied ID that uniquely
--   identifies the <tt>MLModel</tt> . This value should be identical to
--   the value of the <tt>MLModelID</tt> in the request.</li>
--   <li><a>dmlmrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteMLModelResponse :: Int -> DeleteMLModelResponse

-- | Represents the output of a <tt>DeleteMLModel</tt> operation.
--   
--   You can use the <tt>GetMLModel</tt> operation and check the value of
--   the <tt>Status</tt> parameter to see whether an <tt>MLModel</tt> is
--   marked as <tt>DELETED</tt> .
--   
--   <i>See:</i> <a>deleteMLModelResponse</a> smart constructor.
data DeleteMLModelResponse

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
--   This value should be identical to the value of the <tt>MLModelID</tt>
--   in the request.
dmlmrsMLModelId :: Lens' DeleteMLModelResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dmlmrsResponseStatus :: Lens' DeleteMLModelResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Data.Data.Data Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteMLModel.DeleteMLModelResponse


-- | Assigns the <tt>DELETED</tt> status to an <tt>Evaluation</tt> ,
--   rendering it unusable.
--   
--   After invoking the <tt>DeleteEvaluation</tt> operation, you can use
--   the <tt>GetEvaluation</tt> operation to verify that the status of the
--   <tt>Evaluation</tt> changed to <tt>DELETED</tt> .
--   
--   _<b>_Caution</b> The results of the <tt>DeleteEvaluation</tt>
--   operation are irreversible.
--   
--   __
module Network.AWS.MachineLearning.DeleteEvaluation

-- | Creates a value of <a>DeleteEvaluation</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>deEvaluationId</a> - A user-supplied ID that uniquely
--   identifies the <tt>Evaluation</tt> to delete.</li>
--   </ul>
deleteEvaluation :: Text -> DeleteEvaluation

-- | <i>See:</i> <a>deleteEvaluation</a> smart constructor.
data DeleteEvaluation

-- | A user-supplied ID that uniquely identifies the <tt>Evaluation</tt> to
--   delete.
deEvaluationId :: Lens' DeleteEvaluation Text

-- | Creates a value of <a>DeleteEvaluationResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dersEvaluationId</a> - A user-supplied ID that uniquely
--   identifies the <tt>Evaluation</tt> . This value should be identical to
--   the value of the <tt>EvaluationId</tt> in the request.</li>
--   <li><a>dersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteEvaluationResponse :: Int -> DeleteEvaluationResponse

-- | Represents the output of a <tt>DeleteEvaluation</tt> operation. The
--   output indicates that Amazon Machine Learning (Amazon ML) received the
--   request.
--   
--   You can use the <tt>GetEvaluation</tt> operation and check the value
--   of the <tt>Status</tt> parameter to see whether an <tt>Evaluation</tt>
--   is marked as <tt>DELETED</tt> .
--   
--   <i>See:</i> <a>deleteEvaluationResponse</a> smart constructor.
data DeleteEvaluationResponse

-- | A user-supplied ID that uniquely identifies the <tt>Evaluation</tt> .
--   This value should be identical to the value of the
--   <tt>EvaluationId</tt> in the request.
dersEvaluationId :: Lens' DeleteEvaluationResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dersResponseStatus :: Lens' DeleteEvaluationResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Data.Data.Data Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteEvaluation.DeleteEvaluationResponse


-- | Assigns the DELETED status to a <tt>DataSource</tt> , rendering it
--   unusable.
--   
--   After using the <tt>DeleteDataSource</tt> operation, you can use the
--   <tt>GetDataSource</tt> operation to verify that the status of the
--   <tt>DataSource</tt> changed to DELETED.
--   
--   <b>Caution:</b> The results of the <tt>DeleteDataSource</tt> operation
--   are irreversible.
module Network.AWS.MachineLearning.DeleteDataSource

-- | Creates a value of <a>DeleteDataSource</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ddsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> .</li>
--   </ul>
deleteDataSource :: Text -> DeleteDataSource

-- | <i>See:</i> <a>deleteDataSource</a> smart constructor.
data DeleteDataSource

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
ddsDataSourceId :: Lens' DeleteDataSource Text

-- | Creates a value of <a>DeleteDataSourceResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ddsrsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> . This value should be identical to
--   the value of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>ddsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteDataSourceResponse :: Int -> DeleteDataSourceResponse

-- | Represents the output of a <tt>DeleteDataSource</tt> operation.
--   
--   <i>See:</i> <a>deleteDataSourceResponse</a> smart constructor.
data DeleteDataSourceResponse

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
--   This value should be identical to the value of the
--   <tt>DataSourceID</tt> in the request.
ddsrsDataSourceId :: Lens' DeleteDataSourceResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
ddsrsResponseStatus :: Lens' DeleteDataSourceResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Data.Data.Data Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSource
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteDataSource.DeleteDataSourceResponse


-- | Assigns the DELETED status to a <tt>BatchPrediction</tt> , rendering
--   it unusable.
--   
--   After using the <tt>DeleteBatchPrediction</tt> operation, you can use
--   the <tt>GetBatchPrediction</tt> operation to verify that the status of
--   the <tt>BatchPrediction</tt> changed to DELETED.
--   
--   <b>Caution:</b> The result of the <tt>DeleteBatchPrediction</tt>
--   operation is irreversible.
module Network.AWS.MachineLearning.DeleteBatchPrediction

-- | Creates a value of <a>DeleteBatchPrediction</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dbpBatchPredictionId</a> - A user-supplied ID that uniquely
--   identifies the <tt>BatchPrediction</tt> .</li>
--   </ul>
deleteBatchPrediction :: Text -> DeleteBatchPrediction

-- | <i>See:</i> <a>deleteBatchPrediction</a> smart constructor.
data DeleteBatchPrediction

-- | A user-supplied ID that uniquely identifies the
--   <tt>BatchPrediction</tt> .
dbpBatchPredictionId :: Lens' DeleteBatchPrediction Text

-- | Creates a value of <a>DeleteBatchPredictionResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dbprsBatchPredictionId</a> - A user-supplied ID that uniquely
--   identifies the <tt>BatchPrediction</tt> . This value should be
--   identical to the value of the <tt>BatchPredictionID</tt> in the
--   request.</li>
--   <li><a>dbprsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
deleteBatchPredictionResponse :: Int -> DeleteBatchPredictionResponse

-- | Represents the output of a <tt>DeleteBatchPrediction</tt> operation.
--   
--   You can use the <tt>GetBatchPrediction</tt> operation and check the
--   value of the <tt>Status</tt> parameter to see whether a
--   <tt>BatchPrediction</tt> is marked as <tt>DELETED</tt> .
--   
--   <i>See:</i> <a>deleteBatchPredictionResponse</a> smart constructor.
data DeleteBatchPredictionResponse

-- | A user-supplied ID that uniquely identifies the
--   <tt>BatchPrediction</tt> . This value should be identical to the value
--   of the <tt>BatchPredictionID</tt> in the request.
dbprsBatchPredictionId :: Lens' DeleteBatchPredictionResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
dbprsResponseStatus :: Lens' DeleteBatchPredictionResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance Data.Data.Data Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Data.Data.Data Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Show.Show Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Read.Read Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance GHC.Classes.Eq Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.DeleteBatchPrediction.DeleteBatchPredictionResponse


-- | Creates a real-time endpoint for the <tt>MLModel</tt> . The endpoint
--   contains the URI of the <tt>MLModel</tt> ; that is, the location to
--   send real-time prediction requests for the specified <tt>MLModel</tt>
--   .
module Network.AWS.MachineLearning.CreateRealtimeEndpoint

-- | Creates a value of <a>CreateRealtimeEndpoint</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>creMLModelId</a> - The ID assigned to the <tt>MLModel</tt>
--   during creation.</li>
--   </ul>
createRealtimeEndpoint :: Text -> CreateRealtimeEndpoint

-- | <i>See:</i> <a>createRealtimeEndpoint</a> smart constructor.
data CreateRealtimeEndpoint

-- | The ID assigned to the <tt>MLModel</tt> during creation.
creMLModelId :: Lens' CreateRealtimeEndpoint Text

-- | Creates a value of <a>CreateRealtimeEndpointResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>crersRealtimeEndpointInfo</a> - The endpoint information of the
--   <tt>MLModel</tt></li>
--   <li><a>crersMLModelId</a> - A user-supplied ID that uniquely
--   identifies the <tt>MLModel</tt> . This value should be identical to
--   the value of the <tt>MLModelId</tt> in the request.</li>
--   <li><a>crersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createRealtimeEndpointResponse :: Int -> CreateRealtimeEndpointResponse

-- | Represents the output of an <tt>CreateRealtimeEndpoint</tt> operation.
--   
--   The result contains the <tt>MLModelId</tt> and the endpoint
--   information for the <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>createRealtimeEndpointResponse</a> smart constructor.
data CreateRealtimeEndpointResponse

-- | The endpoint information of the <tt>MLModel</tt>
crersRealtimeEndpointInfo :: Lens' CreateRealtimeEndpointResponse (Maybe RealtimeEndpointInfo)

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
--   This value should be identical to the value of the <tt>MLModelId</tt>
--   in the request.
crersMLModelId :: Lens' CreateRealtimeEndpointResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
crersResponseStatus :: Lens' CreateRealtimeEndpointResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Data.Data.Data Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Show.Show Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Read.Read Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpoint
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateRealtimeEndpoint.CreateRealtimeEndpointResponse


-- | Creates a new <tt>MLModel</tt> using the <tt>DataSource</tt> and the
--   recipe as information sources.
--   
--   An <tt>MLModel</tt> is nearly immutable. Users can update only the
--   <tt>MLModelName</tt> and the <tt>ScoreThreshold</tt> in an
--   <tt>MLModel</tt> without creating a new <tt>MLModel</tt> .
--   
--   <tt>CreateMLModel</tt> is an asynchronous operation. In response to
--   <tt>CreateMLModel</tt> , Amazon Machine Learning (Amazon ML)
--   immediately returns and sets the <tt>MLModel</tt> status to
--   <tt>PENDING</tt> . After the <tt>MLModel</tt> has been created and
--   ready is for use, Amazon ML sets the status to <tt>COMPLETED</tt> .
--   
--   You can use the <tt>GetMLModel</tt> operation to check the progress of
--   the <tt>MLModel</tt> during the creation operation.
--   
--   <tt>CreateMLModel</tt> requires a <tt>DataSource</tt> with computed
--   statistics, which can be created by setting <tt>ComputeStatistics</tt>
--   to <tt>true</tt> in <tt>CreateDataSourceFromRDS</tt> ,
--   <tt>CreateDataSourceFromS3</tt> , or
--   <tt>CreateDataSourceFromRedshift</tt> operations.
module Network.AWS.MachineLearning.CreateMLModel

-- | Creates a value of <a>CreateMLModel</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cmlmRecipe</a> - The data recipe for creating the
--   <tt>MLModel</tt> . You must specify either the recipe or its URI. If
--   you don't specify a recipe or its URI, Amazon ML creates a
--   default.</li>
--   <li><a>cmlmRecipeURI</a> - The Amazon Simple Storage Service (Amazon
--   S3) location and file name that contains the <tt>MLModel</tt> recipe.
--   You must specify either the recipe or its URI. If you don't specify a
--   recipe or its URI, Amazon ML creates a default.</li>
--   <li><a>cmlmMLModelName</a> - A user-supplied name or description of
--   the <tt>MLModel</tt> .</li>
--   <li><a>cmlmParameters</a> - A list of the training parameters in the
--   <tt>MLModel</tt> . The list is implemented as a map of key-value
--   pairs. The following is the current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   We strongly recommend that you shuffle your data. *
--   <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to zero, resulting in a
--   sparse feature set. If you use this parameter, start by specifying a
--   small value, such as <tt>1.0E-08</tt> . The value is a double that
--   ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not
--   use L1 normalization. This parameter can't be used when <tt>L2</tt> is
--   specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.</li>
--   <li><a>cmlmMLModelId</a> - A user-supplied ID that uniquely identifies
--   the <tt>MLModel</tt> .</li>
--   <li><a>cmlmMLModelType</a> - The category of supervised learning that
--   this <tt>MLModel</tt> will address. Choose from the following types: *
--   Choose <tt>REGRESSION</tt> if the <tt>MLModel</tt> will be used to
--   predict a numeric value. * Choose <tt>BINARY</tt> if the
--   <tt>MLModel</tt> result has two possible values. * Choose
--   <tt>MULTICLASS</tt> if the <tt>MLModel</tt> result has a limited
--   number of values. For more information, see the <a>Amazon Machine
--   Learning Developer Guide</a> .</li>
--   <li><a>cmlmTrainingDataSourceId</a> - The <tt>DataSource</tt> that
--   points to the training data.</li>
--   </ul>
createMLModel :: Text -> MLModelType -> Text -> CreateMLModel

-- | <i>See:</i> <a>createMLModel</a> smart constructor.
data CreateMLModel

-- | The data recipe for creating the <tt>MLModel</tt> . You must specify
--   either the recipe or its URI. If you don't specify a recipe or its
--   URI, Amazon ML creates a default.
cmlmRecipe :: Lens' CreateMLModel (Maybe Text)

-- | The Amazon Simple Storage Service (Amazon S3) location and file name
--   that contains the <tt>MLModel</tt> recipe. You must specify either the
--   recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
--   creates a default.
cmlmRecipeURI :: Lens' CreateMLModel (Maybe Text)

-- | A user-supplied name or description of the <tt>MLModel</tt> .
cmlmMLModelName :: Lens' CreateMLModel (Maybe Text)

-- | A list of the training parameters in the <tt>MLModel</tt> . The list
--   is implemented as a map of key-value pairs. The following is the
--   current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   We strongly recommend that you shuffle your data. *
--   <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to zero, resulting in a
--   sparse feature set. If you use this parameter, start by specifying a
--   small value, such as <tt>1.0E-08</tt> . The value is a double that
--   ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not
--   use L1 normalization. This parameter can't be used when <tt>L2</tt> is
--   specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm. It controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.
cmlmParameters :: Lens' CreateMLModel (HashMap Text Text)

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
cmlmMLModelId :: Lens' CreateMLModel Text

-- | The category of supervised learning that this <tt>MLModel</tt> will
--   address. Choose from the following types: * Choose <tt>REGRESSION</tt>
--   if the <tt>MLModel</tt> will be used to predict a numeric value. *
--   Choose <tt>BINARY</tt> if the <tt>MLModel</tt> result has two possible
--   values. * Choose <tt>MULTICLASS</tt> if the <tt>MLModel</tt> result
--   has a limited number of values. For more information, see the
--   <a>Amazon Machine Learning Developer Guide</a> .
cmlmMLModelType :: Lens' CreateMLModel MLModelType

-- | The <tt>DataSource</tt> that points to the training data.
cmlmTrainingDataSourceId :: Lens' CreateMLModel Text

-- | Creates a value of <a>CreateMLModelResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cmlmrsMLModelId</a> - A user-supplied ID that uniquely
--   identifies the <tt>MLModel</tt> . This value should be identical to
--   the value of the <tt>MLModelId</tt> in the request.</li>
--   <li><a>cmlmrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createMLModelResponse :: Int -> CreateMLModelResponse

-- | Represents the output of a <tt>CreateMLModel</tt> operation, and is an
--   acknowledgement that Amazon ML received the request.
--   
--   The <tt>CreateMLModel</tt> operation is asynchronous. You can poll for
--   status updates by using the <tt>GetMLModel</tt> operation and checking
--   the <tt>Status</tt> parameter.
--   
--   <i>See:</i> <a>createMLModelResponse</a> smart constructor.
data CreateMLModelResponse

-- | A user-supplied ID that uniquely identifies the <tt>MLModel</tt> .
--   This value should be identical to the value of the <tt>MLModelId</tt>
--   in the request.
cmlmrsMLModelId :: Lens' CreateMLModelResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cmlmrsResponseStatus :: Lens' CreateMLModelResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Data.Data.Data Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Show.Show Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Read.Read Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateMLModel.CreateMLModel
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateMLModel.CreateMLModelResponse


-- | Creates a new <tt>Evaluation</tt> of an <tt>MLModel</tt> . An
--   <tt>MLModel</tt> is evaluated on a set of observations associated to a
--   <tt>DataSource</tt> . Like a <tt>DataSource</tt> for an
--   <tt>MLModel</tt> , the <tt>DataSource</tt> for an <tt>Evaluation</tt>
--   contains values for the <tt>Target Variable</tt> . The
--   <tt>Evaluation</tt> compares the predicted result for each observation
--   to the actual outcome and provides a summary so that you know how
--   effective the <tt>MLModel</tt> functions on the test data. Evaluation
--   generates a relevant performance metric, such as BinaryAUC,
--   RegressionRMSE or MulticlassAvgFScore based on the corresponding
--   <tt>MLModelType</tt> : <tt>BINARY</tt> , <tt>REGRESSION</tt> or
--   <tt>MULTICLASS</tt> .
--   
--   <tt>CreateEvaluation</tt> is an asynchronous operation. In response to
--   <tt>CreateEvaluation</tt> , Amazon Machine Learning (Amazon ML)
--   immediately returns and sets the evaluation status to <tt>PENDING</tt>
--   . After the <tt>Evaluation</tt> is created and ready for use, Amazon
--   ML sets the status to <tt>COMPLETED</tt> .
--   
--   You can use the <tt>GetEvaluation</tt> operation to check progress of
--   the evaluation during the creation operation.
module Network.AWS.MachineLearning.CreateEvaluation

-- | Creates a value of <a>CreateEvaluation</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>ceEvaluationName</a> - A user-supplied name or description of
--   the <tt>Evaluation</tt> .</li>
--   <li><a>ceEvaluationId</a> - A user-supplied ID that uniquely
--   identifies the <tt>Evaluation</tt> .</li>
--   <li><a>ceMLModelId</a> - The ID of the <tt>MLModel</tt> to evaluate.
--   The schema used in creating the <tt>MLModel</tt> must match the schema
--   of the <tt>DataSource</tt> used in the <tt>Evaluation</tt> .</li>
--   <li><a>ceEvaluationDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> for the evaluation. The schema of the
--   <tt>DataSource</tt> must match the schema used to create the
--   <tt>MLModel</tt> .</li>
--   </ul>
createEvaluation :: Text -> Text -> Text -> CreateEvaluation

-- | <i>See:</i> <a>createEvaluation</a> smart constructor.
data CreateEvaluation

-- | A user-supplied name or description of the <tt>Evaluation</tt> .
ceEvaluationName :: Lens' CreateEvaluation (Maybe Text)

-- | A user-supplied ID that uniquely identifies the <tt>Evaluation</tt> .
ceEvaluationId :: Lens' CreateEvaluation Text

-- | The ID of the <tt>MLModel</tt> to evaluate. The schema used in
--   creating the <tt>MLModel</tt> must match the schema of the
--   <tt>DataSource</tt> used in the <tt>Evaluation</tt> .
ceMLModelId :: Lens' CreateEvaluation Text

-- | The ID of the <tt>DataSource</tt> for the evaluation. The schema of
--   the <tt>DataSource</tt> must match the schema used to create the
--   <tt>MLModel</tt> .
ceEvaluationDataSourceId :: Lens' CreateEvaluation Text

-- | Creates a value of <a>CreateEvaluationResponse</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cersEvaluationId</a> - The user-supplied ID that uniquely
--   identifies the <tt>Evaluation</tt> . This value should be identical to
--   the value of the <tt>EvaluationId</tt> in the request.</li>
--   <li><a>cersResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createEvaluationResponse :: Int -> CreateEvaluationResponse

-- | Represents the output of a <tt>CreateEvaluation</tt> operation, and is
--   an acknowledgement that Amazon ML received the request.
--   
--   <tt>CreateEvaluation</tt> operation is asynchronous. You can poll for
--   status updates by using the <tt>GetEvcaluation</tt> operation and
--   checking the <tt>Status</tt> parameter.
--   
--   <i>See:</i> <a>createEvaluationResponse</a> smart constructor.
data CreateEvaluationResponse

-- | The user-supplied ID that uniquely identifies the <tt>Evaluation</tt>
--   . This value should be identical to the value of the
--   <tt>EvaluationId</tt> in the request.
cersEvaluationId :: Lens' CreateEvaluationResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cersResponseStatus :: Lens' CreateEvaluationResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Data.Data.Data Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Show.Show Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Read.Read Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluation
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateEvaluation.CreateEvaluationResponse


-- | Creates a <tt>DataSource</tt> object. A <tt>DataSource</tt> references
--   data that can be used to perform <tt>CreateMLModel</tt> ,
--   <tt>CreateEvaluation</tt> , or <tt>CreateBatchPrediction</tt>
--   operations.
--   
--   <tt>CreateDataSourceFromS3</tt> is an asynchronous operation. In
--   response to <tt>CreateDataSourceFromS3</tt> , Amazon Machine Learning
--   (Amazon ML) immediately returns and sets the <tt>DataSource</tt>
--   status to <tt>PENDING</tt> . After the <tt>DataSource</tt> has been
--   created and is ready for use, Amazon ML sets the <tt>Status</tt>
--   parameter to <tt>COMPLETED</tt> . <tt>DataSource</tt> in the
--   <tt>COMPLETED</tt> or <tt>PENDING</tt> state can be used to perform
--   only <tt>CreateMLModel</tt> , <tt>CreateEvaluation</tt> or
--   <tt>CreateBatchPrediction</tt> operations.
--   
--   If Amazon ML can't accept the input source, it sets the
--   <tt>Status</tt> parameter to <tt>FAILED</tt> and includes an error
--   message in the <tt>Message</tt> attribute of the
--   <tt>GetDataSource</tt> operation response.
--   
--   The observation data used in a <tt>DataSource</tt> should be ready to
--   use; that is, it should have a consistent structure, and missing data
--   values should be kept to a minimum. The observation data must reside
--   in one or more .csv files in an Amazon Simple Storage Service (Amazon
--   S3) location, along with a schema that describes the data items by
--   name and type. The same schema must be used for all of the data files
--   referenced by the <tt>DataSource</tt> .
--   
--   After the <tt>DataSource</tt> has been created, it's ready to use in
--   evaluations and batch predictions. If you plan to use the
--   <tt>DataSource</tt> to train an <tt>MLModel</tt> , the
--   <tt>DataSource</tt> also needs a recipe. A recipe describes how each
--   input variable will be used in training an <tt>MLModel</tt> . Will the
--   variable be included or excluded from training? Will the variable be
--   manipulated; for example, will it be combined with another variable or
--   will it be split apart into word combinations? The recipe provides
--   answers to these questions.
module Network.AWS.MachineLearning.CreateDataSourceFromS3

-- | Creates a value of <a>CreateDataSourceFromS3</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfsDataSourceName</a> - A user-supplied name or description
--   of the <tt>DataSource</tt> .</li>
--   <li><a>cdsfsComputeStatistics</a> - The compute statistics for a
--   <tt>DataSource</tt> . The statistics are generated from the
--   observation data referenced by a <tt>DataSource</tt> . Amazon ML uses
--   the statistics internally during <tt>MLModel</tt> training. This
--   parameter must be set to <tt>true</tt> if the DataSourceneeds to be
--   used for <tt>MLModel</tt> training.</li>
--   <li><a>cdsfsDataSourceId</a> - A user-supplied identifier that
--   uniquely identifies the <tt>DataSource</tt> .</li>
--   <li><a>cdsfsDataSpec</a> - The data specification of a
--   <tt>DataSource</tt> : * DataLocationS3 - The Amazon S3 location of the
--   observation data. * DataSchemaLocationS3 - The Amazon S3 location of
--   the <tt>DataSchema</tt> . * DataSchema - A JSON string representing
--   the schema. This is not required if <tt>DataSchemaUri</tt> is
--   specified. * DataRearrangement - A JSON string that represents the
--   splitting and rearrangement requirements for the <tt>Datasource</tt> .
--   Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt></li>
--   </ul>
createDataSourceFromS3 :: Text -> S3DataSpec -> CreateDataSourceFromS3

-- | <i>See:</i> <a>createDataSourceFromS3</a> smart constructor.
data CreateDataSourceFromS3

-- | A user-supplied name or description of the <tt>DataSource</tt> .
cdsfsDataSourceName :: Lens' CreateDataSourceFromS3 (Maybe Text)

-- | The compute statistics for a <tt>DataSource</tt> . The statistics are
--   generated from the observation data referenced by a
--   <tt>DataSource</tt> . Amazon ML uses the statistics internally during
--   <tt>MLModel</tt> training. This parameter must be set to <tt>true</tt>
--   if the DataSourceneeds to be used for <tt>MLModel</tt> training.
cdsfsComputeStatistics :: Lens' CreateDataSourceFromS3 (Maybe Bool)

-- | A user-supplied identifier that uniquely identifies the
--   <tt>DataSource</tt> .
cdsfsDataSourceId :: Lens' CreateDataSourceFromS3 Text

-- | The data specification of a <tt>DataSource</tt> : * DataLocationS3 -
--   The Amazon S3 location of the observation data. * DataSchemaLocationS3
--   - The Amazon S3 location of the <tt>DataSchema</tt> . * DataSchema - A
--   JSON string representing the schema. This is not required if
--   <tt>DataSchemaUri</tt> is specified. * DataRearrangement - A JSON
--   string that represents the splitting and rearrangement requirements
--   for the <tt>Datasource</tt> . Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt>
cdsfsDataSpec :: Lens' CreateDataSourceFromS3 S3DataSpec

-- | Creates a value of <a>CreateDataSourceFromS3Response</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfsrsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> . This value should be identical to
--   the value of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>cdsfsrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createDataSourceFromS3Response :: Int -> CreateDataSourceFromS3Response

-- | Represents the output of a <tt>CreateDataSourceFromS3</tt> operation,
--   and is an acknowledgement that Amazon ML received the request.
--   
--   The <tt>CreateDataSourceFromS3</tt> operation is asynchronous. You can
--   poll for updates by using the <tt>GetBatchPrediction</tt> operation
--   and checking the <tt>Status</tt> parameter.
--   
--   <i>See:</i> <a>createDataSourceFromS3Response</a> smart constructor.
data CreateDataSourceFromS3Response

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
--   This value should be identical to the value of the
--   <tt>DataSourceID</tt> in the request.
cdsfsrsDataSourceId :: Lens' CreateDataSourceFromS3Response (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cdsfsrsResponseStatus :: Lens' CreateDataSourceFromS3Response Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromS3.CreateDataSourceFromS3Response


-- | Creates a <tt>DataSource</tt> from a database hosted on an Amazon
--   Redshift cluster. A <tt>DataSource</tt> references data that can be
--   used to perform either <tt>CreateMLModel</tt> ,
--   <tt>CreateEvaluation</tt> , or <tt>CreateBatchPrediction</tt>
--   operations.
--   
--   <tt>CreateDataSourceFromRedshift</tt> is an asynchronous operation. In
--   response to <tt>CreateDataSourceFromRedshift</tt> , Amazon Machine
--   Learning (Amazon ML) immediately returns and sets the
--   <tt>DataSource</tt> status to <tt>PENDING</tt> . After the
--   <tt>DataSource</tt> is created and ready for use, Amazon ML sets the
--   <tt>Status</tt> parameter to <tt>COMPLETED</tt> . <tt>DataSource</tt>
--   in <tt>COMPLETED</tt> or <tt>PENDING</tt> states can be used to
--   perform only <tt>CreateMLModel</tt> , <tt>CreateEvaluation</tt> , or
--   <tt>CreateBatchPrediction</tt> operations.
--   
--   If Amazon ML can't accept the input source, it sets the
--   <tt>Status</tt> parameter to <tt>FAILED</tt> and includes an error
--   message in the <tt>Message</tt> attribute of the
--   <tt>GetDataSource</tt> operation response.
--   
--   The observations should be contained in the database hosted on an
--   Amazon Redshift cluster and should be specified by a
--   <tt>SelectSqlQuery</tt> query. Amazon ML executes an <tt>Unload</tt>
--   command in Amazon Redshift to transfer the result set of the
--   <tt>SelectSqlQuery</tt> query to <tt>S3StagingLocation</tt> .
--   
--   After the <tt>DataSource</tt> has been created, it's ready for use in
--   evaluations and batch predictions. If you plan to use the
--   <tt>DataSource</tt> to train an <tt>MLModel</tt> , the
--   <tt>DataSource</tt> also requires a recipe. A recipe describes how
--   each input variable will be used in training an <tt>MLModel</tt> .
--   Will the variable be included or excluded from training? Will the
--   variable be manipulated; for example, will it be combined with another
--   variable or will it be split apart into word combinations? The recipe
--   provides answers to these questions.
--   
--   You can't change an existing datasource, but you can copy and modify
--   the settings from an existing Amazon Redshift datasource to create a
--   new datasource. To do so, call <tt>GetDataSource</tt> for an existing
--   datasource and copy the values to a <tt>CreateDataSource</tt> call.
--   Change the settings that you want to change and make sure that all
--   required fields have the appropriate values.
module Network.AWS.MachineLearning.CreateDataSourceFromRedshift

-- | Creates a value of <a>CreateDataSourceFromRedshift</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfrDataSourceName</a> - A user-supplied name or description
--   of the <tt>DataSource</tt> .</li>
--   <li><a>cdsfrComputeStatistics</a> - The compute statistics for a
--   <tt>DataSource</tt> . The statistics are generated from the
--   observation data referenced by a <tt>DataSource</tt> . Amazon ML uses
--   the statistics internally during <tt>MLModel</tt> training. This
--   parameter must be set to <tt>true</tt> if the <tt>DataSource</tt>
--   needs to be used for <tt>MLModel</tt> training.</li>
--   <li><a>cdsfrDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> .</li>
--   <li><a>cdsfrDataSpec</a> - The data specification of an Amazon
--   Redshift <tt>DataSource</tt> : * DatabaseInformation - *
--   <tt>DatabaseName</tt> - The name of the Amazon Redshift database. *
--   <tt>ClusterIdentifier</tt> - The unique ID for the Amazon Redshift
--   cluster. * DatabaseCredentials - The AWS Identity and Access
--   Management (IAM) credentials that are used to connect to the Amazon
--   Redshift database. * SelectSqlQuery - The query that is used to
--   retrieve the observation data for the <tt>Datasource</tt> . *
--   S3StagingLocation - The Amazon Simple Storage Service (Amazon S3)
--   location for staging Amazon Redshift data. The data retrieved from
--   Amazon Redshift using the <tt>SelectSqlQuery</tt> query is stored in
--   this location. * DataSchemaUri - The Amazon S3 location of the
--   <tt>DataSchema</tt> . * DataSchema - A JSON string representing the
--   schema. This is not required if <tt>DataSchemaUri</tt> is specified. *
--   DataRearrangement - A JSON string that represents the splitting and
--   rearrangement requirements for the <tt>DataSource</tt> . Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt></li>
--   <li><a>cdsfrRoleARN</a> - A fully specified role Amazon Resource Name
--   (ARN). Amazon ML assumes the role on behalf of the user to create the
--   following: * A security group to allow Amazon ML to execute the
--   <tt>SelectSqlQuery</tt> query on an Amazon Redshift cluster * An
--   Amazon S3 bucket policy to grant Amazon ML read/write permissions on
--   the <tt>S3StagingLocation</tt></li>
--   </ul>
createDataSourceFromRedshift :: Text -> RedshiftDataSpec -> Text -> CreateDataSourceFromRedshift

-- | <i>See:</i> <a>createDataSourceFromRedshift</a> smart constructor.
data CreateDataSourceFromRedshift

-- | A user-supplied name or description of the <tt>DataSource</tt> .
cdsfrDataSourceName :: Lens' CreateDataSourceFromRedshift (Maybe Text)

-- | The compute statistics for a <tt>DataSource</tt> . The statistics are
--   generated from the observation data referenced by a
--   <tt>DataSource</tt> . Amazon ML uses the statistics internally during
--   <tt>MLModel</tt> training. This parameter must be set to <tt>true</tt>
--   if the <tt>DataSource</tt> needs to be used for <tt>MLModel</tt>
--   training.
cdsfrComputeStatistics :: Lens' CreateDataSourceFromRedshift (Maybe Bool)

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
cdsfrDataSourceId :: Lens' CreateDataSourceFromRedshift Text

-- | The data specification of an Amazon Redshift <tt>DataSource</tt> : *
--   DatabaseInformation - * <tt>DatabaseName</tt> - The name of the Amazon
--   Redshift database. * <tt>ClusterIdentifier</tt> - The unique ID for
--   the Amazon Redshift cluster. * DatabaseCredentials - The AWS Identity
--   and Access Management (IAM) credentials that are used to connect to
--   the Amazon Redshift database. * SelectSqlQuery - The query that is
--   used to retrieve the observation data for the <tt>Datasource</tt> . *
--   S3StagingLocation - The Amazon Simple Storage Service (Amazon S3)
--   location for staging Amazon Redshift data. The data retrieved from
--   Amazon Redshift using the <tt>SelectSqlQuery</tt> query is stored in
--   this location. * DataSchemaUri - The Amazon S3 location of the
--   <tt>DataSchema</tt> . * DataSchema - A JSON string representing the
--   schema. This is not required if <tt>DataSchemaUri</tt> is specified. *
--   DataRearrangement - A JSON string that represents the splitting and
--   rearrangement requirements for the <tt>DataSource</tt> . Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt>
cdsfrDataSpec :: Lens' CreateDataSourceFromRedshift RedshiftDataSpec

-- | A fully specified role Amazon Resource Name (ARN). Amazon ML assumes
--   the role on behalf of the user to create the following: * A security
--   group to allow Amazon ML to execute the <tt>SelectSqlQuery</tt> query
--   on an Amazon Redshift cluster * An Amazon S3 bucket policy to grant
--   Amazon ML read/write permissions on the <tt>S3StagingLocation</tt>
cdsfrRoleARN :: Lens' CreateDataSourceFromRedshift Text

-- | Creates a value of <a>CreateDataSourceFromRedshiftResponse</a> with
--   the minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfrrsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the datasource. This value should be identical to the value
--   of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>cdsfrrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createDataSourceFromRedshiftResponse :: Int -> CreateDataSourceFromRedshiftResponse

-- | Represents the output of a <tt>CreateDataSourceFromRedshift</tt>
--   operation, and is an acknowledgement that Amazon ML received the
--   request.
--   
--   The <tt>CreateDataSourceFromRedshift</tt> operation is asynchronous.
--   You can poll for updates by using the <tt>GetBatchPrediction</tt>
--   operation and checking the <tt>Status</tt> parameter.
--   
--   <i>See:</i> <a>createDataSourceFromRedshiftResponse</a> smart
--   constructor.
data CreateDataSourceFromRedshiftResponse

-- | A user-supplied ID that uniquely identifies the datasource. This value
--   should be identical to the value of the <tt>DataSourceID</tt> in the
--   request.
cdsfrrsDataSourceId :: Lens' CreateDataSourceFromRedshiftResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cdsfrrsResponseStatus :: Lens' CreateDataSourceFromRedshiftResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshift
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromRedshift.CreateDataSourceFromRedshiftResponse


-- | Creates a <tt>DataSource</tt> object from an <a>Amazon Relational
--   Database Service</a> (Amazon RDS). A <tt>DataSource</tt> references
--   data that can be used to perform <tt>CreateMLModel</tt> ,
--   <tt>CreateEvaluation</tt> , or <tt>CreateBatchPrediction</tt>
--   operations.
--   
--   <tt>CreateDataSourceFromRDS</tt> is an asynchronous operation. In
--   response to <tt>CreateDataSourceFromRDS</tt> , Amazon Machine Learning
--   (Amazon ML) immediately returns and sets the <tt>DataSource</tt>
--   status to <tt>PENDING</tt> . After the <tt>DataSource</tt> is created
--   and ready for use, Amazon ML sets the <tt>Status</tt> parameter to
--   <tt>COMPLETED</tt> . <tt>DataSource</tt> in the <tt>COMPLETED</tt> or
--   <tt>PENDING</tt> state can be used only to perform
--   <tt>&gt;CreateMLModel</tt> &gt;, <tt>CreateEvaluation</tt> , or
--   <tt>CreateBatchPrediction</tt> operations.
--   
--   If Amazon ML cannot accept the input source, it sets the
--   <tt>Status</tt> parameter to <tt>FAILED</tt> and includes an error
--   message in the <tt>Message</tt> attribute of the
--   <tt>GetDataSource</tt> operation response.
module Network.AWS.MachineLearning.CreateDataSourceFromRDS

-- | Creates a value of <a>CreateDataSourceFromRDS</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfrdsDataSourceName</a> - A user-supplied name or description
--   of the <tt>DataSource</tt> .</li>
--   <li><a>cdsfrdsComputeStatistics</a> - The compute statistics for a
--   <tt>DataSource</tt> . The statistics are generated from the
--   observation data referenced by a <tt>DataSource</tt> . Amazon ML uses
--   the statistics internally during <tt>MLModel</tt> training. This
--   parameter must be set to <tt>true</tt> if the DataSourceneeds to be
--   used for <tt>MLModel</tt> training.</li>
--   <li><a>cdsfrdsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the <tt>DataSource</tt> . Typically, an Amazon Resource
--   Number (ARN) becomes the ID for a <tt>DataSource</tt> .</li>
--   <li><a>cdsfrdsRDSData</a> - The data specification of an Amazon RDS
--   <tt>DataSource</tt> : * DatabaseInformation - * <tt>DatabaseName</tt>
--   - The name of the Amazon RDS database. * <tt>InstanceIdentifier </tt>
--   - A unique identifier for the Amazon RDS database instance. *
--   DatabaseCredentials - AWS Identity and Access Management (IAM)
--   credentials that are used to connect to the Amazon RDS database. *
--   ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an
--   EC2 instance to carry out the copy task from Amazon RDS to Amazon
--   Simple Storage Service (Amazon S3). For more information, see <a>Role
--   templates</a> for data pipelines. * ServiceRole - A role
--   (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to
--   monitor the progress of the copy task from Amazon RDS to Amazon S3.
--   For more information, see <a>Role templates</a> for data pipelines. *
--   SecurityInfo - The security information to use to access an RDS DB
--   instance. You need to set up appropriate ingress rules for the
--   security entity IDs provided to allow access to the Amazon RDS
--   instance. Specify a [<tt>SubnetId</tt> , <tt>SecurityGroupIds</tt> ]
--   pair for a VPC-based RDS DB instance. * SelectSqlQuery - A query that
--   is used to retrieve the observation data for the <tt>Datasource</tt> .
--   * S3StagingLocation - The Amazon S3 location for staging Amazon RDS
--   data. The data retrieved from Amazon RDS using <tt>SelectSqlQuery</tt>
--   is stored in this location. * DataSchemaUri - The Amazon S3 location
--   of the <tt>DataSchema</tt> . * DataSchema - A JSON string representing
--   the schema. This is not required if <tt>DataSchemaUri</tt> is
--   specified. * DataRearrangement - A JSON string that represents the
--   splitting and rearrangement requirements for the <tt>Datasource</tt> .
--   Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt></li>
--   <li><a>cdsfrdsRoleARN</a> - The role that Amazon ML assumes on behalf
--   of the user to create and activate a data pipeline in the user's
--   account and copy data using the <tt>SelectSqlQuery</tt> query from
--   Amazon RDS to Amazon S3.</li>
--   </ul>
createDataSourceFromRDS :: Text -> RDSDataSpec -> Text -> CreateDataSourceFromRDS

-- | <i>See:</i> <a>createDataSourceFromRDS</a> smart constructor.
data CreateDataSourceFromRDS

-- | A user-supplied name or description of the <tt>DataSource</tt> .
cdsfrdsDataSourceName :: Lens' CreateDataSourceFromRDS (Maybe Text)

-- | The compute statistics for a <tt>DataSource</tt> . The statistics are
--   generated from the observation data referenced by a
--   <tt>DataSource</tt> . Amazon ML uses the statistics internally during
--   <tt>MLModel</tt> training. This parameter must be set to <tt>true</tt>
--   if the DataSourceneeds to be used for <tt>MLModel</tt> training.
cdsfrdsComputeStatistics :: Lens' CreateDataSourceFromRDS (Maybe Bool)

-- | A user-supplied ID that uniquely identifies the <tt>DataSource</tt> .
--   Typically, an Amazon Resource Number (ARN) becomes the ID for a
--   <tt>DataSource</tt> .
cdsfrdsDataSourceId :: Lens' CreateDataSourceFromRDS Text

-- | The data specification of an Amazon RDS <tt>DataSource</tt> : *
--   DatabaseInformation - * <tt>DatabaseName</tt> - The name of the Amazon
--   RDS database. * <tt>InstanceIdentifier </tt> - A unique identifier for
--   the Amazon RDS database instance. * DatabaseCredentials - AWS Identity
--   and Access Management (IAM) credentials that are used to connect to
--   the Amazon RDS database. * ResourceRole - A role
--   (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry
--   out the copy task from Amazon RDS to Amazon Simple Storage Service
--   (Amazon S3). For more information, see <a>Role templates</a> for data
--   pipelines. * ServiceRole - A role (DataPipelineDefaultRole) assumed by
--   the AWS Data Pipeline service to monitor the progress of the copy task
--   from Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines. * SecurityInfo - The security
--   information to use to access an RDS DB instance. You need to set up
--   appropriate ingress rules for the security entity IDs provided to
--   allow access to the Amazon RDS instance. Specify a [<tt>SubnetId</tt>
--   , <tt>SecurityGroupIds</tt> ] pair for a VPC-based RDS DB instance. *
--   SelectSqlQuery - A query that is used to retrieve the observation data
--   for the <tt>Datasource</tt> . * S3StagingLocation - The Amazon S3
--   location for staging Amazon RDS data. The data retrieved from Amazon
--   RDS using <tt>SelectSqlQuery</tt> is stored in this location. *
--   DataSchemaUri - The Amazon S3 location of the <tt>DataSchema</tt> . *
--   DataSchema - A JSON string representing the schema. This is not
--   required if <tt>DataSchemaUri</tt> is specified. * DataRearrangement -
--   A JSON string that represents the splitting and rearrangement
--   requirements for the <tt>Datasource</tt> . Sample -
--   <tt>"{"splitting":{"percentBegin":10,"percentEnd":60}}"</tt>
cdsfrdsRDSData :: Lens' CreateDataSourceFromRDS RDSDataSpec

-- | The role that Amazon ML assumes on behalf of the user to create and
--   activate a data pipeline in the user's account and copy data using the
--   <tt>SelectSqlQuery</tt> query from Amazon RDS to Amazon S3.
cdsfrdsRoleARN :: Lens' CreateDataSourceFromRDS Text

-- | Creates a value of <a>CreateDataSourceFromRDSResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cdsfrdsrsDataSourceId</a> - A user-supplied ID that uniquely
--   identifies the datasource. This value should be identical to the value
--   of the <tt>DataSourceID</tt> in the request.</li>
--   <li><a>cdsfrdsrsResponseStatus</a> - -- | The response status
--   code.</li>
--   </ul>
createDataSourceFromRDSResponse :: Int -> CreateDataSourceFromRDSResponse

-- | Represents the output of a <tt>CreateDataSourceFromRDS</tt> operation,
--   and is an acknowledgement that Amazon ML received the request.
--   
--   The <tt>CreateDataSourceFromRDS</tt> &gt; operation is asynchronous.
--   You can poll for updates by using the <tt>GetBatchPrediction</tt>
--   operation and checking the <tt>Status</tt> parameter. You can inspect
--   the <tt>Message</tt> when <tt>Status</tt> shows up as <tt>FAILED</tt>
--   . You can also check the progress of the copy operation by going to
--   the <tt>DataPipeline</tt> console and looking up the pipeline using
--   the <tt>pipelineId </tt> from the describe call.
--   
--   <i>See:</i> <a>createDataSourceFromRDSResponse</a> smart constructor.
data CreateDataSourceFromRDSResponse

-- | A user-supplied ID that uniquely identifies the datasource. This value
--   should be identical to the value of the <tt>DataSourceID</tt> in the
--   request.
cdsfrdsrsDataSourceId :: Lens' CreateDataSourceFromRDSResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cdsfrdsrsResponseStatus :: Lens' CreateDataSourceFromRDSResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Data.Data.Data Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Show.Show Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Read.Read Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDS
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateDataSourceFromRDS.CreateDataSourceFromRDSResponse


-- | Generates predictions for a group of observations. The observations to
--   process exist in one or more data files referenced by a
--   <tt>DataSource</tt> . This operation creates a new
--   <tt>BatchPrediction</tt> , and uses an <tt>MLModel</tt> and the data
--   files referenced by the <tt>DataSource</tt> as information sources.
--   
--   <tt>CreateBatchPrediction</tt> is an asynchronous operation. In
--   response to <tt>CreateBatchPrediction</tt> , Amazon Machine Learning
--   (Amazon ML) immediately returns and sets the <tt>BatchPrediction</tt>
--   status to <tt>PENDING</tt> . After the <tt>BatchPrediction</tt>
--   completes, Amazon ML sets the status to <tt>COMPLETED</tt> .
--   
--   You can poll for status updates by using the
--   <tt>GetBatchPrediction</tt> operation and checking the <tt>Status</tt>
--   parameter of the result. After the <tt>COMPLETED</tt> status appears,
--   the results are available in the location specified by the
--   <tt>OutputUri</tt> parameter.
module Network.AWS.MachineLearning.CreateBatchPrediction

-- | Creates a value of <a>CreateBatchPrediction</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cbpBatchPredictionName</a> - A user-supplied name or
--   description of the <tt>BatchPrediction</tt> .
--   <tt>BatchPredictionName</tt> can only use the UTF-8 character
--   set.</li>
--   <li><a>cbpBatchPredictionId</a> - A user-supplied ID that uniquely
--   identifies the <tt>BatchPrediction</tt> .</li>
--   <li><a>cbpMLModelId</a> - The ID of the <tt>MLModel</tt> that will
--   generate predictions for the group of observations.</li>
--   <li><a>cbpBatchPredictionDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> that points to the group of observations to
--   predict.</li>
--   <li><a>cbpOutputURI</a> - The location of an Amazon Simple Storage
--   Service (Amazon S3) bucket or directory to store the batch prediction
--   results. The following substrings are not allowed in the <tt>s3
--   key</tt> portion of the <tt>outputURI</tt> field: <tt>:</tt>,
--   <tt>//</tt>, <tt>/./</tt>, <tt>/../</tt>. Amazon ML needs permissions
--   to store and retrieve the logs on your behalf. For information about
--   how to set permissions, see the <a>Amazon Machine Learning Developer
--   Guide</a> .</li>
--   </ul>
createBatchPrediction :: Text -> Text -> Text -> Text -> CreateBatchPrediction

-- | <i>See:</i> <a>createBatchPrediction</a> smart constructor.
data CreateBatchPrediction

-- | A user-supplied name or description of the <tt>BatchPrediction</tt> .
--   <tt>BatchPredictionName</tt> can only use the UTF-8 character set.
cbpBatchPredictionName :: Lens' CreateBatchPrediction (Maybe Text)

-- | A user-supplied ID that uniquely identifies the
--   <tt>BatchPrediction</tt> .
cbpBatchPredictionId :: Lens' CreateBatchPrediction Text

-- | The ID of the <tt>MLModel</tt> that will generate predictions for the
--   group of observations.
cbpMLModelId :: Lens' CreateBatchPrediction Text

-- | The ID of the <tt>DataSource</tt> that points to the group of
--   observations to predict.
cbpBatchPredictionDataSourceId :: Lens' CreateBatchPrediction Text

-- | The location of an Amazon Simple Storage Service (Amazon S3) bucket or
--   directory to store the batch prediction results. The following
--   substrings are not allowed in the <tt>s3 key</tt> portion of the
--   <tt>outputURI</tt> field: <tt>:</tt>, <tt>//</tt>, <tt>/./</tt>,
--   <tt>/../</tt>. Amazon ML needs permissions to store and retrieve the
--   logs on your behalf. For information about how to set permissions, see
--   the <a>Amazon Machine Learning Developer Guide</a> .
cbpOutputURI :: Lens' CreateBatchPrediction Text

-- | Creates a value of <a>CreateBatchPredictionResponse</a> with the
--   minimum fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>cbprsBatchPredictionId</a> - A user-supplied ID that uniquely
--   identifies the <tt>BatchPrediction</tt> . This value is identical to
--   the value of the <tt>BatchPredictionId</tt> in the request.</li>
--   <li><a>cbprsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
createBatchPredictionResponse :: Int -> CreateBatchPredictionResponse

-- | Represents the output of a <tt>CreateBatchPrediction</tt> operation,
--   and is an acknowledgement that Amazon ML received the request.
--   
--   The <tt>CreateBatchPrediction</tt> operation is asynchronous. You can
--   poll for status updates by using the <tt>&gt;GetBatchPrediction</tt>
--   operation and checking the <tt>Status</tt> parameter of the result.
--   
--   <i>See:</i> <a>createBatchPredictionResponse</a> smart constructor.
data CreateBatchPredictionResponse

-- | A user-supplied ID that uniquely identifies the
--   <tt>BatchPrediction</tt> . This value is identical to the value of the
--   <tt>BatchPredictionId</tt> in the request.
cbprsBatchPredictionId :: Lens' CreateBatchPredictionResponse (Maybe Text)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
cbprsResponseStatus :: Lens' CreateBatchPredictionResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance Data.Data.Data Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Show.Show Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Read.Read Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Data.Data.Data Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Show.Show Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Read.Read Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance GHC.Classes.Eq Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPrediction
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.CreateBatchPrediction.CreateBatchPredictionResponse


-- | Adds one or more tags to an object, up to a limit of 10. Each tag
--   consists of a key and an optional value. If you add a tag using a key
--   that is already associated with the ML object, <tt>AddTags</tt>
--   updates the tag's value.
module Network.AWS.MachineLearning.AddTags

-- | Creates a value of <a>AddTags</a> with the minimum fields required to
--   make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>atTags</a> - The key-value pairs to use to create tags. If you
--   specify a key without specifying a value, Amazon ML creates a tag with
--   the specified key and a value of null.</li>
--   <li><a>atResourceId</a> - The ID of the ML object to tag. For example,
--   <tt>exampleModelId</tt> .</li>
--   <li><a>atResourceType</a> - The type of the ML object to tag.</li>
--   </ul>
addTags :: Text -> TaggableResourceType -> AddTags

-- | <i>See:</i> <a>addTags</a> smart constructor.
data AddTags

-- | The key-value pairs to use to create tags. If you specify a key
--   without specifying a value, Amazon ML creates a tag with the specified
--   key and a value of null.
atTags :: Lens' AddTags [Tag]

-- | The ID of the ML object to tag. For example, <tt>exampleModelId</tt> .
atResourceId :: Lens' AddTags Text

-- | The type of the ML object to tag.
atResourceType :: Lens' AddTags TaggableResourceType

-- | Creates a value of <a>AddTagsResponse</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>atrsResourceId</a> - The ID of the ML object that was
--   tagged.</li>
--   <li><a>atrsResourceType</a> - The type of the ML object that was
--   tagged.</li>
--   <li><a>atrsResponseStatus</a> - -- | The response status code.</li>
--   </ul>
addTagsResponse :: Int -> AddTagsResponse

-- | Amazon ML returns the following elements.
--   
--   <i>See:</i> <a>addTagsResponse</a> smart constructor.
data AddTagsResponse

-- | The ID of the ML object that was tagged.
atrsResourceId :: Lens' AddTagsResponse (Maybe Text)

-- | The type of the ML object that was tagged.
atrsResourceType :: Lens' AddTagsResponse (Maybe TaggableResourceType)

-- | <ul>
--   <li>- | The response status code.</li>
--   </ul>
atrsResponseStatus :: Lens' AddTagsResponse Int
instance GHC.Generics.Generic Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance Data.Data.Data Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance GHC.Show.Show Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance GHC.Read.Read Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance GHC.Classes.Eq Network.AWS.MachineLearning.AddTags.AddTagsResponse
instance GHC.Generics.Generic Network.AWS.MachineLearning.AddTags.AddTags
instance Data.Data.Data Network.AWS.MachineLearning.AddTags.AddTags
instance GHC.Show.Show Network.AWS.MachineLearning.AddTags.AddTags
instance GHC.Read.Read Network.AWS.MachineLearning.AddTags.AddTags
instance GHC.Classes.Eq Network.AWS.MachineLearning.AddTags.AddTags
instance Network.AWS.Types.AWSRequest Network.AWS.MachineLearning.AddTags.AddTags
instance Data.Hashable.Class.Hashable Network.AWS.MachineLearning.AddTags.AddTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.AddTags.AddTags
instance Network.AWS.Data.Headers.ToHeaders Network.AWS.MachineLearning.AddTags.AddTags
instance Data.Aeson.Types.ToJSON.ToJSON Network.AWS.MachineLearning.AddTags.AddTags
instance Network.AWS.Data.Path.ToPath Network.AWS.MachineLearning.AddTags.AddTags
instance Network.AWS.Data.Query.ToQuery Network.AWS.MachineLearning.AddTags.AddTags
instance Control.DeepSeq.NFData Network.AWS.MachineLearning.AddTags.AddTagsResponse


-- | Definition of the public APIs exposed by Amazon Machine Learning
module Network.AWS.MachineLearning

-- | API version <tt>2014-12-12</tt> of the Amazon Machine Learning SDK
--   configuration.
machineLearning :: Service

-- | Prism for InvalidTagException' errors.
_InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError

-- | An error on the server occurred when trying to process a request.
_InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError

-- | An error on the client occurred. Typically, the cause is an invalid
--   input value.
_InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError

-- | A second request to use or change an object was not allowed. This can
--   result from retrying a request using a parameter that was not present
--   in the original request.
_IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError

-- | Prism for TagLimitExceededException' errors.
_TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The exception is thrown when a predict request is made to an unmounted
--   <tt>MLModel</tt> .
_PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError

-- | A specified resource cannot be located.
_ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError

-- | The subscriber exceeded the maximum number of operations. This
--   exception can occur when listing objects such as <tt>DataSource</tt> .
_LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError

-- | Polls <a>DescribeMLModels</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
mLModelAvailable :: Wait DescribeMLModels

-- | Polls <a>DescribeBatchPredictions</a> every 30 seconds until a
--   successful state is reached. An error is returned after 60 failed
--   checks.
batchPredictionAvailable :: Wait DescribeBatchPredictions

-- | Polls <a>DescribeDataSources</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
dataSourceAvailable :: Wait DescribeDataSources

-- | Polls <a>DescribeEvaluations</a> every 30 seconds until a successful
--   state is reached. An error is returned after 60 failed checks.
evaluationAvailable :: Wait DescribeEvaluations

-- | The function used to train an <tt>MLModel</tt> . Training choices
--   supported by Amazon ML include the following:
--   
--   <ul>
--   <li><tt>SGD</tt> - Stochastic Gradient Descent. *
--   <tt>RandomForest</tt> - Random forest of decision trees.</li>
--   </ul>
data Algorithm
SGD :: Algorithm

-- | A list of the variables to use in searching or filtering
--   <tt>BatchPrediction</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>BatchPrediction</tt> creation date. * <tt>Status</tt> - Sets the
--   search criteria to <tt>BatchPrediction</tt> status. * <tt>Name</tt> -
--   Sets the search criteria to the contents of <tt>BatchPrediction</tt>
--   ____ <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>BatchPrediction</tt> creation. *
--   <tt>MLModelId</tt> - Sets the search criteria to the <tt>MLModel</tt>
--   used in the <tt>BatchPrediction</tt> . * <tt>DataSourceId</tt> - Sets
--   the search criteria to the <tt>DataSource</tt> used in the
--   <tt>BatchPrediction</tt> . * <tt>DataURI</tt> - Sets the search
--   criteria to the data file(s) used in the <tt>BatchPrediction</tt> .
--   The URL can identify either a file or an Amazon Simple Storage Service
--   (Amazon S3) bucket or directory.</li>
--   </ul>
data BatchPredictionFilterVariable
BatchCreatedAt :: BatchPredictionFilterVariable
BatchDataSourceId :: BatchPredictionFilterVariable
BatchDataURI :: BatchPredictionFilterVariable
BatchIAMUser :: BatchPredictionFilterVariable
BatchLastUpdatedAt :: BatchPredictionFilterVariable
BatchMLModelId :: BatchPredictionFilterVariable
BatchName :: BatchPredictionFilterVariable
BatchStatus :: BatchPredictionFilterVariable

-- | A list of the variables to use in searching or filtering
--   <tt>DataSource</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>DataSource</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>DataSource</tt> status. * <tt>Name</tt> - Sets the
--   search criteria to the contents of <tt>DataSource</tt> ____
--   <tt>Name</tt> . * <tt>DataUri</tt> - Sets the search criteria to the
--   URI of data files used to create the <tt>DataSource</tt> . The URI can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory. * <tt>IAMUser</tt> - Sets the search criteria to
--   the user account that invoked the <tt>DataSource</tt> creation.</li>
--   </ul>
data DataSourceFilterVariable
DataCreatedAt :: DataSourceFilterVariable
DataDATALOCATIONS3 :: DataSourceFilterVariable
DataIAMUser :: DataSourceFilterVariable
DataLastUpdatedAt :: DataSourceFilterVariable
DataName :: DataSourceFilterVariable
DataStatus :: DataSourceFilterVariable

-- | Contains the key values of <tt>DetailsMap</tt> :
--   <tt>PredictiveModelType</tt> - Indicates the type of the
--   <tt>MLModel</tt> . <tt>Algorithm</tt> - Indicates the algorithm that
--   was used for the <tt>MLModel</tt> .
data DetailsAttributes
Algorithm :: DetailsAttributes
PredictiveModelType :: DetailsAttributes

-- | Object status with the following possible values:
--   
--   <ul>
--   <li><tt>PENDING</tt> * <tt>INPROGRESS</tt> * <tt>FAILED</tt> *
--   <tt>COMPLETED</tt> * <tt>DELETED</tt></li>
--   </ul>
data EntityStatus
ESCompleted :: EntityStatus
ESDeleted :: EntityStatus
ESFailed :: EntityStatus
ESInprogress :: EntityStatus
ESPending :: EntityStatus

-- | A list of the variables to use in searching or filtering
--   <tt>Evaluation</tt> .
--   
--   <ul>
--   <li><tt>CreatedAt</tt> - Sets the search criteria to
--   <tt>Evaluation</tt> creation date. * <tt>Status</tt> - Sets the search
--   criteria to <tt>Evaluation</tt> status. * <tt>Name</tt> - Sets the
--   search criteria to the contents of <tt>Evaluation</tt> ____
--   <tt>Name</tt> . * <tt>IAMUser</tt> - Sets the search criteria to the
--   user account that invoked an evaluation. * <tt>MLModelId</tt> - Sets
--   the search criteria to the <tt>Predictor</tt> that was evaluated. *
--   <tt>DataSourceId</tt> - Sets the search criteria to the
--   <tt>DataSource</tt> used in evaluation. * <tt>DataUri</tt> - Sets the
--   search criteria to the data file(s) used in evaluation. The URL can
--   identify either a file or an Amazon Simple Storage Service (Amazon S3)
--   bucket or directory.</li>
--   </ul>
data EvaluationFilterVariable
EvalCreatedAt :: EvaluationFilterVariable
EvalDataSourceId :: EvaluationFilterVariable
EvalDataURI :: EvaluationFilterVariable
EvalIAMUser :: EvaluationFilterVariable
EvalLastUpdatedAt :: EvaluationFilterVariable
EvalMLModelId :: EvaluationFilterVariable
EvalName :: EvaluationFilterVariable
EvalStatus :: EvaluationFilterVariable
data MLModelFilterVariable
MLMFVAlgorithm :: MLModelFilterVariable
MLMFVCreatedAt :: MLModelFilterVariable
MLMFVIAMUser :: MLModelFilterVariable
MLMFVLastUpdatedAt :: MLModelFilterVariable
MLMFVMLModelType :: MLModelFilterVariable
MLMFVName :: MLModelFilterVariable
MLMFVRealtimeEndpointStatus :: MLModelFilterVariable
MLMFVStatus :: MLModelFilterVariable
MLMFVTrainingDataSourceId :: MLModelFilterVariable
MLMFVTrainingDataURI :: MLModelFilterVariable
data MLModelType
Binary :: MLModelType
Multiclass :: MLModelType
Regression :: MLModelType
data RealtimeEndpointStatus
Failed :: RealtimeEndpointStatus
None :: RealtimeEndpointStatus
Ready :: RealtimeEndpointStatus
Updating :: RealtimeEndpointStatus

-- | The sort order specified in a listing condition. Possible values
--   include the following:
--   
--   <ul>
--   <li><tt>asc</tt> - Present the information in ascending order (from
--   A-Z). * <tt>dsc</tt> - Present the information in descending order
--   (from Z-A).</li>
--   </ul>
data SortOrder
Asc :: SortOrder
Dsc :: SortOrder
data TaggableResourceType
BatchPrediction :: TaggableResourceType
DataSource :: TaggableResourceType
Evaluation :: TaggableResourceType
MLModel :: TaggableResourceType

-- | Represents the output of a <tt>GetBatchPrediction</tt> operation.
--   
--   The content consists of the detailed metadata, the status, and the
--   data file information of a <tt>Batch Prediction</tt> .
--   
--   <i>See:</i> <a>batchPrediction</a> smart constructor.
data BatchPrediction

-- | Creates a value of <a>BatchPrediction</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>bpStatus</a> - The status of the <tt>BatchPrediction</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to generate
--   predictions for a batch of observations. * <tt>INPROGRESS</tt> - The
--   process is underway. * <tt>FAILED</tt> - The request to perform a
--   batch prediction did not run to completion. It is not usable. *
--   <tt>COMPLETED</tt> - The batch prediction process completed
--   successfully. * <tt>DELETED</tt> - The <tt>BatchPrediction</tt> is
--   marked as deleted. It is not usable.</li>
--   <li><a>bpLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>bpCreatedAt</a> - The time that the <tt>BatchPrediction</tt>
--   was created. The time is expressed in epoch time.</li>
--   <li><a>bpComputeTime</a> - Undocumented member.</li>
--   <li><a>bpInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>bpMLModelId</a> - The ID of the <tt>MLModel</tt> that generated
--   predictions for the <tt>BatchPrediction</tt> request.</li>
--   <li><a>bpBatchPredictionDataSourceId</a> - The ID of the
--   <tt>DataSource</tt> that points to the group of observations to
--   predict.</li>
--   <li><a>bpTotalRecordCount</a> - Undocumented member.</li>
--   <li><a>bpStartedAt</a> - Undocumented member.</li>
--   <li><a>bpBatchPredictionId</a> - The ID assigned to the
--   <tt>BatchPrediction</tt> at creation. This value should be identical
--   to the value of the <tt>BatchPredictionID</tt> in the request.</li>
--   <li><a>bpFinishedAt</a> - Undocumented member.</li>
--   <li><a>bpInvalidRecordCount</a> - Undocumented member.</li>
--   <li><a>bpCreatedByIAMUser</a> - The AWS user account that invoked the
--   <tt>BatchPrediction</tt> . The account type can be either an AWS root
--   account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>bpName</a> - A user-supplied name or description of the
--   <tt>BatchPrediction</tt> .</li>
--   <li><a>bpMessage</a> - A description of the most recent details about
--   processing the batch prediction request.</li>
--   <li><a>bpOutputURI</a> - The location of an Amazon S3 bucket or
--   directory to receive the operation results. The following substrings
--   are not allowed in the <tt>s3 key</tt> portion of the
--   <tt>outputURI</tt> field: <tt>:</tt>, <tt>//</tt>, <tt>/./</tt>,
--   <tt>/../</tt>.</li>
--   </ul>
batchPrediction :: BatchPrediction

-- | The status of the <tt>BatchPrediction</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to generate predictions for a batch of
--   observations. * <tt>INPROGRESS</tt> - The process is underway. *
--   <tt>FAILED</tt> - The request to perform a batch prediction did not
--   run to completion. It is not usable. * <tt>COMPLETED</tt> - The batch
--   prediction process completed successfully. * <tt>DELETED</tt> - The
--   <tt>BatchPrediction</tt> is marked as deleted. It is not usable.
bpStatus :: Lens' BatchPrediction (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | The time that the <tt>BatchPrediction</tt> was created. The time is
--   expressed in epoch time.
bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | Undocumented member.
bpComputeTime :: Lens' BatchPrediction (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that generated predictions for the
--   <tt>BatchPrediction</tt> request.
bpMLModelId :: Lens' BatchPrediction (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that points to the group of
--   observations to predict.
bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text)

-- | Undocumented member.
bpTotalRecordCount :: Lens' BatchPrediction (Maybe Integer)

-- | Undocumented member.
bpStartedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | The ID assigned to the <tt>BatchPrediction</tt> at creation. This
--   value should be identical to the value of the
--   <tt>BatchPredictionID</tt> in the request.
bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text)

-- | Undocumented member.
bpFinishedAt :: Lens' BatchPrediction (Maybe UTCTime)

-- | Undocumented member.
bpInvalidRecordCount :: Lens' BatchPrediction (Maybe Integer)

-- | The AWS user account that invoked the <tt>BatchPrediction</tt> . The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text)

-- | A user-supplied name or description of the <tt>BatchPrediction</tt> .
bpName :: Lens' BatchPrediction (Maybe Text)

-- | A description of the most recent details about processing the batch
--   prediction request.
bpMessage :: Lens' BatchPrediction (Maybe Text)

-- | The location of an Amazon S3 bucket or directory to receive the
--   operation results. The following substrings are not allowed in the
--   <tt>s3 key</tt> portion of the <tt>outputURI</tt> field: <tt>:</tt>,
--   <tt>//</tt>, <tt>/./</tt>, <tt>/../</tt>.
bpOutputURI :: Lens' BatchPrediction (Maybe Text)

-- | Represents the output of the <tt>GetDataSource</tt> operation.
--   
--   The content consists of the detailed metadata and data file
--   information and the current status of the <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>dataSource</a> smart constructor.
data DataSource

-- | Creates a value of <a>DataSource</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>dsStatus</a> - The current status of the <tt>DataSource</tt> .
--   This element can have one of the following values: * PENDING - Amazon
--   Machine Learning (Amazon ML) submitted a request to create a
--   <tt>DataSource</tt> . * INPROGRESS - The creation process is underway.
--   * FAILED - The request to create a <tt>DataSource</tt> did not run to
--   completion. It is not usable. * COMPLETED - The creation process
--   completed successfully. * DELETED - The <tt>DataSource</tt> is marked
--   as deleted. It is not usable.</li>
--   <li><a>dsNumberOfFiles</a> - The number of data files referenced by
--   the <tt>DataSource</tt> .</li>
--   <li><a>dsLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>BatchPrediction</tt> . The time is expressed in epoch time.</li>
--   <li><a>dsCreatedAt</a> - The time that the <tt>DataSource</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>dsComputeTime</a> - Undocumented member.</li>
--   <li><a>dsDataSourceId</a> - The ID that is assigned to the
--   <tt>DataSource</tt> during creation.</li>
--   <li><a>dsRDSMetadata</a> - Undocumented member.</li>
--   <li><a>dsDataSizeInBytes</a> - The total number of observations
--   contained in the data files that the <tt>DataSource</tt>
--   references.</li>
--   <li><a>dsStartedAt</a> - Undocumented member.</li>
--   <li><a>dsFinishedAt</a> - Undocumented member.</li>
--   <li><a>dsCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>DataSource</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>dsName</a> - A user-supplied name or description of the
--   <tt>DataSource</tt> .</li>
--   <li><a>dsDataLocationS3</a> - The location and name of the data in
--   Amazon Simple Storage Service (Amazon S3) that is used by a
--   <tt>DataSource</tt> .</li>
--   <li><a>dsComputeStatistics</a> - The parameter is <tt>true</tt> if
--   statistics need to be generated from the observation data.</li>
--   <li><a>dsMessage</a> - A description of the most recent details about
--   creating the <tt>DataSource</tt> .</li>
--   <li><a>dsRedshiftMetadata</a> - Undocumented member.</li>
--   <li><a>dsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement requirement used when this
--   <tt>DataSource</tt> was created.</li>
--   <li><a>dsRoleARN</a> - Undocumented member.</li>
--   </ul>
dataSource :: DataSource

-- | The current status of the <tt>DataSource</tt> . This element can have
--   one of the following values: * PENDING - Amazon Machine Learning
--   (Amazon ML) submitted a request to create a <tt>DataSource</tt> . *
--   INPROGRESS - The creation process is underway. * FAILED - The request
--   to create a <tt>DataSource</tt> did not run to completion. It is not
--   usable. * COMPLETED - The creation process completed successfully. *
--   DELETED - The <tt>DataSource</tt> is marked as deleted. It is not
--   usable.
dsStatus :: Lens' DataSource (Maybe EntityStatus)

-- | The number of data files referenced by the <tt>DataSource</tt> .
dsNumberOfFiles :: Lens' DataSource (Maybe Integer)

-- | The time of the most recent edit to the <tt>BatchPrediction</tt> . The
--   time is expressed in epoch time.
dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime)

-- | The time that the <tt>DataSource</tt> was created. The time is
--   expressed in epoch time.
dsCreatedAt :: Lens' DataSource (Maybe UTCTime)

-- | Undocumented member.
dsComputeTime :: Lens' DataSource (Maybe Integer)

-- | The ID that is assigned to the <tt>DataSource</tt> during creation.
dsDataSourceId :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRDSMetadata :: Lens' DataSource (Maybe RDSMetadata)

-- | The total number of observations contained in the data files that the
--   <tt>DataSource</tt> references.
dsDataSizeInBytes :: Lens' DataSource (Maybe Integer)

-- | Undocumented member.
dsStartedAt :: Lens' DataSource (Maybe UTCTime)

-- | Undocumented member.
dsFinishedAt :: Lens' DataSource (Maybe UTCTime)

-- | The AWS user account from which the <tt>DataSource</tt> was created.
--   The account type can be either an AWS root account or an AWS Identity
--   and Access Management (IAM) user account.
dsCreatedByIAMUser :: Lens' DataSource (Maybe Text)

-- | A user-supplied name or description of the <tt>DataSource</tt> .
dsName :: Lens' DataSource (Maybe Text)

-- | The location and name of the data in Amazon Simple Storage Service
--   (Amazon S3) that is used by a <tt>DataSource</tt> .
dsDataLocationS3 :: Lens' DataSource (Maybe Text)

-- | The parameter is <tt>true</tt> if statistics need to be generated from
--   the observation data.
dsComputeStatistics :: Lens' DataSource (Maybe Bool)

-- | A description of the most recent details about creating the
--   <tt>DataSource</tt> .
dsMessage :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRedshiftMetadata :: Lens' DataSource (Maybe RedshiftMetadata)

-- | A JSON string that represents the splitting and rearrangement
--   requirement used when this <tt>DataSource</tt> was created.
dsDataRearrangement :: Lens' DataSource (Maybe Text)

-- | Undocumented member.
dsRoleARN :: Lens' DataSource (Maybe Text)

-- | Represents the output of <tt>GetEvaluation</tt> operation.
--   
--   The content consists of the detailed metadata and data file
--   information and the current status of the <tt>Evaluation</tt> .
--   
--   <i>See:</i> <a>evaluation</a> smart constructor.
data Evaluation

-- | Creates a value of <a>Evaluation</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>eStatus</a> - The status of the evaluation. This element can
--   have one of the following values: * <tt>PENDING</tt> - Amazon Machine
--   Learning (Amazon ML) submitted a request to evaluate an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The evaluation is underway.
--   * <tt>FAILED</tt> - The request to evaluate an <tt>MLModel</tt> did
--   not run to completion. It is not usable. * <tt>COMPLETED</tt> - The
--   evaluation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>Evaluation</tt> is marked as deleted. It is not usable.</li>
--   <li><a>ePerformanceMetrics</a> - Measurements of how well the
--   <tt>MLModel</tt> performed, using observations referenced by the
--   <tt>DataSource</tt> . One of the following metrics is returned, based
--   on the type of the <tt>MLModel</tt> : * BinaryAUC: A binary
--   <tt>MLModel</tt> uses the Area Under the Curve (AUC) technique to
--   measure performance. * RegressionRMSE: A regression <tt>MLModel</tt>
--   uses the Root Mean Square Error (RMSE) technique to measure
--   performance. RMSE measures the difference between predicted and actual
--   values for a single variable. * MulticlassAvgFScore: A multiclass
--   <tt>MLModel</tt> uses the F1 score technique to measure performance.
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .</li>
--   <li><a>eLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>Evaluation</tt> . The time is expressed in epoch time.</li>
--   <li><a>eCreatedAt</a> - The time that the <tt>Evaluation</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>eComputeTime</a> - Undocumented member.</li>
--   <li><a>eInputDataLocationS3</a> - The location and name of the data in
--   Amazon Simple Storage Server (Amazon S3) that is used in the
--   evaluation.</li>
--   <li><a>eMLModelId</a> - The ID of the <tt>MLModel</tt> that is the
--   focus of the evaluation.</li>
--   <li><a>eStartedAt</a> - Undocumented member.</li>
--   <li><a>eFinishedAt</a> - Undocumented member.</li>
--   <li><a>eCreatedByIAMUser</a> - The AWS user account that invoked the
--   evaluation. The account type can be either an AWS root account or an
--   AWS Identity and Access Management (IAM) user account.</li>
--   <li><a>eName</a> - A user-supplied name or description of the
--   <tt>Evaluation</tt> .</li>
--   <li><a>eEvaluationId</a> - The ID that is assigned to the
--   <tt>Evaluation</tt> at creation.</li>
--   <li><a>eMessage</a> - A description of the most recent details about
--   evaluating the <tt>MLModel</tt> .</li>
--   <li><a>eEvaluationDataSourceId</a> - The ID of the <tt>DataSource</tt>
--   that is used to evaluate the <tt>MLModel</tt> .</li>
--   </ul>
evaluation :: Evaluation

-- | The status of the evaluation. This element can have one of the
--   following values: * <tt>PENDING</tt> - Amazon Machine Learning (Amazon
--   ML) submitted a request to evaluate an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The evaluation is underway. * <tt>FAILED</tt> -
--   The request to evaluate an <tt>MLModel</tt> did not run to completion.
--   It is not usable. * <tt>COMPLETED</tt> - The evaluation process
--   completed successfully. * <tt>DELETED</tt> - The <tt>Evaluation</tt>
--   is marked as deleted. It is not usable.
eStatus :: Lens' Evaluation (Maybe EntityStatus)

-- | Measurements of how well the <tt>MLModel</tt> performed, using
--   observations referenced by the <tt>DataSource</tt> . One of the
--   following metrics is returned, based on the type of the
--   <tt>MLModel</tt> : * BinaryAUC: A binary <tt>MLModel</tt> uses the
--   Area Under the Curve (AUC) technique to measure performance. *
--   RegressionRMSE: A regression <tt>MLModel</tt> uses the Root Mean
--   Square Error (RMSE) technique to measure performance. RMSE measures
--   the difference between predicted and actual values for a single
--   variable. * MulticlassAvgFScore: A multiclass <tt>MLModel</tt> uses
--   the F1 score technique to measure performance. For more information
--   about performance metrics, please see the <a>Amazon Machine Learning
--   Developer Guide</a> .
ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics)

-- | The time of the most recent edit to the <tt>Evaluation</tt> . The time
--   is expressed in epoch time.
eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime)

-- | The time that the <tt>Evaluation</tt> was created. The time is
--   expressed in epoch time.
eCreatedAt :: Lens' Evaluation (Maybe UTCTime)

-- | Undocumented member.
eComputeTime :: Lens' Evaluation (Maybe Integer)

-- | The location and name of the data in Amazon Simple Storage Server
--   (Amazon S3) that is used in the evaluation.
eInputDataLocationS3 :: Lens' Evaluation (Maybe Text)

-- | The ID of the <tt>MLModel</tt> that is the focus of the evaluation.
eMLModelId :: Lens' Evaluation (Maybe Text)

-- | Undocumented member.
eStartedAt :: Lens' Evaluation (Maybe UTCTime)

-- | Undocumented member.
eFinishedAt :: Lens' Evaluation (Maybe UTCTime)

-- | The AWS user account that invoked the evaluation. The account type can
--   be either an AWS root account or an AWS Identity and Access Management
--   (IAM) user account.
eCreatedByIAMUser :: Lens' Evaluation (Maybe Text)

-- | A user-supplied name or description of the <tt>Evaluation</tt> .
eName :: Lens' Evaluation (Maybe Text)

-- | The ID that is assigned to the <tt>Evaluation</tt> at creation.
eEvaluationId :: Lens' Evaluation (Maybe Text)

-- | A description of the most recent details about evaluating the
--   <tt>MLModel</tt> .
eMessage :: Lens' Evaluation (Maybe Text)

-- | The ID of the <tt>DataSource</tt> that is used to evaluate the
--   <tt>MLModel</tt> .
eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text)

-- | Represents the output of a <tt>GetMLModel</tt> operation.
--   
--   The content consists of the detailed metadata and the current status
--   of the <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>mLModel</a> smart constructor.
data MLModel

-- | Creates a value of <a>MLModel</a> with the minimum fields required to
--   make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>mlmStatus</a> - The current status of an <tt>MLModel</tt> .
--   This element can have one of the following values: * <tt>PENDING</tt>
--   - Amazon Machine Learning (Amazon ML) submitted a request to create an
--   <tt>MLModel</tt> . * <tt>INPROGRESS</tt> - The creation process is
--   underway. * <tt>FAILED</tt> - The request to create an
--   <tt>MLModel</tt> didn't run to completion. The model isn't usable. *
--   <tt>COMPLETED</tt> - The creation process completed successfully. *
--   <tt>DELETED</tt> - The <tt>MLModel</tt> is marked as deleted. It isn't
--   usable.</li>
--   <li><a>mlmLastUpdatedAt</a> - The time of the most recent edit to the
--   <tt>MLModel</tt> . The time is expressed in epoch time.</li>
--   <li><a>mlmTrainingParameters</a> - A list of the training parameters
--   in the <tt>MLModel</tt> . The list is implemented as a map of
--   key-value pairs. The following is the current set of training
--   parameters: * <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed
--   size of the model. Depending on the input data, the size of the model
--   might affect its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   * <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm, which controls overfitting the data by penalizing large
--   coefficients. This parameter tends to drive coefficients to zero,
--   resulting in sparse feature set. If you use this parameter, start by
--   specifying a small value, such as <tt>1.0E-08</tt> . The value is a
--   double that ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The
--   default is to not use L1 normalization. This parameter can't be used
--   when <tt>L2</tt> is specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm, which controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.</li>
--   <li><a>mlmScoreThresholdLastUpdatedAt</a> - The time of the most
--   recent edit to the <tt>ScoreThreshold</tt> . The time is expressed in
--   epoch time.</li>
--   <li><a>mlmCreatedAt</a> - The time that the <tt>MLModel</tt> was
--   created. The time is expressed in epoch time.</li>
--   <li><a>mlmComputeTime</a> - Undocumented member.</li>
--   <li><a>mlmInputDataLocationS3</a> - The location of the data file or
--   directory in Amazon Simple Storage Service (Amazon S3).</li>
--   <li><a>mlmMLModelId</a> - The ID assigned to the <tt>MLModel</tt> at
--   creation.</li>
--   <li><a>mlmSizeInBytes</a> - Undocumented member.</li>
--   <li><a>mlmStartedAt</a> - Undocumented member.</li>
--   <li><a>mlmScoreThreshold</a> - Undocumented member.</li>
--   <li><a>mlmFinishedAt</a> - Undocumented member.</li>
--   <li><a>mlmAlgorithm</a> - The algorithm used to train the
--   <tt>MLModel</tt> . The following algorithm is supported: *
--   <tt>SGD</tt> -- Stochastic gradient descent. The goal of <tt>SGD</tt>
--   is to minimize the gradient of the loss function.</li>
--   <li><a>mlmCreatedByIAMUser</a> - The AWS user account from which the
--   <tt>MLModel</tt> was created. The account type can be either an AWS
--   root account or an AWS Identity and Access Management (IAM) user
--   account.</li>
--   <li><a>mlmName</a> - A user-supplied name or description of the
--   <tt>MLModel</tt> .</li>
--   <li><a>mlmEndpointInfo</a> - The current endpoint of the
--   <tt>MLModel</tt> .</li>
--   <li><a>mlmTrainingDataSourceId</a> - The ID of the training
--   <tt>DataSource</tt> . The <tt>CreateMLModel</tt> operation uses the
--   <tt>TrainingDataSourceId</tt> .</li>
--   <li><a>mlmMessage</a> - A description of the most recent details about
--   accessing the <tt>MLModel</tt> .</li>
--   <li><a>mlmMLModelType</a> - Identifies the <tt>MLModel</tt> category.
--   The following are the available types: * <tt>REGRESSION</tt> -
--   Produces a numeric result. For example, "What price should a house be
--   listed at?" * <tt>BINARY</tt> - Produces one of two possible results.
--   For example, "Is this a child-friendly web site?". *
--   <tt>MULTICLASS</tt> - Produces one of several possible results. For
--   example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".</li>
--   </ul>
mLModel :: MLModel

-- | The current status of an <tt>MLModel</tt> . This element can have one
--   of the following values: * <tt>PENDING</tt> - Amazon Machine Learning
--   (Amazon ML) submitted a request to create an <tt>MLModel</tt> . *
--   <tt>INPROGRESS</tt> - The creation process is underway. *
--   <tt>FAILED</tt> - The request to create an <tt>MLModel</tt> didn't run
--   to completion. The model isn't usable. * <tt>COMPLETED</tt> - The
--   creation process completed successfully. * <tt>DELETED</tt> - The
--   <tt>MLModel</tt> is marked as deleted. It isn't usable.
mlmStatus :: Lens' MLModel (Maybe EntityStatus)

-- | The time of the most recent edit to the <tt>MLModel</tt> . The time is
--   expressed in epoch time.
mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

-- | A list of the training parameters in the <tt>MLModel</tt> . The list
--   is implemented as a map of key-value pairs. The following is the
--   current set of training parameters: *
--   <tt>sgd.maxMLModelSizeInBytes</tt> - The maximum allowed size of the
--   model. Depending on the input data, the size of the model might affect
--   its performance. The value is an integer that ranges from
--   <tt>100000</tt> to <tt>2147483648</tt> . The default value is
--   <tt>33554432</tt> . * <tt>sgd.maxPasses</tt> - The number of times
--   that the training process traverses the observations to build the
--   <tt>MLModel</tt> . The value is an integer that ranges from <tt>1</tt>
--   to <tt>10000</tt> . The default value is <tt>10</tt> . *
--   <tt>sgd.shuffleType</tt> - Whether Amazon ML shuffles the training
--   data. Shuffling the data improves a model's ability to find the
--   optimal solution for a variety of data types. The valid values are
--   <tt>auto</tt> and <tt>none</tt> . The default value is <tt>none</tt> .
--   * <tt>sgd.l1RegularizationAmount</tt> - The coefficient regularization
--   L1 norm, which controls overfitting the data by penalizing large
--   coefficients. This parameter tends to drive coefficients to zero,
--   resulting in sparse feature set. If you use this parameter, start by
--   specifying a small value, such as <tt>1.0E-08</tt> . The value is a
--   double that ranges from <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The
--   default is to not use L1 normalization. This parameter can't be used
--   when <tt>L2</tt> is specified. Use this parameter sparingly. *
--   <tt>sgd.l2RegularizationAmount</tt> - The coefficient regularization
--   L2 norm, which controls overfitting the data by penalizing large
--   coefficients. This tends to drive coefficients to small, nonzero
--   values. If you use this parameter, start by specifying a small value,
--   such as <tt>1.0E-08</tt> . The value is a double that ranges from
--   <tt>0</tt> to <tt>MAX_DOUBLE</tt> . The default is to not use L2
--   normalization. This parameter can't be used when <tt>L1</tt> is
--   specified. Use this parameter sparingly.
mlmTrainingParameters :: Lens' MLModel (HashMap Text Text)

-- | The time of the most recent edit to the <tt>ScoreThreshold</tt> . The
--   time is expressed in epoch time.
mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime)

-- | The time that the <tt>MLModel</tt> was created. The time is expressed
--   in epoch time.
mlmCreatedAt :: Lens' MLModel (Maybe UTCTime)

-- | Undocumented member.
mlmComputeTime :: Lens' MLModel (Maybe Integer)

-- | The location of the data file or directory in Amazon Simple Storage
--   Service (Amazon S3).
mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text)

-- | The ID assigned to the <tt>MLModel</tt> at creation.
mlmMLModelId :: Lens' MLModel (Maybe Text)

-- | Undocumented member.
mlmSizeInBytes :: Lens' MLModel (Maybe Integer)

-- | Undocumented member.
mlmStartedAt :: Lens' MLModel (Maybe UTCTime)

-- | Undocumented member.
mlmScoreThreshold :: Lens' MLModel (Maybe Double)

-- | Undocumented member.
mlmFinishedAt :: Lens' MLModel (Maybe UTCTime)

-- | The algorithm used to train the <tt>MLModel</tt> . The following
--   algorithm is supported: * <tt>SGD</tt> -- Stochastic gradient descent.
--   The goal of <tt>SGD</tt> is to minimize the gradient of the loss
--   function.
mlmAlgorithm :: Lens' MLModel (Maybe Algorithm)

-- | The AWS user account from which the <tt>MLModel</tt> was created. The
--   account type can be either an AWS root account or an AWS Identity and
--   Access Management (IAM) user account.
mlmCreatedByIAMUser :: Lens' MLModel (Maybe Text)

-- | A user-supplied name or description of the <tt>MLModel</tt> .
mlmName :: Lens' MLModel (Maybe Text)

-- | The current endpoint of the <tt>MLModel</tt> .
mlmEndpointInfo :: Lens' MLModel (Maybe RealtimeEndpointInfo)

-- | The ID of the training <tt>DataSource</tt> . The
--   <tt>CreateMLModel</tt> operation uses the
--   <tt>TrainingDataSourceId</tt> .
mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text)

-- | A description of the most recent details about accessing the
--   <tt>MLModel</tt> .
mlmMessage :: Lens' MLModel (Maybe Text)

-- | Identifies the <tt>MLModel</tt> category. The following are the
--   available types: * <tt>REGRESSION</tt> - Produces a numeric result.
--   For example, "What price should a house be listed at?" *
--   <tt>BINARY</tt> - Produces one of two possible results. For example,
--   "Is this a child-friendly web site?". * <tt>MULTICLASS</tt> - Produces
--   one of several possible results. For example, "Is this a HIGH-, LOW-,
--   or MEDIUM-risk trade?".
mlmMLModelType :: Lens' MLModel (Maybe MLModelType)

-- | Measurements of how well the <tt>MLModel</tt> performed on known
--   observations. One of the following metrics is returned, based on the
--   type of the <tt>MLModel</tt> :
--   
--   <ul>
--   <li>BinaryAUC: The binary <tt>MLModel</tt> uses the Area Under the
--   Curve (AUC) technique to measure performance.</li>
--   <li>RegressionRMSE: The regression <tt>MLModel</tt> uses the Root Mean
--   Square Error (RMSE) technique to measure performance. RMSE measures
--   the difference between predicted and actual values for a single
--   variable.</li>
--   <li>MulticlassAvgFScore: The multiclass <tt>MLModel</tt> uses the F1
--   score technique to measure performance.</li>
--   </ul>
--   
--   For more information about performance metrics, please see the
--   <a>Amazon Machine Learning Developer Guide</a> .
--   
--   <i>See:</i> <a>performanceMetrics</a> smart constructor.
data PerformanceMetrics

-- | Creates a value of <a>PerformanceMetrics</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pmProperties</a> - Undocumented member.</li>
--   </ul>
performanceMetrics :: PerformanceMetrics

-- | Undocumented member.
pmProperties :: Lens' PerformanceMetrics (HashMap Text Text)

-- | The output from a <tt>Predict</tt> operation:
--   
--   <ul>
--   <li><tt>Details</tt> - Contains the following attributes:
--   <tt>DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY |
--   MULTICLASS</tt> <tt>DetailsAttributes.ALGORITHM - SGD</tt></li>
--   <li><tt>PredictedLabel</tt> - Present for either a <tt>BINARY</tt> or
--   <tt>MULTICLASS</tt> <tt>MLModel</tt> request.</li>
--   <li><tt>PredictedScores</tt> - Contains the raw classification score
--   corresponding to each label.</li>
--   <li><tt>PredictedValue</tt> - Present for a <tt>REGRESSION</tt>
--   <tt>MLModel</tt> request.</li>
--   </ul>
--   
--   <i>See:</i> <a>prediction</a> smart constructor.
data Prediction

-- | Creates a value of <a>Prediction</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>pPredictedValue</a> - The prediction value for
--   <tt>REGRESSION</tt> <tt>MLModel</tt> .</li>
--   <li><a>pPredictedLabel</a> - The prediction label for either a
--   <tt>BINARY</tt> or <tt>MULTICLASS</tt> <tt>MLModel</tt> .</li>
--   <li><a>pPredictedScores</a> - Undocumented member.</li>
--   <li><a>pDetails</a> - Undocumented member.</li>
--   </ul>
prediction :: Prediction

-- | The prediction value for <tt>REGRESSION</tt> <tt>MLModel</tt> .
pPredictedValue :: Lens' Prediction (Maybe Double)

-- | The prediction label for either a <tt>BINARY</tt> or
--   <tt>MULTICLASS</tt> <tt>MLModel</tt> .
pPredictedLabel :: Lens' Prediction (Maybe Text)

-- | Undocumented member.
pPredictedScores :: Lens' Prediction (HashMap Text Double)

-- | Undocumented member.
pDetails :: Lens' Prediction (HashMap DetailsAttributes Text)

-- | The data specification of an Amazon Relational Database Service
--   (Amazon RDS) <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>rdsDataSpec</a> smart constructor.
data RDSDataSpec

-- | Creates a value of <a>RDSDataSpec</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdsDataSchemaURI</a> - The Amazon S3 location of the
--   <tt>DataSchema</tt> .</li>
--   <li><a>rdsdsDataSchema</a> - A JSON string that represents the schema
--   for an Amazon RDS <tt>DataSource</tt> . The <tt>DataSchema</tt>
--   defines the structure of the observation data in the data file(s)
--   referenced in the <tt>DataSource</tt> . A <tt>DataSchema</tt> is not
--   required if you specify a <tt>DataSchemaUri</tt> Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>rdsdsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>rdsdsDatabaseInformation</a> - Describes the
--   <tt>DatabaseName</tt> and <tt>InstanceIdentifier</tt> of an Amazon RDS
--   database.</li>
--   <li><a>rdsdsSelectSqlQuery</a> - The query that is used to retrieve
--   the observation data for the <tt>DataSource</tt> .</li>
--   <li><a>rdsdsDatabaseCredentials</a> - The AWS Identity and Access
--   Management (IAM) credentials that are used connect to the Amazon RDS
--   database.</li>
--   <li><a>rdsdsS3StagingLocation</a> - The Amazon S3 location for staging
--   Amazon RDS data. The data retrieved from Amazon RDS using
--   <tt>SelectSqlQuery</tt> is stored in this location.</li>
--   <li><a>rdsdsResourceRole</a> - The role
--   (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute
--   Cloud (Amazon EC2) instance to carry out the copy operation from
--   Amazon RDS to an Amazon S3 task. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   <li><a>rdsdsServiceRole</a> - The role (DataPipelineDefaultRole)
--   assumed by AWS Data Pipeline service to monitor the progress of the
--   copy task from Amazon RDS to Amazon S3. For more information, see
--   <a>Role templates</a> for data pipelines.</li>
--   <li><a>rdsdsSubnetId</a> - The subnet ID to be used to access a
--   VPC-based RDS DB instance. This attribute is used by Data Pipeline to
--   carry out the copy task from Amazon RDS to Amazon S3.</li>
--   <li><a>rdsdsSecurityGroupIds</a> - The security group IDs to be used
--   to access a VPC-based RDS DB instance. Ensure that there are
--   appropriate ingress rules set up to allow access to the RDS DB
--   instance. This attribute is used by Data Pipeline to carry out the
--   copy operation from Amazon RDS to an Amazon S3 task.</li>
--   </ul>
rdsDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec

-- | The Amazon S3 location of the <tt>DataSchema</tt> .
rdsdsDataSchemaURI :: Lens' RDSDataSpec (Maybe Text)

-- | A JSON string that represents the schema for an Amazon RDS
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . A <tt>DataSchema</tt> is not required if you
--   specify a <tt>DataSchemaUri</tt> Define your <tt>DataSchema</tt> as a
--   series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text)

-- | Describes the <tt>DatabaseName</tt> and <tt>InstanceIdentifier</tt> of
--   an Amazon RDS database.
rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase

-- | The query that is used to retrieve the observation data for the
--   <tt>DataSource</tt> .
rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text

-- | The AWS Identity and Access Management (IAM) credentials that are used
--   connect to the Amazon RDS database.
rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials

-- | The Amazon S3 location for staging Amazon RDS data. The data retrieved
--   from Amazon RDS using <tt>SelectSqlQuery</tt> is stored in this
--   location.
rdsdsS3StagingLocation :: Lens' RDSDataSpec Text

-- | The role (DataPipelineDefaultResourceRole) assumed by an Amazon
--   Elastic Compute Cloud (Amazon EC2) instance to carry out the copy
--   operation from Amazon RDS to an Amazon S3 task. For more information,
--   see <a>Role templates</a> for data pipelines.
rdsdsResourceRole :: Lens' RDSDataSpec Text

-- | The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
--   service to monitor the progress of the copy task from Amazon RDS to
--   Amazon S3. For more information, see <a>Role templates</a> for data
--   pipelines.
rdsdsServiceRole :: Lens' RDSDataSpec Text

-- | The subnet ID to be used to access a VPC-based RDS DB instance. This
--   attribute is used by Data Pipeline to carry out the copy task from
--   Amazon RDS to Amazon S3.
rdsdsSubnetId :: Lens' RDSDataSpec Text

-- | The security group IDs to be used to access a VPC-based RDS DB
--   instance. Ensure that there are appropriate ingress rules set up to
--   allow access to the RDS DB instance. This attribute is used by Data
--   Pipeline to carry out the copy operation from Amazon RDS to an Amazon
--   S3 task.
rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text]

-- | The database details of an Amazon RDS database.
--   
--   <i>See:</i> <a>rdsDatabase</a> smart constructor.
data RDSDatabase

-- | Creates a value of <a>RDSDatabase</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdInstanceIdentifier</a> - The ID of an RDS DB instance.</li>
--   <li><a>rdsdDatabaseName</a> - Undocumented member.</li>
--   </ul>
rdsDatabase :: Text -> Text -> RDSDatabase

-- | The ID of an RDS DB instance.
rdsdInstanceIdentifier :: Lens' RDSDatabase Text

-- | Undocumented member.
rdsdDatabaseName :: Lens' RDSDatabase Text

-- | The database credentials to connect to a database on an RDS DB
--   instance.
--   
--   <i>See:</i> <a>rdsDatabaseCredentials</a> smart constructor.
data RDSDatabaseCredentials

-- | Creates a value of <a>RDSDatabaseCredentials</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdsdcUsername</a> - Undocumented member.</li>
--   <li><a>rdsdcPassword</a> - Undocumented member.</li>
--   </ul>
rdsDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials

-- | Undocumented member.
rdsdcUsername :: Lens' RDSDatabaseCredentials Text

-- | Undocumented member.
rdsdcPassword :: Lens' RDSDatabaseCredentials Text

-- | The datasource details that are specific to Amazon RDS.
--   
--   <i>See:</i> <a>rdsMetadata</a> smart constructor.
data RDSMetadata

-- | Creates a value of <a>RDSMetadata</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rmSelectSqlQuery</a> - The SQL query that is supplied during
--   <tt>CreateDataSourceFromRDS</tt> . Returns only if <tt>Verbose</tt> is
--   true in <tt>GetDataSourceInput</tt> .</li>
--   <li><a>rmDataPipelineId</a> - The ID of the Data Pipeline instance
--   that is used to carry to copy data from Amazon RDS to Amazon S3. You
--   can use the ID to find details about the instance in the Data Pipeline
--   console.</li>
--   <li><a>rmDatabase</a> - The database details required to connect to an
--   Amazon RDS.</li>
--   <li><a>rmDatabaseUserName</a> - Undocumented member.</li>
--   <li><a>rmResourceRole</a> - The role (DataPipelineDefaultResourceRole)
--   assumed by an Amazon EC2 instance to carry out the copy task from
--   Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   <li><a>rmServiceRole</a> - The role (DataPipelineDefaultRole) assumed
--   by the Data Pipeline service to monitor the progress of the copy task
--   from Amazon RDS to Amazon S3. For more information, see <a>Role
--   templates</a> for data pipelines.</li>
--   </ul>
rdsMetadata :: RDSMetadata

-- | The SQL query that is supplied during <tt>CreateDataSourceFromRDS</tt>
--   . Returns only if <tt>Verbose</tt> is true in
--   <tt>GetDataSourceInput</tt> .
rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text)

-- | The ID of the Data Pipeline instance that is used to carry to copy
--   data from Amazon RDS to Amazon S3. You can use the ID to find details
--   about the instance in the Data Pipeline console.
rmDataPipelineId :: Lens' RDSMetadata (Maybe Text)

-- | The database details required to connect to an Amazon RDS.
rmDatabase :: Lens' RDSMetadata (Maybe RDSDatabase)

-- | Undocumented member.
rmDatabaseUserName :: Lens' RDSMetadata (Maybe Text)

-- | The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2
--   instance to carry out the copy task from Amazon RDS to Amazon S3. For
--   more information, see <a>Role templates</a> for data pipelines.
rmResourceRole :: Lens' RDSMetadata (Maybe Text)

-- | The role (DataPipelineDefaultRole) assumed by the Data Pipeline
--   service to monitor the progress of the copy task from Amazon RDS to
--   Amazon S3. For more information, see <a>Role templates</a> for data
--   pipelines.
rmServiceRole :: Lens' RDSMetadata (Maybe Text)

-- | Describes the real-time endpoint information for an <tt>MLModel</tt> .
--   
--   <i>See:</i> <a>realtimeEndpointInfo</a> smart constructor.
data RealtimeEndpointInfo

-- | Creates a value of <a>RealtimeEndpointInfo</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>reiCreatedAt</a> - The time that the request to create the
--   real-time endpoint for the <tt>MLModel</tt> was received. The time is
--   expressed in epoch time.</li>
--   <li><a>reiEndpointURL</a> - The URI that specifies where to send
--   real-time prediction requests for the <tt>MLModel</tt> .</li>
--   <li><a>reiEndpointStatus</a> - The current status of the real-time
--   endpoint for the <tt>MLModel</tt> . This element can have one of the
--   following values: * <tt>NONE</tt> - Endpoint does not exist or was
--   previously deleted. * <tt>READY</tt> - Endpoint is ready to be used
--   for real-time predictions. * <tt>UPDATING</tt> - Updating/creating the
--   endpoint.</li>
--   <li><a>reiPeakRequestsPerSecond</a> - The maximum processing rate for
--   the real-time endpoint for <tt>MLModel</tt> , measured in incoming
--   requests per second.</li>
--   </ul>
realtimeEndpointInfo :: RealtimeEndpointInfo

-- | The time that the request to create the real-time endpoint for the
--   <tt>MLModel</tt> was received. The time is expressed in epoch time.
reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime)

-- | The URI that specifies where to send real-time prediction requests for
--   the <tt>MLModel</tt> .
reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text)

-- | The current status of the real-time endpoint for the <tt>MLModel</tt>
--   . This element can have one of the following values: * <tt>NONE</tt> -
--   Endpoint does not exist or was previously deleted. * <tt>READY</tt> -
--   Endpoint is ready to be used for real-time predictions. *
--   <tt>UPDATING</tt> - Updating/creating the endpoint.
reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus)

-- | The maximum processing rate for the real-time endpoint for
--   <tt>MLModel</tt> , measured in incoming requests per second.
reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int)

-- | Describes the data specification of an Amazon Redshift
--   <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>redshiftDataSpec</a> smart constructor.
data RedshiftDataSpec

-- | Creates a value of <a>RedshiftDataSpec</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rDataSchemaURI</a> - Describes the schema location for an
--   Amazon Redshift <tt>DataSource</tt> .</li>
--   <li><a>rDataSchema</a> - A JSON string that represents the schema for
--   an Amazon Redshift <tt>DataSource</tt> . The <tt>DataSchema</tt>
--   defines the structure of the observation data in the data file(s)
--   referenced in the <tt>DataSource</tt> . A <tt>DataSchema</tt> is not
--   required if you specify a <tt>DataSchemaUri</tt> . Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>rDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>rDatabaseInformation</a> - Describes the <tt>DatabaseName</tt>
--   and <tt>ClusterIdentifier</tt> for an Amazon Redshift
--   <tt>DataSource</tt> .</li>
--   <li><a>rSelectSqlQuery</a> - Describes the SQL Query to execute on an
--   Amazon Redshift database for an Amazon Redshift <tt>DataSource</tt>
--   .</li>
--   <li><a>rDatabaseCredentials</a> - Describes AWS Identity and Access
--   Management (IAM) credentials that are used connect to the Amazon
--   Redshift database.</li>
--   <li><a>rS3StagingLocation</a> - Describes an Amazon S3 location to
--   store the result set of the <tt>SelectSqlQuery</tt> query.</li>
--   </ul>
redshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec

-- | Describes the schema location for an Amazon Redshift
--   <tt>DataSource</tt> .
rDataSchemaURI :: Lens' RedshiftDataSpec (Maybe Text)

-- | A JSON string that represents the schema for an Amazon Redshift
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . A <tt>DataSchema</tt> is not required if you
--   specify a <tt>DataSchemaUri</tt> . Define your <tt>DataSchema</tt> as
--   a series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
rDataSchema :: Lens' RedshiftDataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text)

-- | Describes the <tt>DatabaseName</tt> and <tt>ClusterIdentifier</tt> for
--   an Amazon Redshift <tt>DataSource</tt> .
rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase

-- | Describes the SQL Query to execute on an Amazon Redshift database for
--   an Amazon Redshift <tt>DataSource</tt> .
rSelectSqlQuery :: Lens' RedshiftDataSpec Text

-- | Describes AWS Identity and Access Management (IAM) credentials that
--   are used connect to the Amazon Redshift database.
rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials

-- | Describes an Amazon S3 location to store the result set of the
--   <tt>SelectSqlQuery</tt> query.
rS3StagingLocation :: Lens' RedshiftDataSpec Text

-- | Describes the database details required to connect to an Amazon
--   Redshift database.
--   
--   <i>See:</i> <a>redshiftDatabase</a> smart constructor.
data RedshiftDatabase

-- | Creates a value of <a>RedshiftDatabase</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdDatabaseName</a> - Undocumented member.</li>
--   <li><a>rdClusterIdentifier</a> - Undocumented member.</li>
--   </ul>
redshiftDatabase :: Text -> Text -> RedshiftDatabase

-- | Undocumented member.
rdDatabaseName :: Lens' RedshiftDatabase Text

-- | Undocumented member.
rdClusterIdentifier :: Lens' RedshiftDatabase Text

-- | Describes the database credentials for connecting to a database on an
--   Amazon Redshift cluster.
--   
--   <i>See:</i> <a>redshiftDatabaseCredentials</a> smart constructor.
data RedshiftDatabaseCredentials

-- | Creates a value of <a>RedshiftDatabaseCredentials</a> with the minimum
--   fields required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>rdcUsername</a> - Undocumented member.</li>
--   <li><a>rdcPassword</a> - Undocumented member.</li>
--   </ul>
redshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials

-- | Undocumented member.
rdcUsername :: Lens' RedshiftDatabaseCredentials Text

-- | Undocumented member.
rdcPassword :: Lens' RedshiftDatabaseCredentials Text

-- | Describes the <tt>DataSource</tt> details specific to Amazon Redshift.
--   
--   <i>See:</i> <a>redshiftMetadata</a> smart constructor.
data RedshiftMetadata

-- | Creates a value of <a>RedshiftMetadata</a> with the minimum fields
--   required to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>redSelectSqlQuery</a> - The SQL query that is specified during
--   <tt>CreateDataSourceFromRedshift</tt> . Returns only if
--   <tt>Verbose</tt> is true in GetDataSourceInput.</li>
--   <li><a>redRedshiftDatabase</a> - Undocumented member.</li>
--   <li><a>redDatabaseUserName</a> - Undocumented member.</li>
--   </ul>
redshiftMetadata :: RedshiftMetadata

-- | The SQL query that is specified during
--   <tt>CreateDataSourceFromRedshift</tt> . Returns only if
--   <tt>Verbose</tt> is true in GetDataSourceInput.
redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text)

-- | Undocumented member.
redRedshiftDatabase :: Lens' RedshiftMetadata (Maybe RedshiftDatabase)

-- | Undocumented member.
redDatabaseUserName :: Lens' RedshiftMetadata (Maybe Text)

-- | Describes the data specification of a <tt>DataSource</tt> .
--   
--   <i>See:</i> <a>s3DataSpec</a> smart constructor.
data S3DataSpec

-- | Creates a value of <a>S3DataSpec</a> with the minimum fields required
--   to make a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>sdsDataSchema</a> - A JSON string that represents the schema
--   for an Amazon S3 <tt>DataSource</tt> . The <tt>DataSchema</tt> defines
--   the structure of the observation data in the data file(s) referenced
--   in the <tt>DataSource</tt> . You must provide either the
--   <tt>DataSchema</tt> or the <tt>DataSchemaLocationS3</tt> . Define your
--   <tt>DataSchema</tt> as a series of key-value pairs.
--   <tt>attributes</tt> and <tt>excludedVariableNames</tt> have an array
--   of key-value pairs for their value. Use the following format to define
--   your <tt>DataSchema</tt> . { "version": "1.0",
--   "recordAnnotationFieldName": <a>F1</a>, "recordWeightFieldName":
--   <a>F2</a>, "targetFieldName": <a>F3</a>, "dataFormat": <a>CSV</a>,
--   "dataFileContainsHeader": true, "attributes": [ { "fieldName":
--   <a>F1</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F2</a>,
--   "fieldType": <a>NUMERIC</a> }, { "fieldName": <a>F3</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F4</a>, "fieldType":
--   <a>NUMERIC</a> }, { "fieldName": <a>F5</a>, "fieldType":
--   <a>CATEGORICAL</a> }, { "fieldName": <a>F6</a>, "fieldType":
--   <a>TEXT</a> }, { "fieldName": <a>F7</a>, "fieldType":
--   <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>, "fieldType":
--   <a>WEIGHTED_STRING_SEQUENCE</a> } ], "excludedVariableNames": [
--   <a>F6</a> ] }</li>
--   <li><a>sdsDataSchemaLocationS3</a> - Describes the schema location in
--   Amazon S3. You must provide either the <tt>DataSchema</tt> or the
--   <tt>DataSchemaLocationS3</tt> .</li>
--   <li><a>sdsDataRearrangement</a> - A JSON string that represents the
--   splitting and rearrangement processing to be applied to a
--   <tt>DataSource</tt> . If the <tt>DataRearrangement</tt> parameter is
--   not provided, all of the input data is used to create the
--   <tt>Datasource</tt> . There are multiple parameters that control what
--   data is used to create a datasource: * <b><tt>percentBegin</tt> </b>
--   Use <tt>percentBegin</tt> to indicate the beginning of the range of
--   the data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>percentEnd</tt>
--   </b> Use <tt>percentEnd</tt> to indicate the end of the range of the
--   data used to create the Datasource. If you do not include
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML includes all
--   of the data when creating the datasource. * <b><tt>complement</tt>
--   </b> The <tt>complement</tt> parameter instructs Amazon ML to use the
--   data that is not included in the range of <tt>percentBegin</tt> to
--   <tt>percentEnd</tt> to create a datasource. The <tt>complement</tt>
--   parameter is useful if you need to create complementary datasources
--   for training and evaluation. To create a complementary datasource, use
--   the same values for <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   along with the <tt>complement</tt> parameter. For example, the
--   following two datasources do not share any data, and can be used to
--   train and evaluate a model. The first datasource has 25 percent of the
--   data, and the second one has 75 percent of the data. Datasource for
--   evaluation: <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt>
--   Datasource for training: <tt>{"splitting":{"percentBegin":0,
--   "percentEnd":25, "complement":"true"}}</tt> * <b><tt>strategy</tt>
--   </b> To change how Amazon ML splits the data for a datasource, use the
--   <tt>strategy</tt> parameter. The default value for the
--   <tt>strategy</tt> parameter is <tt>sequential</tt> , meaning that
--   Amazon ML takes all of the data records between the
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> parameters for the
--   datasource, in the order that the records appear in the input data.
--   The following two <tt>DataRearrangement</tt> lines are examples of
--   sequentially ordered training and evaluation datasources: Datasource
--   for evaluation: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential"}}</tt> Datasource for training:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt></li>
--   <li><a>sdsDataLocationS3</a> - The location of the data file(s) used
--   by a <tt>DataSource</tt> . The URI specifies a data file or an Amazon
--   Simple Storage Service (Amazon S3) directory or bucket containing data
--   files.</li>
--   </ul>
s3DataSpec :: Text -> S3DataSpec

-- | A JSON string that represents the schema for an Amazon S3
--   <tt>DataSource</tt> . The <tt>DataSchema</tt> defines the structure of
--   the observation data in the data file(s) referenced in the
--   <tt>DataSource</tt> . You must provide either the <tt>DataSchema</tt>
--   or the <tt>DataSchemaLocationS3</tt> . Define your <tt>DataSchema</tt>
--   as a series of key-value pairs. <tt>attributes</tt> and
--   <tt>excludedVariableNames</tt> have an array of key-value pairs for
--   their value. Use the following format to define your
--   <tt>DataSchema</tt> . { "version": "1.0", "recordAnnotationFieldName":
--   <a>F1</a>, "recordWeightFieldName": <a>F2</a>, "targetFieldName":
--   <a>F3</a>, "dataFormat": <a>CSV</a>, "dataFileContainsHeader": true,
--   "attributes": [ { "fieldName": <a>F1</a>, "fieldType": <a>TEXT</a> },
--   { "fieldName": <a>F2</a>, "fieldType": <a>NUMERIC</a> }, {
--   "fieldName": <a>F3</a>, "fieldType": <a>CATEGORICAL</a> }, {
--   "fieldName": <a>F4</a>, "fieldType": <a>NUMERIC</a> }, { "fieldName":
--   <a>F5</a>, "fieldType": <a>CATEGORICAL</a> }, { "fieldName":
--   <a>F6</a>, "fieldType": <a>TEXT</a> }, { "fieldName": <a>F7</a>,
--   "fieldType": <a>WEIGHTED_INT_SEQUENCE</a> }, { "fieldName": <a>F8</a>,
--   "fieldType": <a>WEIGHTED_STRING_SEQUENCE</a> } ],
--   "excludedVariableNames": [ <a>F6</a> ] }
sdsDataSchema :: Lens' S3DataSpec (Maybe Text)

-- | Describes the schema location in Amazon S3. You must provide either
--   the <tt>DataSchema</tt> or the <tt>DataSchemaLocationS3</tt> .
sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text)

-- | A JSON string that represents the splitting and rearrangement
--   processing to be applied to a <tt>DataSource</tt> . If the
--   <tt>DataRearrangement</tt> parameter is not provided, all of the input
--   data is used to create the <tt>Datasource</tt> . There are multiple
--   parameters that control what data is used to create a datasource: *
--   <b><tt>percentBegin</tt> </b> Use <tt>percentBegin</tt> to indicate
--   the beginning of the range of the data used to create the Datasource.
--   If you do not include <tt>percentBegin</tt> and <tt>percentEnd</tt> ,
--   Amazon ML includes all of the data when creating the datasource. *
--   <b><tt>percentEnd</tt> </b> Use <tt>percentEnd</tt> to indicate the
--   end of the range of the data used to create the Datasource. If you do
--   not include <tt>percentBegin</tt> and <tt>percentEnd</tt> , Amazon ML
--   includes all of the data when creating the datasource. *
--   <b><tt>complement</tt> </b> The <tt>complement</tt> parameter
--   instructs Amazon ML to use the data that is not included in the range
--   of <tt>percentBegin</tt> to <tt>percentEnd</tt> to create a
--   datasource. The <tt>complement</tt> parameter is useful if you need to
--   create complementary datasources for training and evaluation. To
--   create a complementary datasource, use the same values for
--   <tt>percentBegin</tt> and <tt>percentEnd</tt> , along with the
--   <tt>complement</tt> parameter. For example, the following two
--   datasources do not share any data, and can be used to train and
--   evaluate a model. The first datasource has 25 percent of the data, and
--   the second one has 75 percent of the data. Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":0, "percentEnd":25}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":0, "percentEnd":25,
--   "complement":"true"}}</tt> * <b><tt>strategy</tt> </b> To change how
--   Amazon ML splits the data for a datasource, use the <tt>strategy</tt>
--   parameter. The default value for the <tt>strategy</tt> parameter is
--   <tt>sequential</tt> , meaning that Amazon ML takes all of the data
--   records between the <tt>percentBegin</tt> and <tt>percentEnd</tt>
--   parameters for the datasource, in the order that the records appear in
--   the input data. The following two <tt>DataRearrangement</tt> lines are
--   examples of sequentially ordered training and evaluation datasources:
--   Datasource for evaluation: <tt>{"splitting":{"percentBegin":70,
--   "percentEnd":100, "strategy":"sequential"}}</tt> Datasource for
--   training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"sequential", "complement":"true"}}</tt> To randomly split
--   the input data into the proportions indicated by the percentBegin and
--   percentEnd parameters, set the <tt>strategy</tt> parameter to
--   <tt>random</tt> and provide a string that is used as the seed value
--   for the random data splitting (for example, you can use the S3 path to
--   your data as the random seed string). If you choose the random split
--   strategy, Amazon ML assigns each row of data a pseudo-random number
--   between 0 and 100, and then selects the rows that have an assigned
--   number between <tt>percentBegin</tt> and <tt>percentEnd</tt> .
--   Pseudo-random numbers are assigned using both the input seed string
--   value and the byte offset as a seed, so changing the data results in a
--   different split. Any existing ordering is preserved. The random
--   splitting strategy ensures that variables in the training and
--   evaluation data are distributed similarly. It is useful in the cases
--   where the input data may have an implicit sort order, which would
--   otherwise result in training and evaluation datasources containing
--   non-similar data records. The following two <tt>DataRearrangement</tt>
--   lines are examples of non-sequentially ordered training and evaluation
--   datasources: Datasource for evaluation:
--   <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv"}}</tt> Datasource
--   for training: <tt>{"splitting":{"percentBegin":70, "percentEnd":100,
--   "strategy":"random",
--   "randomSeed"="s3:/<i>my_s3_path</i>bucket/file.csv",
--   "complement":"true"}}</tt>
sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text)

-- | The location of the data file(s) used by a <tt>DataSource</tt> . The
--   URI specifies a data file or an Amazon Simple Storage Service (Amazon
--   S3) directory or bucket containing data files.
sdsDataLocationS3 :: Lens' S3DataSpec Text

-- | A custom key-value pair associated with an ML object, such as an ML
--   model.
--   
--   <i>See:</i> <a>tag</a> smart constructor.
data Tag

-- | Creates a value of <a>Tag</a> with the minimum fields required to make
--   a request.
--   
--   Use one of the following lenses to modify other fields as desired:
--   
--   <ul>
--   <li><a>tagValue</a> - An optional string, typically used to describe
--   or define the tag. Valid characters include Unicode letters, digits,
--   white space, _, ., /, =, +, -, %, and @.</li>
--   <li><a>tagKey</a> - A unique identifier for the tag. Valid characters
--   include Unicode letters, digits, white space, _, ., /, =, +, -, %, and
--   @.</li>
--   </ul>
tag :: Tag

-- | An optional string, typically used to describe or define the tag.
--   Valid characters include Unicode letters, digits, white space, _, .,
--   /, =, +, -, %, and @.
tagValue :: Lens' Tag (Maybe Text)

-- | A unique identifier for the tag. Valid characters include Unicode
--   letters, digits, white space, _, ., /, =, +, -, %, and @.
tagKey :: Lens' Tag (Maybe Text)
