Version 2014-12-12 of the Amazon Machine Learning service.

PerformanceMetrics constructor.

The fields accessible through corresponding lenses are:

• pmProperties :: HashMap Text Text

RealtimeEndpointInfo constructor.

The fields accessible through corresponding lenses are:

• reiCreatedAt :: Maybe UTCTime
• reiEndpointStatus :: Maybe RealtimeEndpointStatus
• reiEndpointUrl :: Maybe Text
• reiPeakRequestsPerSecond :: Maybe Int

The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

The current status of the real-time endpoint for the MLModel. This element can have one of the following values:

NONE - Endpoint does not exist or was previously deleted. READY - Endpoint is ready to be used for real-time predictions. UPDATING - Updating/creating the endpoint.

The URI that specifies where to send real-time prediction requests for the MLModel.

Note The application must wait until the real-time endpoint is ready before using this URI.

The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

Prediction constructor.

The fields accessible through corresponding lenses are:

• pDetails :: HashMap DetailsAttributes Text
• pPredictedLabel :: Maybe Text
• pPredictedScores :: HashMap Text Double
• pPredictedValue :: Maybe Double

The prediction label for either a BINARY or MULTICLASS MLModel.

The prediction value for REGRESSION MLModel.

S3DataSpec constructor.

The fields accessible through corresponding lenses are:

• sdsDataLocationS3 :: Text
• sdsDataRearrangement :: Maybe Text
• sdsDataSchema :: Maybe Text
• sdsDataSchemaLocationS3 :: Maybe Text

The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.

Describes the splitting requirement of a Datasource.

Describes the schema for an Amazon S3 DataSource.

Describes the schema Location in Amazon S3.

RDSDataSpec constructor.

The fields accessible through corresponding lenses are:

• rdsdsDataRearrangement :: Maybe Text
• rdsdsDataSchema :: Maybe Text
• rdsdsDataSchemaUri :: Maybe Text
• rdsdsDatabaseCredentials :: RDSDatabaseCredentials
• rdsdsDatabaseInformation :: RDSDatabase
• rdsdsResourceRole :: Text
• rdsdsS3StagingLocation :: Text
• rdsdsSecurityGroupIds :: [Text]
• rdsdsSelectSqlQuery :: Text
• rdsdsServiceRole :: Text
• rdsdsSubnetId :: Text

DataRearrangement - A JSON string that represents the splitting requirement of a DataSource.

Sample - ' "{"randomSeed":"some-random-seed","splitting":{"percentBegin":10,"percentEnd":60}}"'

A JSON string that represents the schema. This is not required if DataSchemaUri is specified.

The Amazon S3 location of the DataSchema.

The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.

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 Role templates for data pipelines.

The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

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.

The query that is used to retrieve the observation data for the DataSource.

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 Role templates for data pipelines.

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.

RDSMetadata constructor.

The fields accessible through corresponding lenses are:

• rdsmDataPipelineId :: Maybe Text
• rdsmDatabase :: Maybe RDSDatabase
• rdsmDatabaseUserName :: Maybe Text
• rdsmResourceRole :: Maybe Text
• rdsmSelectSqlQuery :: Maybe Text
• rdsmServiceRole :: 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.

The database details required to connect to an Amazon RDS.

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 Role templates for data pipelines.

The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

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 Role templates for data pipelines.

RedshiftDatabase constructor.

The fields accessible through corresponding lenses are:

• rdClusterIdentifier :: Text
• rdDatabaseName :: Text

RedshiftDatabaseCredentials constructor.

The fields accessible through corresponding lenses are:

• rdcPassword :: Text
• rdcUsername :: Text

MLModel constructor.

The fields accessible through corresponding lenses are:

• mlmAlgorithm :: Maybe Algorithm
• mlmCreatedAt :: Maybe UTCTime
• mlmCreatedByIamUser :: Maybe Text
• mlmEndpointInfo :: Maybe RealtimeEndpointInfo
• mlmInputDataLocationS3 :: Maybe Text
• mlmLastUpdatedAt :: Maybe UTCTime
• mlmMLModelId :: Maybe Text
• mlmMLModelType :: Maybe MLModelType
• mlmMessage :: Maybe Text
• mlmName :: Maybe Text
• mlmScoreThreshold :: Maybe Double
• mlmScoreThresholdLastUpdatedAt :: Maybe UTCTime
• mlmSizeInBytes :: Maybe Integer
• mlmStatus :: Maybe EntityStatus
• mlmTrainingDataSourceId :: Maybe Text
• mlmTrainingParameters :: HashMap Text Text

The algorithm used to train the MLModel. The following algorithm is supported:

SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

The time that the MLModel was created. The time is expressed in epoch time.

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

The current endpoint of the MLModel.

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

The ID assigned to the MLModel at creation.

Identifies the MLModel category. The following are the available types:

REGRESSION - Produces a numeric result. For example, "What listing price should a house have?". BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?". MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

A description of the most recent details about accessing the MLModel.

A user-supplied name or description of the MLModel.

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

The current status of an MLModel. This element can have one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel. INPROGRESS - The creation process is underway. FAILED - The request to create an MLModel did not run to completion. It is not usable. COMPLETED - The creation process completed successfully. DELETED - The MLModel is marked as deleted. It is not usable.

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

'sgd.l1RegularizationAmount' - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

'sgd.l2RegularizationAmount' - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

'sgd.maxPasses' - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

'sgd.maxMLModelSizeInBytes' - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

BatchPrediction constructor.

The fields accessible through corresponding lenses are:

• bpBatchPredictionDataSourceId :: Maybe Text
• bpBatchPredictionId :: Maybe Text
• bpCreatedAt :: Maybe UTCTime
• bpCreatedByIamUser :: Maybe Text
• bpInputDataLocationS3 :: Maybe Text
• bpLastUpdatedAt :: Maybe UTCTime
• bpMLModelId :: Maybe Text
• bpMessage :: Maybe Text
• bpName :: Maybe Text
• bpOutputUri :: Maybe Text
• bpStatus :: Maybe EntityStatus

The ID of the DataSource that points to the group of observations to predict.

The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.

The time that the BatchPrediction was created. The time is expressed in epoch time.

The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

The ID of the MLModel that generated predictions for the BatchPrediction request.

A description of the most recent details about processing the batch prediction request.

A user-supplied name or description of the BatchPrediction.

The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the "outputURI" field: :, //, /./, /../.

The status of the BatchPrediction. This element can have one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations. INPROGRESS - The process is underway. FAILED - The request to peform a batch prediction did not run to completion. It is not usable. COMPLETED - The batch prediction process completed successfully. DELETED - The BatchPrediction is marked as deleted. It is not usable.

DataSource constructor.

The fields accessible through corresponding lenses are:

• dsComputeStatistics :: Maybe Bool
• dsCreatedAt :: Maybe UTCTime
• dsCreatedByIamUser :: Maybe Text
• dsDataLocationS3 :: Maybe Text
• dsDataRearrangement :: Maybe Text
• dsDataSizeInBytes :: Maybe Integer
• dsDataSourceId :: Maybe Text
• dsLastUpdatedAt :: Maybe UTCTime
• dsMessage :: Maybe Text
• dsName :: Maybe Text
• dsNumberOfFiles :: Maybe Integer
• dsRDSMetadata :: Maybe RDSMetadata
• dsRedshiftMetadata :: Maybe RedshiftMetadata
• dsRoleARN :: Maybe Text
• dsStatus :: Maybe EntityStatus

The parameter is true if statistics need to be generated from the observation data.

The time that the DataSource was created. The time is expressed in epoch time.

The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource.

A JSON string that represents the splitting requirement of a Datasource.

The total number of observations contained in the data files that the DataSource references.

The ID that is assigned to the DataSource during creation.

The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

A description of the most recent details about creating the DataSource.

A user-supplied name or description of the DataSource.

The number of data files referenced by the DataSource.

The current status of the DataSource. This element can have one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a DataSource. INPROGRESS - The creation process is underway. FAILED - The request to create a DataSource did not run to completion. It is not usable. COMPLETED - The creation process completed successfully. DELETED - The DataSource is marked as deleted. It is not usable.

RDSDatabase constructor.

The fields accessible through corresponding lenses are:

• rdsdDatabaseName :: Text
• rdsdInstanceIdentifier :: Text

The ID of an RDS DB instance.

RDSDatabaseCredentials constructor.

The fields accessible through corresponding lenses are:

• rdsdcPassword :: Text
• rdsdcUsername :: Text

RedshiftDataSpec constructor.

The fields accessible through corresponding lenses are:

• rdsDataRearrangement :: Maybe Text
• rdsDataSchema :: Maybe Text
• rdsDataSchemaUri :: Maybe Text
• rdsDatabaseCredentials :: RedshiftDatabaseCredentials
• rdsDatabaseInformation :: RedshiftDatabase
• rdsS3StagingLocation :: Text
• rdsSelectSqlQuery :: Text

Describes the splitting specifications for a DataSource.

Describes the schema for an Amazon Redshift DataSource.

Describes the schema location for an Amazon Redshift DataSource.

Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

Evaluation constructor.

The fields accessible through corresponding lenses are:

• eCreatedAt :: Maybe UTCTime
• eCreatedByIamUser :: Maybe Text
• eEvaluationDataSourceId :: Maybe Text
• eEvaluationId :: Maybe Text
• eInputDataLocationS3 :: Maybe Text
• eLastUpdatedAt :: Maybe UTCTime
• eMLModelId :: Maybe Text
• eMessage :: Maybe Text
• eName :: Maybe Text
• ePerformanceMetrics :: Maybe PerformanceMetrics
• eStatus :: Maybe EntityStatus

The time that the Evaluation was created. The time is expressed in epoch time.

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.

The ID of the DataSource that is used to evaluate the MLModel.

The ID that is assigned to the Evaluation at creation.

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

The ID of the MLModel that is the focus of the evaluation.

A description of the most recent details about evaluating the MLModel.

A user-supplied name or description of the Evaluation.

Measurements of how well the MLModel performed, using observations referenced by the DataSource. One of the following metrics is returned, based on the type of the MLModel:

BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.

RegressionRMSE: A regression MLModel 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 MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the AmazonMachine Learning Developer Guide.

The status of the evaluation. This element can have one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel. INPROGRESS - The evaluation is underway. FAILED - The request to evaluate an MLModel did not run to completion. It is not usable. COMPLETED - The evaluation process completed successfully. DELETED - The Evaluation is marked as deleted. It is not usable.

RedshiftMetadata constructor.

The fields accessible through corresponding lenses are:

• rmDatabaseUserName :: Maybe Text
• rmRedshiftDatabase :: Maybe RedshiftDatabase
• rmSelectSqlQuery :: Maybe Text

The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput.