Module types 

Data structures used by operation inputs/outputs.

Modules

builders

Builders

error

Error types that Amazon Machine Learning can respond with.

Structs

BatchPrediction

Represents the output of a GetBatchPrediction operation.

The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.

DataSource

Represents the output of the GetDataSource operation.

The content consists of the detailed metadata and data file information and the current status of the DataSource.

Evaluation

Represents the output of GetEvaluation operation.

The content consists of the detailed metadata and data file information and the current status of the Evaluation.

MlModel

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

PerformanceMetrics

Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:

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

• RegressionRMSE: The 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: The multiclass MLModel uses the F1 score technique to measure performance.

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

Prediction

The output from a Predict operation:

• Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD

• PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.

• PredictedScores - Contains the raw classification score corresponding to each label.

• PredictedValue - Present for a REGRESSION MLModel request.

RdsDataSpec

The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

RdsDatabase

The database details of an Amazon RDS database.

RdsDatabaseCredentials

The database credentials to connect to a database on an RDS DB instance.

RdsMetadata

The datasource details that are specific to Amazon RDS.

RealtimeEndpointInfo

Describes the real-time endpoint information for an MLModel.

RedshiftDataSpec

Describes the data specification of an Amazon Redshift DataSource.

RedshiftDatabase

Describes the database details required to connect to an Amazon Redshift database.

RedshiftDatabaseCredentials

Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

RedshiftMetadata

Describes the DataSource details specific to Amazon Redshift.

S3DataSpec

Describes the data specification of a DataSource.

Tag

A custom key-value pair associated with an ML object, such as an ML model.

Enums

Algorithm

When writing a match expression against Algorithm, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

BatchPredictionFilterVariable

When writing a match expression against BatchPredictionFilterVariable, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

DataSourceFilterVariable

When writing a match expression against DataSourceFilterVariable, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

DetailsAttributes

When writing a match expression against DetailsAttributes, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

EntityStatus

When writing a match expression against EntityStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

EvaluationFilterVariable

When writing a match expression against EvaluationFilterVariable, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

MlModelFilterVariable

When writing a match expression against MlModelFilterVariable, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

MlModelType

When writing a match expression against MlModelType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

RealtimeEndpointStatus

When writing a match expression against RealtimeEndpointStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

SortOrder

When writing a match expression against SortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

TaggableResourceType

When writing a match expression against TaggableResourceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.