Interface MachineLearningClient

All Superinterfaces:

AutoCloseable, AwsClient, SdkAutoCloseable, SdkClient

@Generated("software.amazon.awssdk:codegen")
@ThreadSafe
public interface MachineLearningClient
extends AwsClient

Service client for accessing Amazon Machine Learning. This can be created using the static builder() method. Definition of the public APIs exposed by Amazon Machine Learning

Field Summary

Fields

Modifier and Type	Field	Description
static final String	SERVICE_METADATA_ID	Value for looking up the service's metadata from the ServiceMetadataProvider.
static final String	SERVICE_NAME

Method Summary

Modifier and Type	Method	Description
default AddTagsResponse	addTags(Consumer<AddTagsRequest.Builder> addTagsRequest)	Adds one or more tags to an object, up to a limit of 10.
default AddTagsResponse	addTags(AddTagsRequest addTagsRequest)	Adds one or more tags to an object, up to a limit of 10.
static MachineLearningClientBuilder	builder()	Create a builder that can be used to configure and create a MachineLearningClient.
static MachineLearningClient	create()	Create a MachineLearningClient with the region loaded from the DefaultAwsRegionProviderChain and credentials loaded from the DefaultCredentialsProvider.
default CreateBatchPredictionResponse	createBatchPrediction(Consumer<CreateBatchPredictionRequest.Builder> createBatchPredictionRequest)	Generates predictions for a group of observations.
default CreateBatchPredictionResponse	createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest)	Generates predictions for a group of observations.
default CreateDataSourceFromRdsResponse	createDataSourceFromRDS(Consumer<CreateDataSourceFromRdsRequest.Builder> createDataSourceFromRdsRequest)	Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS).
default CreateDataSourceFromRdsResponse	createDataSourceFromRDS(CreateDataSourceFromRdsRequest createDataSourceFromRdsRequest)	Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS).
default CreateDataSourceFromRedshiftResponse	createDataSourceFromRedshift(Consumer<CreateDataSourceFromRedshiftRequest.Builder> createDataSourceFromRedshiftRequest)	Creates a DataSource from a database hosted on an Amazon Redshift cluster.
default CreateDataSourceFromRedshiftResponse	createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)	Creates a DataSource from a database hosted on an Amazon Redshift cluster.
default CreateDataSourceFromS3Response	createDataSourceFromS3(Consumer<CreateDataSourceFromS3Request.Builder> createDataSourceFromS3Request)	Creates a DataSource object.
default CreateDataSourceFromS3Response	createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request)	Creates a DataSource object.
default CreateEvaluationResponse	createEvaluation(Consumer<CreateEvaluationRequest.Builder> createEvaluationRequest)	Creates a new Evaluation of an MLModel.
default CreateEvaluationResponse	createEvaluation(CreateEvaluationRequest createEvaluationRequest)	Creates a new Evaluation of an MLModel.
default CreateMlModelResponse	createMLModel(Consumer<CreateMlModelRequest.Builder> createMlModelRequest)	Creates a new MLModel using the DataSource and the recipe as information sources.
default CreateMlModelResponse	createMLModel(CreateMlModelRequest createMlModelRequest)	Creates a new MLModel using the DataSource and the recipe as information sources.
default CreateRealtimeEndpointResponse	createRealtimeEndpoint(Consumer<CreateRealtimeEndpointRequest.Builder> createRealtimeEndpointRequest)	Creates a real-time endpoint for the MLModel.
default CreateRealtimeEndpointResponse	createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)	Creates a real-time endpoint for the MLModel.
default DeleteBatchPredictionResponse	deleteBatchPrediction(Consumer<DeleteBatchPredictionRequest.Builder> deleteBatchPredictionRequest)	Assigns the DELETED status to a BatchPrediction, rendering it unusable.
default DeleteBatchPredictionResponse	deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest)	Assigns the DELETED status to a BatchPrediction, rendering it unusable.
default DeleteDataSourceResponse	deleteDataSource(Consumer<DeleteDataSourceRequest.Builder> deleteDataSourceRequest)	Assigns the DELETED status to a DataSource, rendering it unusable.
default DeleteDataSourceResponse	deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest)	Assigns the DELETED status to a DataSource, rendering it unusable.
default DeleteEvaluationResponse	deleteEvaluation(Consumer<DeleteEvaluationRequest.Builder> deleteEvaluationRequest)	Assigns the DELETED status to an Evaluation, rendering it unusable.
default DeleteEvaluationResponse	deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest)	Assigns the DELETED status to an Evaluation, rendering it unusable.
default DeleteMlModelResponse	deleteMLModel(Consumer<DeleteMlModelRequest.Builder> deleteMlModelRequest)	Assigns the DELETED status to an MLModel, rendering it unusable.
default DeleteMlModelResponse	deleteMLModel(DeleteMlModelRequest deleteMlModelRequest)	Assigns the DELETED status to an MLModel, rendering it unusable.
default DeleteRealtimeEndpointResponse	deleteRealtimeEndpoint(Consumer<DeleteRealtimeEndpointRequest.Builder> deleteRealtimeEndpointRequest)	Deletes a real time endpoint of an MLModel.
default DeleteRealtimeEndpointResponse	deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)	Deletes a real time endpoint of an MLModel.
default DeleteTagsResponse	deleteTags(Consumer<DeleteTagsRequest.Builder> deleteTagsRequest)	Deletes the specified tags associated with an ML object.
default DeleteTagsResponse	deleteTags(DeleteTagsRequest deleteTagsRequest)	Deletes the specified tags associated with an ML object.
default DescribeBatchPredictionsResponse	describeBatchPredictions()	Returns a list of BatchPrediction operations that match the search criteria in the request.
default DescribeBatchPredictionsResponse	describeBatchPredictions(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest)	Returns a list of BatchPrediction operations that match the search criteria in the request.
default DescribeBatchPredictionsResponse	describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)	Returns a list of BatchPrediction operations that match the search criteria in the request.
default DescribeBatchPredictionsIterable	describeBatchPredictionsPaginator()	This is a variant of describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest) operation.
default DescribeBatchPredictionsIterable	describeBatchPredictionsPaginator(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest)	This is a variant of describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest) operation.
default DescribeBatchPredictionsIterable	describeBatchPredictionsPaginator(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)	This is a variant of describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest) operation.
default DescribeDataSourcesResponse	describeDataSources()	Returns a list of DataSource that match the search criteria in the request.
default DescribeDataSourcesResponse	describeDataSources(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest)	Returns a list of DataSource that match the search criteria in the request.
default DescribeDataSourcesResponse	describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)	Returns a list of DataSource that match the search criteria in the request.
default DescribeDataSourcesIterable	describeDataSourcesPaginator()	This is a variant of describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest) operation.
default DescribeDataSourcesIterable	describeDataSourcesPaginator(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest)	This is a variant of describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest) operation.
default DescribeDataSourcesIterable	describeDataSourcesPaginator(DescribeDataSourcesRequest describeDataSourcesRequest)	This is a variant of describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest) operation.
default DescribeEvaluationsResponse	describeEvaluations()	Returns a list of DescribeEvaluations that match the search criteria in the request.
default DescribeEvaluationsResponse	describeEvaluations(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest)	Returns a list of DescribeEvaluations that match the search criteria in the request.
default DescribeEvaluationsResponse	describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)	Returns a list of DescribeEvaluations that match the search criteria in the request.
default DescribeEvaluationsIterable	describeEvaluationsPaginator()	This is a variant of describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest) operation.
default DescribeEvaluationsIterable	describeEvaluationsPaginator(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest)	This is a variant of describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest) operation.
default DescribeEvaluationsIterable	describeEvaluationsPaginator(DescribeEvaluationsRequest describeEvaluationsRequest)	This is a variant of describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest) operation.
default DescribeMlModelsResponse	describeMLModels()	Returns a list of MLModel that match the search criteria in the request.
default DescribeMlModelsResponse	describeMLModels(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest)	Returns a list of MLModel that match the search criteria in the request.
default DescribeMlModelsResponse	describeMLModels(DescribeMlModelsRequest describeMlModelsRequest)	Returns a list of MLModel that match the search criteria in the request.
default DescribeMLModelsIterable	describeMLModelsPaginator()	This is a variant of describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest) operation.
default DescribeMLModelsIterable	describeMLModelsPaginator(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest)	This is a variant of describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest) operation.
default DescribeMLModelsIterable	describeMLModelsPaginator(DescribeMlModelsRequest describeMlModelsRequest)	This is a variant of describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest) operation.
default DescribeTagsResponse	describeTags(Consumer<DescribeTagsRequest.Builder> describeTagsRequest)	Describes one or more of the tags for your Amazon ML object.
default DescribeTagsResponse	describeTags(DescribeTagsRequest describeTagsRequest)	Describes one or more of the tags for your Amazon ML object.
default GetBatchPredictionResponse	getBatchPrediction(Consumer<GetBatchPredictionRequest.Builder> getBatchPredictionRequest)	Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
default GetBatchPredictionResponse	getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)	Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
default GetDataSourceResponse	getDataSource(Consumer<GetDataSourceRequest.Builder> getDataSourceRequest)	Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.
default GetDataSourceResponse	getDataSource(GetDataSourceRequest getDataSourceRequest)	Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.
default GetEvaluationResponse	getEvaluation(Consumer<GetEvaluationRequest.Builder> getEvaluationRequest)	Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
default GetEvaluationResponse	getEvaluation(GetEvaluationRequest getEvaluationRequest)	Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
default GetMlModelResponse	getMLModel(Consumer<GetMlModelRequest.Builder> getMlModelRequest)	Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.
default GetMlModelResponse	getMLModel(GetMlModelRequest getMlModelRequest)	Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.
default PredictResponse	predict(Consumer<PredictRequest.Builder> predictRequest)	Generates a prediction for the observation using the specified ML Model.
default PredictResponse	predict(PredictRequest predictRequest)	Generates a prediction for the observation using the specified ML Model.
default MachineLearningServiceClientConfiguration	serviceClientConfiguration()	The SDK service client configuration exposes client settings to the user, e.g., ClientOverrideConfiguration
static ServiceMetadata	serviceMetadata()
default UpdateBatchPredictionResponse	updateBatchPrediction(Consumer<UpdateBatchPredictionRequest.Builder> updateBatchPredictionRequest)	Updates the BatchPredictionName of a BatchPrediction.
default UpdateBatchPredictionResponse	updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)	Updates the BatchPredictionName of a BatchPrediction.
default UpdateDataSourceResponse	updateDataSource(Consumer<UpdateDataSourceRequest.Builder> updateDataSourceRequest)	Updates the DataSourceName of a DataSource.
default UpdateDataSourceResponse	updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)	Updates the DataSourceName of a DataSource.
default UpdateEvaluationResponse	updateEvaluation(Consumer<UpdateEvaluationRequest.Builder> updateEvaluationRequest)	Updates the EvaluationName of an Evaluation.
default UpdateEvaluationResponse	updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)	Updates the EvaluationName of an Evaluation.
default UpdateMlModelResponse	updateMLModel(Consumer<UpdateMlModelRequest.Builder> updateMlModelRequest)	Updates the MLModelName and the ScoreThreshold of an MLModel.
default UpdateMlModelResponse	updateMLModel(UpdateMlModelRequest updateMlModelRequest)	Updates the MLModelName and the ScoreThreshold of an MLModel.
default MachineLearningWaiter	waiter()	Create an instance of MachineLearningWaiter using this client.

Methods inherited from interface software.amazon.awssdk.utils.SdkAutoCloseable

close

Methods inherited from interface software.amazon.awssdk.core.SdkClient

serviceName

Field Details

SERVICE_NAME

static final String SERVICE_NAME

See Also:

• Constant Field Values

SERVICE_METADATA_ID

static final String SERVICE_METADATA_ID

Value for looking up the service's metadata from the ServiceMetadataProvider.

See Also:

• Constant Field Values

Method Details

addTags

default AddTagsResponse addTags(AddTagsRequest addTagsRequest)
                         throws InvalidInputException,
InvalidTagException,
TagLimitExceededException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

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, AddTags updates the tag's value.

Parameters:

addTagsRequest -

Returns:

Result of the AddTags operation returned by the service.

addTags

default AddTagsResponse addTags(Consumer<AddTagsRequest.Builder> addTagsRequest)
                         throws InvalidInputException,
InvalidTagException,
TagLimitExceededException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

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, AddTags updates the tag's value.

This is a convenience which creates an instance of the AddTagsRequest.Builder avoiding the need to create one manually via AddTagsRequest.builder()

Parameters:

addTagsRequest - A Consumer that will call methods on AddTagsRequest.Builder to create a request.

Returns:

Result of the AddTags operation returned by the service.

createBatchPrediction

default CreateBatchPredictionResponse createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest)
                                                     throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.

CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

Parameters:

createBatchPredictionRequest -

Returns:

Result of the CreateBatchPrediction operation returned by the service.

createBatchPrediction

default CreateBatchPredictionResponse createBatchPrediction(Consumer<CreateBatchPredictionRequest.Builder> createBatchPredictionRequest)
                                                     throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.

CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

This is a convenience which creates an instance of the CreateBatchPredictionRequest.Builder avoiding the need to create one manually via CreateBatchPredictionRequest.builder()

Parameters:

createBatchPredictionRequest - A Consumer that will call methods on CreateBatchPredictionRequest.Builder to create a request.

Returns:

Result of the CreateBatchPrediction operation returned by the service.

createDataSourceFromRDS

default CreateDataSourceFromRdsResponse createDataSourceFromRDS(CreateDataSourceFromRdsRequest createDataSourceFromRdsRequest)
                                                         throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

Parameters:

createDataSourceFromRdsRequest -

Returns:

Result of the CreateDataSourceFromRDS operation returned by the service.

createDataSourceFromRDS

default CreateDataSourceFromRdsResponse createDataSourceFromRDS(Consumer<CreateDataSourceFromRdsRequest.Builder> createDataSourceFromRdsRequest)
                                                         throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

This is a convenience which creates an instance of the CreateDataSourceFromRdsRequest.Builder avoiding the need to create one manually via CreateDataSourceFromRdsRequest.builder()

Parameters:

createDataSourceFromRdsRequest - A Consumer that will call methods on CreateDataSourceFromRdsRequest.Builder to create a request.

Returns:

Result of the CreateDataSourceFromRDS operation returned by the service.

createDataSourceFromRedshift

default CreateDataSourceFromRedshiftResponse createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
                                                                   throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation.

After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. 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 GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values.

Parameters:

createDataSourceFromRedshiftRequest -

Returns:

Result of the CreateDataSourceFromRedshift operation returned by the service.

createDataSourceFromRedshift

default CreateDataSourceFromRedshiftResponse createDataSourceFromRedshift(Consumer<CreateDataSourceFromRedshiftRequest.Builder> createDataSourceFromRedshiftRequest)
                                                                   throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation.

After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. 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 GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values.

This is a convenience which creates an instance of the CreateDataSourceFromRedshiftRequest.Builder avoiding the need to create one manually via CreateDataSourceFromRedshiftRequest.builder()

Parameters:

createDataSourceFromRedshiftRequest - A Consumer that will call methods on CreateDataSourceFromRedshiftRequest.Builder to create a request.

Returns:

Result of the CreateDataSourceFromRedshift operation returned by the service.

createDataSourceFromS3

default CreateDataSourceFromS3Response createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request)
                                                       throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observation data used in a DataSource 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 DataSource.

After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. 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.

Parameters:

createDataSourceFromS3Request -

Returns:

Result of the CreateDataSourceFromS3 operation returned by the service.

createDataSourceFromS3

default CreateDataSourceFromS3Response createDataSourceFromS3(Consumer<CreateDataSourceFromS3Request.Builder> createDataSourceFromS3Request)
                                                       throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observation data used in a DataSource 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 DataSource.

After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. 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.

This is a convenience which creates an instance of the CreateDataSourceFromS3Request.Builder avoiding the need to create one manually via CreateDataSourceFromS3Request.builder()

Parameters:

createDataSourceFromS3Request - A Consumer that will call methods on CreateDataSourceFromS3Request.Builder to create a request.

Returns:

Result of the CreateDataSourceFromS3 operation returned by the service.

createEvaluation

default CreateEvaluationResponse createEvaluation(CreateEvaluationRequest createEvaluationRequest)
                                           throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.

CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

Parameters:

createEvaluationRequest -

Returns:

Result of the CreateEvaluation operation returned by the service.

createEvaluation

default CreateEvaluationResponse createEvaluation(Consumer<CreateEvaluationRequest.Builder> createEvaluationRequest)
                                           throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.

CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

This is a convenience which creates an instance of the CreateEvaluationRequest.Builder avoiding the need to create one manually via CreateEvaluationRequest.builder()

Parameters:

createEvaluationRequest - A Consumer that will call methods on CreateEvaluationRequest.Builder to create a request.

Returns:

Result of the CreateEvaluation operation returned by the service.

createMLModel

default CreateMlModelResponse createMLModel(CreateMlModelRequest createMlModelRequest)
                                     throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a new MLModel using the DataSource and the recipe as information sources.

An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING . After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

Parameters:

createMlModelRequest -

Returns:

Result of the CreateMLModel operation returned by the service.

createMLModel

default CreateMlModelResponse createMLModel(Consumer<CreateMlModelRequest.Builder> createMlModelRequest)
                                     throws InvalidInputException,
InternalServerException,
IdempotentParameterMismatchException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a new MLModel using the DataSource and the recipe as information sources.

An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING . After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

This is a convenience which creates an instance of the CreateMlModelRequest.Builder avoiding the need to create one manually via CreateMlModelRequest.builder()

Parameters:

createMlModelRequest - A Consumer that will call methods on CreateMlModelRequest.Builder to create a request.

Returns:

Result of the CreateMLModel operation returned by the service.

createRealtimeEndpoint

default CreateRealtimeEndpointResponse createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
                                                       throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.

Parameters:

createRealtimeEndpointRequest -

Returns:

Result of the CreateRealtimeEndpoint operation returned by the service.

createRealtimeEndpoint

default CreateRealtimeEndpointResponse createRealtimeEndpoint(Consumer<CreateRealtimeEndpointRequest.Builder> createRealtimeEndpointRequest)
                                                       throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.

This is a convenience which creates an instance of the CreateRealtimeEndpointRequest.Builder avoiding the need to create one manually via CreateRealtimeEndpointRequest.builder()

Parameters:

createRealtimeEndpointRequest - A Consumer that will call methods on CreateRealtimeEndpointRequest.Builder to create a request.

Returns:

Result of the CreateRealtimeEndpoint operation returned by the service.

deleteBatchPrediction

default DeleteBatchPredictionResponse deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
                                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Assigns the DELETED status to a BatchPrediction, rendering it unusable.

After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.

Caution: The result of the DeleteBatchPrediction operation is irreversible.

Parameters:

deleteBatchPredictionRequest -

Returns:

Result of the DeleteBatchPrediction operation returned by the service.

deleteBatchPrediction

default DeleteBatchPredictionResponse deleteBatchPrediction(Consumer<DeleteBatchPredictionRequest.Builder> deleteBatchPredictionRequest)
                                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Assigns the DELETED status to a BatchPrediction, rendering it unusable.

After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.

Caution: The result of the DeleteBatchPrediction operation is irreversible.

This is a convenience which creates an instance of the DeleteBatchPredictionRequest.Builder avoiding the need to create one manually via DeleteBatchPredictionRequest.builder()

Parameters:

deleteBatchPredictionRequest - A Consumer that will call methods on DeleteBatchPredictionRequest.Builder to create a request.

Returns:

Result of the DeleteBatchPrediction operation returned by the service.

deleteDataSource

default DeleteDataSourceResponse deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest)
                                           throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Assigns the DELETED status to a DataSource, rendering it unusable.

After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.

Caution: The results of the DeleteDataSource operation are irreversible.

Parameters:

deleteDataSourceRequest -

Returns:

Result of the DeleteDataSource operation returned by the service.

deleteDataSource

default DeleteDataSourceResponse deleteDataSource(Consumer<DeleteDataSourceRequest.Builder> deleteDataSourceRequest)
                                           throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Assigns the DELETED status to a DataSource, rendering it unusable.

After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.

Caution: The results of the DeleteDataSource operation are irreversible.

This is a convenience which creates an instance of the DeleteDataSourceRequest.Builder avoiding the need to create one manually via DeleteDataSourceRequest.builder()

Parameters:

deleteDataSourceRequest - A Consumer that will call methods on DeleteDataSourceRequest.Builder to create a request.

Returns:

Result of the DeleteDataSource operation returned by the service.

deleteEvaluation

default DeleteEvaluationResponse deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest)
                                           throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Assigns the DELETED status to an Evaluation, rendering it unusable.

After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.

Caution: The results of the DeleteEvaluation operation are irreversible.

Parameters:

deleteEvaluationRequest -

Returns:

Result of the DeleteEvaluation operation returned by the service.

deleteEvaluation

default DeleteEvaluationResponse deleteEvaluation(Consumer<DeleteEvaluationRequest.Builder> deleteEvaluationRequest)
                                           throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Assigns the DELETED status to an Evaluation, rendering it unusable.

After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.

Caution: The results of the DeleteEvaluation operation are irreversible.

This is a convenience which creates an instance of the DeleteEvaluationRequest.Builder avoiding the need to create one manually via DeleteEvaluationRequest.builder()

Parameters:

deleteEvaluationRequest - A Consumer that will call methods on DeleteEvaluationRequest.Builder to create a request.

Returns:

Result of the DeleteEvaluation operation returned by the service.

deleteMLModel

default DeleteMlModelResponse deleteMLModel(DeleteMlModelRequest deleteMlModelRequest)
                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Assigns the DELETED status to an MLModel, rendering it unusable.

After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

Caution: The result of the DeleteMLModel operation is irreversible.

Parameters:

deleteMlModelRequest -

Returns:

Result of the DeleteMLModel operation returned by the service.

deleteMLModel

default DeleteMlModelResponse deleteMLModel(Consumer<DeleteMlModelRequest.Builder> deleteMlModelRequest)
                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Assigns the DELETED status to an MLModel, rendering it unusable.

After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

Caution: The result of the DeleteMLModel operation is irreversible.

This is a convenience which creates an instance of the DeleteMlModelRequest.Builder avoiding the need to create one manually via DeleteMlModelRequest.builder()

Parameters:

deleteMlModelRequest - A Consumer that will call methods on DeleteMlModelRequest.Builder to create a request.

Returns:

Result of the DeleteMLModel operation returned by the service.

deleteRealtimeEndpoint

default DeleteRealtimeEndpointResponse deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
                                                       throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Deletes a real time endpoint of an MLModel.

Parameters:

deleteRealtimeEndpointRequest -

Returns:

Result of the DeleteRealtimeEndpoint operation returned by the service.

deleteRealtimeEndpoint

default DeleteRealtimeEndpointResponse deleteRealtimeEndpoint(Consumer<DeleteRealtimeEndpointRequest.Builder> deleteRealtimeEndpointRequest)
                                                       throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Deletes a real time endpoint of an MLModel.

This is a convenience which creates an instance of the DeleteRealtimeEndpointRequest.Builder avoiding the need to create one manually via DeleteRealtimeEndpointRequest.builder()

Parameters:

deleteRealtimeEndpointRequest - A Consumer that will call methods on DeleteRealtimeEndpointRequest.Builder to create a request.

Returns:

Result of the DeleteRealtimeEndpoint operation returned by the service.

deleteTags

default DeleteTagsResponse deleteTags(DeleteTagsRequest deleteTagsRequest)
                               throws InvalidInputException,
InvalidTagException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

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.

Parameters:

deleteTagsRequest -

Returns:

Result of the DeleteTags operation returned by the service.

deleteTags

default DeleteTagsResponse deleteTags(Consumer<DeleteTagsRequest.Builder> deleteTagsRequest)
                               throws InvalidInputException,
InvalidTagException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

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.

This is a convenience which creates an instance of the DeleteTagsRequest.Builder avoiding the need to create one manually via DeleteTagsRequest.builder()

Parameters:

deleteTagsRequest - A Consumer that will call methods on DeleteTagsRequest.Builder to create a request.

Returns:

Result of the DeleteTags operation returned by the service.

describeBatchPredictions

default DescribeBatchPredictionsResponse describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
                                                           throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of BatchPrediction operations that match the search criteria in the request.

Parameters:

describeBatchPredictionsRequest -

Returns:

Result of the DescribeBatchPredictions operation returned by the service.

describeBatchPredictions

default DescribeBatchPredictionsResponse describeBatchPredictions(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest)
                                                           throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of BatchPrediction operations that match the search criteria in the request.

This is a convenience which creates an instance of the DescribeBatchPredictionsRequest.Builder avoiding the need to create one manually via DescribeBatchPredictionsRequest.builder()

Parameters:

describeBatchPredictionsRequest - A Consumer that will call methods on DescribeBatchPredictionsRequest.Builder to create a request.

Returns:

Result of the DescribeBatchPredictions operation returned by the service.

describeBatchPredictions

default DescribeBatchPredictionsResponse describeBatchPredictions()
                                                           throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of BatchPrediction operations that match the search criteria in the request.

Returns:

Result of the DescribeBatchPredictions operation returned by the service.

See Also:

• describeBatchPredictions(DescribeBatchPredictionsRequest)

describeBatchPredictionsPaginator

default DescribeBatchPredictionsIterable describeBatchPredictionsPaginator()
                                                                    throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client
             .describeBatchPredictionsPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest) operation.

Returns:

A custom iterable that can be used to iterate through all the response pages.

See Also:

• describeBatchPredictionsPaginator(DescribeBatchPredictionsRequest)

describeBatchPredictionsPaginator

default DescribeBatchPredictionsIterable describeBatchPredictionsPaginator(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
                                                                    throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client
             .describeBatchPredictionsPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest) operation.

Parameters:

describeBatchPredictionsRequest -

Returns:

A custom iterable that can be used to iterate through all the response pages.

describeBatchPredictionsPaginator

default DescribeBatchPredictionsIterable describeBatchPredictionsPaginator(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest)
                                                                    throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client
             .describeBatchPredictionsPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest) operation.

This is a convenience which creates an instance of the DescribeBatchPredictionsRequest.Builder avoiding the need to create one manually via DescribeBatchPredictionsRequest.builder()

Parameters:

describeBatchPredictionsRequest - A Consumer that will call methods on DescribeBatchPredictionsRequest.Builder to create a request.

Returns:

A custom iterable that can be used to iterate through all the response pages.

describeDataSources

default DescribeDataSourcesResponse describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)
                                                 throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of DataSource that match the search criteria in the request.

Parameters:

describeDataSourcesRequest -

Returns:

Result of the DescribeDataSources operation returned by the service.

describeDataSources

default DescribeDataSourcesResponse describeDataSources(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest)
                                                 throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of DataSource that match the search criteria in the request.

This is a convenience which creates an instance of the DescribeDataSourcesRequest.Builder avoiding the need to create one manually via DescribeDataSourcesRequest.builder()

Parameters:

describeDataSourcesRequest - A Consumer that will call methods on DescribeDataSourcesRequest.Builder to create a request.

Returns:

Result of the DescribeDataSources operation returned by the service.

describeDataSources

default DescribeDataSourcesResponse describeDataSources()
                                                 throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of DataSource that match the search criteria in the request.

Returns:

Result of the DescribeDataSources operation returned by the service.

See Also:

• describeDataSources(DescribeDataSourcesRequest)

describeDataSourcesPaginator

default DescribeDataSourcesIterable describeDataSourcesPaginator()
                                                          throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client
             .describeDataSourcesPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest) operation.

Returns:

A custom iterable that can be used to iterate through all the response pages.

See Also:

• describeDataSourcesPaginator(DescribeDataSourcesRequest)

describeDataSourcesPaginator

default DescribeDataSourcesIterable describeDataSourcesPaginator(DescribeDataSourcesRequest describeDataSourcesRequest)
                                                          throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client
             .describeDataSourcesPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest) operation.

Parameters:

describeDataSourcesRequest -

Returns:

A custom iterable that can be used to iterate through all the response pages.

describeDataSourcesPaginator

default DescribeDataSourcesIterable describeDataSourcesPaginator(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest)
                                                          throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client
             .describeDataSourcesPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest) operation.

This is a convenience which creates an instance of the DescribeDataSourcesRequest.Builder avoiding the need to create one manually via DescribeDataSourcesRequest.builder()

Parameters:

describeDataSourcesRequest - A Consumer that will call methods on DescribeDataSourcesRequest.Builder to create a request.

Returns:

A custom iterable that can be used to iterate through all the response pages.

describeEvaluations

default DescribeEvaluationsResponse describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)
                                                 throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of DescribeEvaluations that match the search criteria in the request.

Parameters:

describeEvaluationsRequest -

Returns:

Result of the DescribeEvaluations operation returned by the service.

describeEvaluations

default DescribeEvaluationsResponse describeEvaluations(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest)
                                                 throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of DescribeEvaluations that match the search criteria in the request.

This is a convenience which creates an instance of the DescribeEvaluationsRequest.Builder avoiding the need to create one manually via DescribeEvaluationsRequest.builder()

Parameters:

describeEvaluationsRequest - A Consumer that will call methods on DescribeEvaluationsRequest.Builder to create a request.

Returns:

Result of the DescribeEvaluations operation returned by the service.

describeEvaluations

default DescribeEvaluationsResponse describeEvaluations()
                                                 throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of DescribeEvaluations that match the search criteria in the request.

Returns:

Result of the DescribeEvaluations operation returned by the service.

See Also:

• describeEvaluations(DescribeEvaluationsRequest)

describeEvaluationsPaginator

default DescribeEvaluationsIterable describeEvaluationsPaginator()
                                                          throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client
             .describeEvaluationsPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest) operation.

Returns:

A custom iterable that can be used to iterate through all the response pages.

See Also:

• describeEvaluationsPaginator(DescribeEvaluationsRequest)

describeEvaluationsPaginator

default DescribeEvaluationsIterable describeEvaluationsPaginator(DescribeEvaluationsRequest describeEvaluationsRequest)
                                                          throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client
             .describeEvaluationsPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest) operation.

Parameters:

describeEvaluationsRequest -

Returns:

A custom iterable that can be used to iterate through all the response pages.

describeEvaluationsPaginator

default DescribeEvaluationsIterable describeEvaluationsPaginator(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest)
                                                          throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client
             .describeEvaluationsPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest) operation.

This is a convenience which creates an instance of the DescribeEvaluationsRequest.Builder avoiding the need to create one manually via DescribeEvaluationsRequest.builder()

Parameters:

describeEvaluationsRequest - A Consumer that will call methods on DescribeEvaluationsRequest.Builder to create a request.

Returns:

A custom iterable that can be used to iterate through all the response pages.

describeMLModels

default DescribeMlModelsResponse describeMLModels(DescribeMlModelsRequest describeMlModelsRequest)
                                           throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of MLModel that match the search criteria in the request.

Parameters:

describeMlModelsRequest -

Returns:

Result of the DescribeMLModels operation returned by the service.

describeMLModels

default DescribeMlModelsResponse describeMLModels(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest)
                                           throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of MLModel that match the search criteria in the request.

This is a convenience which creates an instance of the DescribeMlModelsRequest.Builder avoiding the need to create one manually via DescribeMlModelsRequest.builder()

Parameters:

describeMlModelsRequest - A Consumer that will call methods on DescribeMlModelsRequest.Builder to create a request.

Returns:

Result of the DescribeMLModels operation returned by the service.

describeMLModels

default DescribeMlModelsResponse describeMLModels()
                                           throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a list of MLModel that match the search criteria in the request.

Returns:

Result of the DescribeMLModels operation returned by the service.

See Also:

• describeMLModels(DescribeMlModelsRequest)

describeMLModelsPaginator

default DescribeMLModelsIterable describeMLModelsPaginator()
                                                    throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client
             .describeMLModelsPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest) operation.

Returns:

A custom iterable that can be used to iterate through all the response pages.

See Also:

• describeMLModelsPaginator(DescribeMlModelsRequest)

describeMLModelsPaginator

default DescribeMLModelsIterable describeMLModelsPaginator(DescribeMlModelsRequest describeMlModelsRequest)
                                                    throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client
             .describeMLModelsPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest) operation.

Parameters:

describeMlModelsRequest -

Returns:

A custom iterable that can be used to iterate through all the response pages.

describeMLModelsPaginator

default DescribeMLModelsIterable describeMLModelsPaginator(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest)
                                                    throws InvalidInputException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

This is a variant of describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest) operation. The return type is a custom iterable that can be used to iterate through all the pages. SDK will internally handle making service calls for you.

When this operation is called, a custom iterable is returned but no service calls are made yet. So there is no guarantee that the request is valid. As you iterate through the iterable, SDK will start lazily loading response pages by making service calls until there are no pages left or your iteration stops. If there are errors in your request, you will see the failures only after you start iterating through the iterable.

The following are few ways to iterate through the response pages:

1) Using a Stream

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request);
 responses.stream().forEach(....);
 
 

2) Using For loop

 {
     @code
     software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client
             .describeMLModelsPaginator(request);
     for (software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse response : responses) {
         // do something;
     }
 }
 

3) Use iterator directly

 
 software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request);
 responses.iterator().forEachRemaining(....);
 
 

Please notice that the configuration of Limit won't limit the number of results you get with the paginator. It only limits the number of results in each page.

Note: If you prefer to have control on service calls, use the describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest) operation.

This is a convenience which creates an instance of the DescribeMlModelsRequest.Builder avoiding the need to create one manually via DescribeMlModelsRequest.builder()

Parameters:

describeMlModelsRequest - A Consumer that will call methods on DescribeMlModelsRequest.Builder to create a request.

Returns:

A custom iterable that can be used to iterate through all the response pages.

describeTags

default DescribeTagsResponse describeTags(DescribeTagsRequest describeTagsRequest)
                                   throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Describes one or more of the tags for your Amazon ML object.

Parameters:

describeTagsRequest -

Returns:

Result of the DescribeTags operation returned by the service.

describeTags

default DescribeTagsResponse describeTags(Consumer<DescribeTagsRequest.Builder> describeTagsRequest)
                                   throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Describes one or more of the tags for your Amazon ML object.

This is a convenience which creates an instance of the DescribeTagsRequest.Builder avoiding the need to create one manually via DescribeTagsRequest.builder()

Parameters:

describeTagsRequest - A Consumer that will call methods on DescribeTagsRequest.Builder to create a request.

Returns:

Result of the DescribeTags operation returned by the service.

getBatchPrediction

default GetBatchPredictionResponse getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)
                                               throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

Parameters:

getBatchPredictionRequest -

Returns:

Result of the GetBatchPrediction operation returned by the service.

getBatchPrediction

default GetBatchPredictionResponse getBatchPrediction(Consumer<GetBatchPredictionRequest.Builder> getBatchPredictionRequest)
                                               throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

This is a convenience which creates an instance of the GetBatchPredictionRequest.Builder avoiding the need to create one manually via GetBatchPredictionRequest.builder()

Parameters:

getBatchPredictionRequest - A Consumer that will call methods on GetBatchPredictionRequest.Builder to create a request.

Returns:

Result of the GetBatchPrediction operation returned by the service.

getDataSource

default GetDataSourceResponse getDataSource(GetDataSourceRequest getDataSourceRequest)
                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

GetDataSource 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.

Parameters:

getDataSourceRequest -

Returns:

Result of the GetDataSource operation returned by the service.

getDataSource

default GetDataSourceResponse getDataSource(Consumer<GetDataSourceRequest.Builder> getDataSourceRequest)
                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

GetDataSource 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.

This is a convenience which creates an instance of the GetDataSourceRequest.Builder avoiding the need to create one manually via GetDataSourceRequest.builder()

Parameters:

getDataSourceRequest - A Consumer that will call methods on GetDataSourceRequest.Builder to create a request.

Returns:

Result of the GetDataSource operation returned by the service.

getEvaluation

default GetEvaluationResponse getEvaluation(GetEvaluationRequest getEvaluationRequest)
                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

Parameters:

getEvaluationRequest -

Returns:

Result of the GetEvaluation operation returned by the service.

getEvaluation

default GetEvaluationResponse getEvaluation(Consumer<GetEvaluationRequest.Builder> getEvaluationRequest)
                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

This is a convenience which creates an instance of the GetEvaluationRequest.Builder avoiding the need to create one manually via GetEvaluationRequest.builder()

Parameters:

getEvaluationRequest - A Consumer that will call methods on GetEvaluationRequest.Builder to create a request.

Returns:

Result of the GetEvaluation operation returned by the service.

getMLModel

default GetMlModelResponse getMLModel(GetMlModelRequest getMlModelRequest)
                               throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

GetMLModel provides results in normal or verbose format.

Parameters:

getMlModelRequest -

Returns:

Result of the GetMLModel operation returned by the service.

getMLModel

default GetMlModelResponse getMLModel(Consumer<GetMlModelRequest.Builder> getMlModelRequest)
                               throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

GetMLModel provides results in normal or verbose format.

This is a convenience which creates an instance of the GetMlModelRequest.Builder avoiding the need to create one manually via GetMlModelRequest.builder()

Parameters:

getMlModelRequest - A Consumer that will call methods on GetMlModelRequest.Builder to create a request.

Returns:

Result of the GetMLModel operation returned by the service.

predict

default PredictResponse predict(PredictRequest predictRequest)
                         throws InvalidInputException,
ResourceNotFoundException,
LimitExceededException,
InternalServerException,
PredictorNotMountedException,
AwsServiceException,
SdkClientException,
MachineLearningException

Generates a prediction for the observation using the specified ML Model.

Note: Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

Parameters:

predictRequest -

Returns:

Result of the Predict operation returned by the service.

predict

default PredictResponse predict(Consumer<PredictRequest.Builder> predictRequest)
                         throws InvalidInputException,
ResourceNotFoundException,
LimitExceededException,
InternalServerException,
PredictorNotMountedException,
AwsServiceException,
SdkClientException,
MachineLearningException

Generates a prediction for the observation using the specified ML Model.

Note: Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

This is a convenience which creates an instance of the PredictRequest.Builder avoiding the need to create one manually via PredictRequest.builder()

Parameters:

predictRequest - A Consumer that will call methods on PredictRequest.Builder to create a request.

Returns:

Result of the Predict operation returned by the service.

updateBatchPrediction

default UpdateBatchPredictionResponse updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)
                                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

Parameters:

updateBatchPredictionRequest -

Returns:

Result of the UpdateBatchPrediction operation returned by the service.

updateBatchPrediction

default UpdateBatchPredictionResponse updateBatchPrediction(Consumer<UpdateBatchPredictionRequest.Builder> updateBatchPredictionRequest)
                                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

This is a convenience which creates an instance of the UpdateBatchPredictionRequest.Builder avoiding the need to create one manually via UpdateBatchPredictionRequest.builder()

Parameters:

updateBatchPredictionRequest - A Consumer that will call methods on UpdateBatchPredictionRequest.Builder to create a request.

Returns:

Result of the UpdateBatchPrediction operation returned by the service.

updateDataSource

default UpdateDataSourceResponse updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)
                                           throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

Parameters:

updateDataSourceRequest -

Returns:

Result of the UpdateDataSource operation returned by the service.

updateDataSource

default UpdateDataSourceResponse updateDataSource(Consumer<UpdateDataSourceRequest.Builder> updateDataSourceRequest)
                                           throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

This is a convenience which creates an instance of the UpdateDataSourceRequest.Builder avoiding the need to create one manually via UpdateDataSourceRequest.builder()

Parameters:

updateDataSourceRequest - A Consumer that will call methods on UpdateDataSourceRequest.Builder to create a request.

Returns:

Result of the UpdateDataSource operation returned by the service.

updateEvaluation

default UpdateEvaluationResponse updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)
                                           throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

Parameters:

updateEvaluationRequest -

Returns:

Result of the UpdateEvaluation operation returned by the service.

updateEvaluation

default UpdateEvaluationResponse updateEvaluation(Consumer<UpdateEvaluationRequest.Builder> updateEvaluationRequest)
                                           throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

This is a convenience which creates an instance of the UpdateEvaluationRequest.Builder avoiding the need to create one manually via UpdateEvaluationRequest.builder()

Parameters:

updateEvaluationRequest - A Consumer that will call methods on UpdateEvaluationRequest.Builder to create a request.

Returns:

Result of the UpdateEvaluation operation returned by the service.

updateMLModel

default UpdateMlModelResponse updateMLModel(UpdateMlModelRequest updateMlModelRequest)
                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

Parameters:

updateMlModelRequest -

Returns:

Result of the UpdateMLModel operation returned by the service.

updateMLModel

default UpdateMlModelResponse updateMLModel(Consumer<UpdateMlModelRequest.Builder> updateMlModelRequest)
                                     throws InvalidInputException,
ResourceNotFoundException,
InternalServerException,
AwsServiceException,
SdkClientException,
MachineLearningException

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

This is a convenience which creates an instance of the UpdateMlModelRequest.Builder avoiding the need to create one manually via UpdateMlModelRequest.builder()

Parameters:

updateMlModelRequest - A Consumer that will call methods on UpdateMlModelRequest.Builder to create a request.

Returns:

Result of the UpdateMLModel operation returned by the service.

waiter

default MachineLearningWaiter waiter()

Create an instance of MachineLearningWaiter using this client.

Waiters created via this method are managed by the SDK and resources will be released when the service client is closed.

Returns:

an instance of MachineLearningWaiter

create

static MachineLearningClient create()

Create a MachineLearningClient with the region loaded from the DefaultAwsRegionProviderChain and credentials loaded from the DefaultCredentialsProvider.

builder

static MachineLearningClientBuilder builder()

Create a builder that can be used to configure and create a MachineLearningClient.

serviceMetadata

static ServiceMetadata serviceMetadata()

serviceClientConfiguration

default MachineLearningServiceClientConfiguration serviceClientConfiguration()

Description copied from interface: SdkClient

The SDK service client configuration exposes client settings to the user, e.g., ClientOverrideConfiguration

Specified by:

serviceClientConfiguration in interface AwsClient

Specified by:

serviceClientConfiguration in interface SdkClient

Returns:

SdkServiceClientConfiguration