Interface MachineLearningClient
- All Superinterfaces:
AutoCloseable
,AwsClient
,SdkAutoCloseable
,SdkClient
builder()
method.
Definition of the public APIs exposed by Amazon Machine Learning-
Field Summary
Modifier and TypeFieldDescriptionstatic final String
Value for looking up the service's metadata from theServiceMetadataProvider
.static final String
-
Method Summary
Modifier and TypeMethodDescriptiondefault 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 aMachineLearningClient
.static MachineLearningClient
create()
Create aMachineLearningClient
with the region loaded from theDefaultAwsRegionProviderChain
and credentials loaded from theDefaultCredentialsProvider
.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 aDataSource
object from an Amazon Relational Database Service (Amazon RDS).default CreateDataSourceFromRdsResponse
createDataSourceFromRDS
(CreateDataSourceFromRdsRequest createDataSourceFromRdsRequest) Creates aDataSource
object from an Amazon Relational Database Service (Amazon RDS).createDataSourceFromRedshift
(Consumer<CreateDataSourceFromRedshiftRequest.Builder> createDataSourceFromRedshiftRequest) Creates aDataSource
from a database hosted on an Amazon Redshift cluster.createDataSourceFromRedshift
(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest) Creates aDataSource
from a database hosted on an Amazon Redshift cluster.default CreateDataSourceFromS3Response
createDataSourceFromS3
(Consumer<CreateDataSourceFromS3Request.Builder> createDataSourceFromS3Request) Creates aDataSource
object.default CreateDataSourceFromS3Response
createDataSourceFromS3
(CreateDataSourceFromS3Request createDataSourceFromS3Request) Creates aDataSource
object.default CreateEvaluationResponse
createEvaluation
(Consumer<CreateEvaluationRequest.Builder> createEvaluationRequest) Creates a newEvaluation
of anMLModel
.default CreateEvaluationResponse
createEvaluation
(CreateEvaluationRequest createEvaluationRequest) Creates a newEvaluation
of anMLModel
.default CreateMlModelResponse
createMLModel
(Consumer<CreateMlModelRequest.Builder> createMlModelRequest) Creates a newMLModel
using theDataSource
and the recipe as information sources.default CreateMlModelResponse
createMLModel
(CreateMlModelRequest createMlModelRequest) Creates a newMLModel
using theDataSource
and the recipe as information sources.default CreateRealtimeEndpointResponse
createRealtimeEndpoint
(Consumer<CreateRealtimeEndpointRequest.Builder> createRealtimeEndpointRequest) Creates a real-time endpoint for theMLModel
.default CreateRealtimeEndpointResponse
createRealtimeEndpoint
(CreateRealtimeEndpointRequest createRealtimeEndpointRequest) Creates a real-time endpoint for theMLModel
.default DeleteBatchPredictionResponse
deleteBatchPrediction
(Consumer<DeleteBatchPredictionRequest.Builder> deleteBatchPredictionRequest) Assigns the DELETED status to aBatchPrediction
, rendering it unusable.default DeleteBatchPredictionResponse
deleteBatchPrediction
(DeleteBatchPredictionRequest deleteBatchPredictionRequest) Assigns the DELETED status to aBatchPrediction
, rendering it unusable.default DeleteDataSourceResponse
deleteDataSource
(Consumer<DeleteDataSourceRequest.Builder> deleteDataSourceRequest) Assigns the DELETED status to aDataSource
, rendering it unusable.default DeleteDataSourceResponse
deleteDataSource
(DeleteDataSourceRequest deleteDataSourceRequest) Assigns the DELETED status to aDataSource
, rendering it unusable.default DeleteEvaluationResponse
deleteEvaluation
(Consumer<DeleteEvaluationRequest.Builder> deleteEvaluationRequest) Assigns theDELETED
status to anEvaluation
, rendering it unusable.default DeleteEvaluationResponse
deleteEvaluation
(DeleteEvaluationRequest deleteEvaluationRequest) Assigns theDELETED
status to anEvaluation
, rendering it unusable.default DeleteMlModelResponse
deleteMLModel
(Consumer<DeleteMlModelRequest.Builder> deleteMlModelRequest) Assigns theDELETED
status to anMLModel
, rendering it unusable.default DeleteMlModelResponse
deleteMLModel
(DeleteMlModelRequest deleteMlModelRequest) Assigns theDELETED
status to anMLModel
, rendering it unusable.default DeleteRealtimeEndpointResponse
deleteRealtimeEndpoint
(Consumer<DeleteRealtimeEndpointRequest.Builder> deleteRealtimeEndpointRequest) Deletes a real time endpoint of anMLModel
.default DeleteRealtimeEndpointResponse
deleteRealtimeEndpoint
(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest) Deletes a real time endpoint of anMLModel
.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
Returns a list ofBatchPrediction
operations that match the search criteria in the request.default DescribeBatchPredictionsResponse
describeBatchPredictions
(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest) Returns a list ofBatchPrediction
operations that match the search criteria in the request.default DescribeBatchPredictionsResponse
describeBatchPredictions
(DescribeBatchPredictionsRequest describeBatchPredictionsRequest) Returns a list ofBatchPrediction
operations that match the search criteria in the request.default DescribeBatchPredictionsIterable
This is a variant ofdescribeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)
operation.default DescribeBatchPredictionsIterable
describeBatchPredictionsPaginator
(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest) This is a variant ofdescribeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)
operation.default DescribeBatchPredictionsIterable
describeBatchPredictionsPaginator
(DescribeBatchPredictionsRequest describeBatchPredictionsRequest) This is a variant ofdescribeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)
operation.default DescribeDataSourcesResponse
Returns a list ofDataSource
that match the search criteria in the request.default DescribeDataSourcesResponse
describeDataSources
(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest) Returns a list ofDataSource
that match the search criteria in the request.default DescribeDataSourcesResponse
describeDataSources
(DescribeDataSourcesRequest describeDataSourcesRequest) Returns a list ofDataSource
that match the search criteria in the request.default DescribeDataSourcesIterable
This is a variant ofdescribeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)
operation.default DescribeDataSourcesIterable
describeDataSourcesPaginator
(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest) This is a variant ofdescribeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)
operation.default DescribeDataSourcesIterable
describeDataSourcesPaginator
(DescribeDataSourcesRequest describeDataSourcesRequest) This is a variant ofdescribeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)
operation.default DescribeEvaluationsResponse
Returns a list ofDescribeEvaluations
that match the search criteria in the request.default DescribeEvaluationsResponse
describeEvaluations
(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest) Returns a list ofDescribeEvaluations
that match the search criteria in the request.default DescribeEvaluationsResponse
describeEvaluations
(DescribeEvaluationsRequest describeEvaluationsRequest) Returns a list ofDescribeEvaluations
that match the search criteria in the request.default DescribeEvaluationsIterable
This is a variant ofdescribeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)
operation.default DescribeEvaluationsIterable
describeEvaluationsPaginator
(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest) This is a variant ofdescribeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)
operation.default DescribeEvaluationsIterable
describeEvaluationsPaginator
(DescribeEvaluationsRequest describeEvaluationsRequest) This is a variant ofdescribeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)
operation.default DescribeMlModelsResponse
Returns a list ofMLModel
that match the search criteria in the request.default DescribeMlModelsResponse
describeMLModels
(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest) Returns a list ofMLModel
that match the search criteria in the request.default DescribeMlModelsResponse
describeMLModels
(DescribeMlModelsRequest describeMlModelsRequest) Returns a list ofMLModel
that match the search criteria in the request.default DescribeMLModelsIterable
This is a variant ofdescribeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)
operation.default DescribeMLModelsIterable
describeMLModelsPaginator
(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest) This is a variant ofdescribeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)
operation.default DescribeMLModelsIterable
describeMLModelsPaginator
(DescribeMlModelsRequest describeMlModelsRequest) This is a variant ofdescribeMLModels(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 aBatchPrediction
that includes detailed metadata, status, and data file information for aBatch Prediction
request.default GetBatchPredictionResponse
getBatchPrediction
(GetBatchPredictionRequest getBatchPredictionRequest) Returns aBatchPrediction
that includes detailed metadata, status, and data file information for aBatch Prediction
request.default GetDataSourceResponse
getDataSource
(Consumer<GetDataSourceRequest.Builder> getDataSourceRequest) Returns aDataSource
that includes metadata and data file information, as well as the current status of theDataSource
.default GetDataSourceResponse
getDataSource
(GetDataSourceRequest getDataSourceRequest) Returns aDataSource
that includes metadata and data file information, as well as the current status of theDataSource
.default GetEvaluationResponse
getEvaluation
(Consumer<GetEvaluationRequest.Builder> getEvaluationRequest) Returns anEvaluation
that includes metadata as well as the current status of theEvaluation
.default GetEvaluationResponse
getEvaluation
(GetEvaluationRequest getEvaluationRequest) Returns anEvaluation
that includes metadata as well as the current status of theEvaluation
.default GetMlModelResponse
getMLModel
(Consumer<GetMlModelRequest.Builder> getMlModelRequest) Returns anMLModel
that includes detailed metadata, data source information, and the current status of theMLModel
.default GetMlModelResponse
getMLModel
(GetMlModelRequest getMlModelRequest) Returns anMLModel
that includes detailed metadata, data source information, and the current status of theMLModel
.default PredictResponse
predict
(Consumer<PredictRequest.Builder> predictRequest) Generates a prediction for the observation using the specifiedML Model
.default PredictResponse
predict
(PredictRequest predictRequest) Generates a prediction for the observation using the specifiedML Model
.The SDK service client configuration exposes client settings to the user, e.g., ClientOverrideConfigurationstatic ServiceMetadata
default UpdateBatchPredictionResponse
updateBatchPrediction
(Consumer<UpdateBatchPredictionRequest.Builder> updateBatchPredictionRequest) Updates theBatchPredictionName
of aBatchPrediction
.default UpdateBatchPredictionResponse
updateBatchPrediction
(UpdateBatchPredictionRequest updateBatchPredictionRequest) Updates theBatchPredictionName
of aBatchPrediction
.default UpdateDataSourceResponse
updateDataSource
(Consumer<UpdateDataSourceRequest.Builder> updateDataSourceRequest) Updates theDataSourceName
of aDataSource
.default UpdateDataSourceResponse
updateDataSource
(UpdateDataSourceRequest updateDataSourceRequest) Updates theDataSourceName
of aDataSource
.default UpdateEvaluationResponse
updateEvaluation
(Consumer<UpdateEvaluationRequest.Builder> updateEvaluationRequest) Updates theEvaluationName
of anEvaluation
.default UpdateEvaluationResponse
updateEvaluation
(UpdateEvaluationRequest updateEvaluationRequest) Updates theEvaluationName
of anEvaluation
.default UpdateMlModelResponse
updateMLModel
(Consumer<UpdateMlModelRequest.Builder> updateMlModelRequest) Updates theMLModelName
and theScoreThreshold
of anMLModel
.default UpdateMlModelResponse
updateMLModel
(UpdateMlModelRequest updateMlModelRequest) Updates theMLModelName
and theScoreThreshold
of anMLModel
.default MachineLearningWaiter
waiter()
Create an instance ofMachineLearningWaiter
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
- See Also:
-
SERVICE_METADATA_ID
Value for looking up the service's metadata from theServiceMetadataProvider
.- See Also:
-
-
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 viaAddTagsRequest.builder()
- Parameters:
addTagsRequest
- AConsumer
that will call methods onAddTagsRequest.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 newBatchPrediction
, and uses anMLModel
and the data files referenced by theDataSource
as information sources.CreateBatchPrediction
is an asynchronous operation. In response toCreateBatchPrediction
, Amazon Machine Learning (Amazon ML) immediately returns and sets theBatchPrediction
status toPENDING
. After theBatchPrediction
completes, Amazon ML sets the status toCOMPLETED
.You can poll for status updates by using the GetBatchPrediction operation and checking the
Status
parameter of the result. After theCOMPLETED
status appears, the results are available in the location specified by theOutputUri
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 newBatchPrediction
, and uses anMLModel
and the data files referenced by theDataSource
as information sources.CreateBatchPrediction
is an asynchronous operation. In response toCreateBatchPrediction
, Amazon Machine Learning (Amazon ML) immediately returns and sets theBatchPrediction
status toPENDING
. After theBatchPrediction
completes, Amazon ML sets the status toCOMPLETED
.You can poll for status updates by using the GetBatchPrediction operation and checking the
Status
parameter of the result. After theCOMPLETED
status appears, the results are available in the location specified by theOutputUri
parameter.
This is a convenience which creates an instance of the
CreateBatchPredictionRequest.Builder
avoiding the need to create one manually viaCreateBatchPredictionRequest.builder()
- Parameters:
createBatchPredictionRequest
- AConsumer
that will call methods onCreateBatchPredictionRequest.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). ADataSource
references data that can be used to performCreateMLModel
,CreateEvaluation
, orCreateBatchPrediction
operations.CreateDataSourceFromRDS
is an asynchronous operation. In response toCreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
in theCOMPLETED
orPENDING
state can be used only to perform>CreateMLModel
>,CreateEvaluation
, orCreateBatchPrediction
operations.If Amazon ML cannot accept the input source, it sets the
Status
parameter toFAILED
and includes an error message in theMessage
attribute of theGetDataSource
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). ADataSource
references data that can be used to performCreateMLModel
,CreateEvaluation
, orCreateBatchPrediction
operations.CreateDataSourceFromRDS
is an asynchronous operation. In response toCreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
in theCOMPLETED
orPENDING
state can be used only to perform>CreateMLModel
>,CreateEvaluation
, orCreateBatchPrediction
operations.If Amazon ML cannot accept the input source, it sets the
Status
parameter toFAILED
and includes an error message in theMessage
attribute of theGetDataSource
operation response.
This is a convenience which creates an instance of the
CreateDataSourceFromRdsRequest.Builder
avoiding the need to create one manually viaCreateDataSourceFromRdsRequest.builder()
- Parameters:
createDataSourceFromRdsRequest
- AConsumer
that will call methods onCreateDataSourceFromRdsRequest.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. ADataSource
references data that can be used to perform eitherCreateMLModel
,CreateEvaluation
, orCreateBatchPrediction
operations.CreateDataSourceFromRedshift
is an asynchronous operation. In response toCreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
inCOMPLETED
orPENDING
states can be used to perform onlyCreateMLModel
,CreateEvaluation
, orCreateBatchPrediction
operations.If Amazon ML can't accept the input source, it sets the
Status
parameter toFAILED
and includes an error message in theMessage
attribute of theGetDataSource
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 anUnload
command in Amazon Redshift to transfer the result set of theSelectSqlQuery
query toS3StagingLocation
.After the
DataSource
has been created, it's ready for use in evaluations and batch predictions. If you plan to use theDataSource
to train anMLModel
, theDataSource
also requires a recipe. A recipe describes how each input variable will be used in training anMLModel
. 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 aCreateDataSource
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. ADataSource
references data that can be used to perform eitherCreateMLModel
,CreateEvaluation
, orCreateBatchPrediction
operations.CreateDataSourceFromRedshift
is an asynchronous operation. In response toCreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
is created and ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
inCOMPLETED
orPENDING
states can be used to perform onlyCreateMLModel
,CreateEvaluation
, orCreateBatchPrediction
operations.If Amazon ML can't accept the input source, it sets the
Status
parameter toFAILED
and includes an error message in theMessage
attribute of theGetDataSource
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 anUnload
command in Amazon Redshift to transfer the result set of theSelectSqlQuery
query toS3StagingLocation
.After the
DataSource
has been created, it's ready for use in evaluations and batch predictions. If you plan to use theDataSource
to train anMLModel
, theDataSource
also requires a recipe. A recipe describes how each input variable will be used in training anMLModel
. 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 aCreateDataSource
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 viaCreateDataSourceFromRedshiftRequest.builder()
- Parameters:
createDataSourceFromRedshiftRequest
- AConsumer
that will call methods onCreateDataSourceFromRedshiftRequest.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. ADataSource
references data that can be used to performCreateMLModel
,CreateEvaluation
, orCreateBatchPrediction
operations.CreateDataSourceFromS3
is an asynchronous operation. In response toCreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
has been created and is ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
in theCOMPLETED
orPENDING
state can be used to perform onlyCreateMLModel
,CreateEvaluation
orCreateBatchPrediction
operations.If Amazon ML can't accept the input source, it sets the
Status
parameter toFAILED
and includes an error message in theMessage
attribute of theGetDataSource
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 theDataSource
.After the
DataSource
has been created, it's ready to use in evaluations and batch predictions. If you plan to use theDataSource
to train anMLModel
, theDataSource
also needs a recipe. A recipe describes how each input variable will be used in training anMLModel
. 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. ADataSource
references data that can be used to performCreateMLModel
,CreateEvaluation
, orCreateBatchPrediction
operations.CreateDataSourceFromS3
is an asynchronous operation. In response toCreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets theDataSource
status toPENDING
. After theDataSource
has been created and is ready for use, Amazon ML sets theStatus
parameter toCOMPLETED
.DataSource
in theCOMPLETED
orPENDING
state can be used to perform onlyCreateMLModel
,CreateEvaluation
orCreateBatchPrediction
operations.If Amazon ML can't accept the input source, it sets the
Status
parameter toFAILED
and includes an error message in theMessage
attribute of theGetDataSource
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 theDataSource
.After the
DataSource
has been created, it's ready to use in evaluations and batch predictions. If you plan to use theDataSource
to train anMLModel
, theDataSource
also needs a recipe. A recipe describes how each input variable will be used in training anMLModel
. 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 viaCreateDataSourceFromS3Request.builder()
- Parameters:
createDataSourceFromS3Request
- AConsumer
that will call methods onCreateDataSourceFromS3Request.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 anMLModel
. AnMLModel
is evaluated on a set of observations associated to aDataSource
. Like aDataSource
for anMLModel
, theDataSource
for anEvaluation
contains values for theTarget Variable
. TheEvaluation
compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective theMLModel
functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the correspondingMLModelType
:BINARY
,REGRESSION
orMULTICLASS
.CreateEvaluation
is an asynchronous operation. In response toCreateEvaluation
, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status toPENDING
. After theEvaluation
is created and ready for use, Amazon ML sets the status toCOMPLETED
.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 anMLModel
. AnMLModel
is evaluated on a set of observations associated to aDataSource
. Like aDataSource
for anMLModel
, theDataSource
for anEvaluation
contains values for theTarget Variable
. TheEvaluation
compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective theMLModel
functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the correspondingMLModelType
:BINARY
,REGRESSION
orMULTICLASS
.CreateEvaluation
is an asynchronous operation. In response toCreateEvaluation
, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status toPENDING
. After theEvaluation
is created and ready for use, Amazon ML sets the status toCOMPLETED
.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 viaCreateEvaluationRequest.builder()
- Parameters:
createEvaluationRequest
- AConsumer
that will call methods onCreateEvaluationRequest.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 theDataSource
and the recipe as information sources.An
MLModel
is nearly immutable. Users can update only theMLModelName
and theScoreThreshold
in anMLModel
without creating a newMLModel
.CreateMLModel
is an asynchronous operation. In response toCreateMLModel
, Amazon Machine Learning (Amazon ML) immediately returns and sets theMLModel
status toPENDING
. After theMLModel
has been created and ready is for use, Amazon ML sets the status toCOMPLETED
.You can use the
GetMLModel
operation to check the progress of theMLModel
during the creation operation.CreateMLModel
requires aDataSource
with computed statistics, which can be created by settingComputeStatistics
totrue
inCreateDataSourceFromRDS
,CreateDataSourceFromS3
, orCreateDataSourceFromRedshift
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 theDataSource
and the recipe as information sources.An
MLModel
is nearly immutable. Users can update only theMLModelName
and theScoreThreshold
in anMLModel
without creating a newMLModel
.CreateMLModel
is an asynchronous operation. In response toCreateMLModel
, Amazon Machine Learning (Amazon ML) immediately returns and sets theMLModel
status toPENDING
. After theMLModel
has been created and ready is for use, Amazon ML sets the status toCOMPLETED
.You can use the
GetMLModel
operation to check the progress of theMLModel
during the creation operation.CreateMLModel
requires aDataSource
with computed statistics, which can be created by settingComputeStatistics
totrue
inCreateDataSourceFromRDS
,CreateDataSourceFromS3
, orCreateDataSourceFromRedshift
operations.
This is a convenience which creates an instance of the
CreateMlModelRequest.Builder
avoiding the need to create one manually viaCreateMlModelRequest.builder()
- Parameters:
createMlModelRequest
- AConsumer
that will call methods onCreateMlModelRequest.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 theMLModel
; that is, the location to send real-time prediction requests for the specifiedMLModel
.- 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 theMLModel
; that is, the location to send real-time prediction requests for the specifiedMLModel
.
This is a convenience which creates an instance of the
CreateRealtimeEndpointRequest.Builder
avoiding the need to create one manually viaCreateRealtimeEndpointRequest.builder()
- Parameters:
createRealtimeEndpointRequest
- AConsumer
that will call methods onCreateRealtimeEndpointRequest.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 theBatchPrediction
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 theBatchPrediction
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 viaDeleteBatchPredictionRequest.builder()
- Parameters:
deleteBatchPredictionRequest
- AConsumer
that will call methods onDeleteBatchPredictionRequest.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 theDataSource
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 theDataSource
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 viaDeleteDataSourceRequest.builder()
- Parameters:
deleteDataSourceRequest
- AConsumer
that will call methods onDeleteDataSourceRequest.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 anEvaluation
, rendering it unusable.After invoking the
DeleteEvaluation
operation, you can use theGetEvaluation
operation to verify that the status of theEvaluation
changed toDELETED
.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 anEvaluation
, rendering it unusable.After invoking the
DeleteEvaluation
operation, you can use theGetEvaluation
operation to verify that the status of theEvaluation
changed toDELETED
.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 viaDeleteEvaluationRequest.builder()
- Parameters:
deleteEvaluationRequest
- AConsumer
that will call methods onDeleteEvaluationRequest.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 anMLModel
, rendering it unusable.After using the
DeleteMLModel
operation, you can use theGetMLModel
operation to verify that the status of theMLModel
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 anMLModel
, rendering it unusable.After using the
DeleteMLModel
operation, you can use theGetMLModel
operation to verify that the status of theMLModel
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 viaDeleteMlModelRequest.builder()
- Parameters:
deleteMlModelRequest
- AConsumer
that will call methods onDeleteMlModelRequest.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 viaDeleteRealtimeEndpointRequest.builder()
- Parameters:
deleteRealtimeEndpointRequest
- AConsumer
that will call methods onDeleteRealtimeEndpointRequest.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 viaDeleteTagsRequest.builder()
- Parameters:
deleteTagsRequest
- AConsumer
that will call methods onDeleteTagsRequest.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 viaDescribeBatchPredictionsRequest.builder()
- Parameters:
describeBatchPredictionsRequest
- AConsumer
that will call methods onDescribeBatchPredictionsRequest.Builder
to create a request.- Returns:
- Result of the DescribeBatchPredictions operation returned by the service.
-
describeBatchPredictions
default DescribeBatchPredictionsResponse describeBatchPredictions() throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningExceptionReturns 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:
-
describeBatchPredictionsPaginator
default DescribeBatchPredictionsIterable describeBatchPredictionsPaginator() throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningExceptionThis 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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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
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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsIterable responses = client.describeBatchPredictionsPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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 viaDescribeBatchPredictionsRequest.builder()
- Parameters:
describeBatchPredictionsRequest
- AConsumer
that will call methods onDescribeBatchPredictionsRequest.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 viaDescribeDataSourcesRequest.builder()
- Parameters:
describeDataSourcesRequest
- AConsumer
that will call methods onDescribeDataSourcesRequest.Builder
to create a request.- Returns:
- Result of the DescribeDataSources operation returned by the service.
-
describeDataSources
default DescribeDataSourcesResponse describeDataSources() throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningExceptionReturns a list of
DataSource
that match the search criteria in the request.- Returns:
- Result of the DescribeDataSources operation returned by the service.
- See Also:
-
describeDataSourcesPaginator
default DescribeDataSourcesIterable describeDataSourcesPaginator() throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningExceptionThis 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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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
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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesIterable responses = client.describeDataSourcesPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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 viaDescribeDataSourcesRequest.builder()
- Parameters:
describeDataSourcesRequest
- AConsumer
that will call methods onDescribeDataSourcesRequest.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 viaDescribeEvaluationsRequest.builder()
- Parameters:
describeEvaluationsRequest
- AConsumer
that will call methods onDescribeEvaluationsRequest.Builder
to create a request.- Returns:
- Result of the DescribeEvaluations operation returned by the service.
-
describeEvaluations
default DescribeEvaluationsResponse describeEvaluations() throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningExceptionReturns a list of
DescribeEvaluations
that match the search criteria in the request.- Returns:
- Result of the DescribeEvaluations operation returned by the service.
- See Also:
-
describeEvaluationsPaginator
default DescribeEvaluationsIterable describeEvaluationsPaginator() throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningExceptionThis 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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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
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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsIterable responses = client.describeEvaluationsPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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 viaDescribeEvaluationsRequest.builder()
- Parameters:
describeEvaluationsRequest
- AConsumer
that will call methods onDescribeEvaluationsRequest.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 viaDescribeMlModelsRequest.builder()
- Parameters:
describeMlModelsRequest
- AConsumer
that will call methods onDescribeMlModelsRequest.Builder
to create a request.- Returns:
- Result of the DescribeMLModels operation returned by the service.
-
describeMLModels
default DescribeMlModelsResponse describeMLModels() throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningExceptionReturns a list of
MLModel
that match the search criteria in the request.- Returns:
- Result of the DescribeMLModels operation returned by the service.
- See Also:
-
describeMLModelsPaginator
default DescribeMLModelsIterable describeMLModelsPaginator() throws InvalidInputException, InternalServerException, AwsServiceException, SdkClientException, MachineLearningExceptionThis 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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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
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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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 Streamsoftware.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsIterable responses = client.describeMLModelsPaginator(request); responses.stream().forEach(....);
{ @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 directlysoftware.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 viaDescribeMlModelsRequest.builder()
- Parameters:
describeMlModelsRequest
- AConsumer
that will call methods onDescribeMlModelsRequest.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 viaDescribeTagsRequest.builder()
- Parameters:
describeTagsRequest
- AConsumer
that will call methods onDescribeTagsRequest.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 aBatch 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 aBatch Prediction
request.
This is a convenience which creates an instance of the
GetBatchPredictionRequest.Builder
avoiding the need to create one manually viaGetBatchPredictionRequest.builder()
- Parameters:
getBatchPredictionRequest
- AConsumer
that will call methods onGetBatchPredictionRequest.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 theDataSource
.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 theDataSource
.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 viaGetDataSourceRequest.builder()
- Parameters:
getDataSourceRequest
- AConsumer
that will call methods onGetDataSourceRequest.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 theEvaluation
.- 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 theEvaluation
.
This is a convenience which creates an instance of the
GetEvaluationRequest.Builder
avoiding the need to create one manually viaGetEvaluationRequest.builder()
- Parameters:
getEvaluationRequest
- AConsumer
that will call methods onGetEvaluationRequest.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 theMLModel
.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 theMLModel
.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 viaGetMlModelRequest.builder()
- Parameters:
getMlModelRequest
- AConsumer
that will call methods onGetMlModelRequest.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 viaPredictRequest.builder()
- Parameters:
predictRequest
- AConsumer
that will call methods onPredictRequest.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 aBatchPrediction
.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 aBatchPrediction
.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 viaUpdateBatchPredictionRequest.builder()
- Parameters:
updateBatchPredictionRequest
- AConsumer
that will call methods onUpdateBatchPredictionRequest.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 aDataSource
.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 aDataSource
.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 viaUpdateDataSourceRequest.builder()
- Parameters:
updateDataSourceRequest
- AConsumer
that will call methods onUpdateDataSourceRequest.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 anEvaluation
.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 anEvaluation
.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 viaUpdateEvaluationRequest.builder()
- Parameters:
updateEvaluationRequest
- AConsumer
that will call methods onUpdateEvaluationRequest.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 theScoreThreshold
of anMLModel
.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 theScoreThreshold
of anMLModel
.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 viaUpdateMlModelRequest.builder()
- Parameters:
updateMlModelRequest
- AConsumer
that will call methods onUpdateMlModelRequest.Builder
to create a request.- Returns:
- Result of the UpdateMLModel operation returned by the service.
-
waiter
Create an instance ofMachineLearningWaiter
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
Create aMachineLearningClient
with the region loaded from theDefaultAwsRegionProviderChain
and credentials loaded from theDefaultCredentialsProvider
. -
builder
Create a builder that can be used to configure and create aMachineLearningClient
. -
serviceMetadata
-
serviceClientConfiguration
Description copied from interface:SdkClient
The SDK service client configuration exposes client settings to the user, e.g., ClientOverrideConfiguration- Specified by:
serviceClientConfiguration
in interfaceAwsClient
- Specified by:
serviceClientConfiguration
in interfaceSdkClient
- Returns:
- SdkServiceClientConfiguration
-