@Generated(value="software.amazon.awssdk:codegen") public interface MachineLearningAsyncClient extends SdkClient
builder()
method.
Definition of the public APIs exposed by Amazon Machine LearningModifier and Type | Field and Description |
---|---|
static String |
SERVICE_NAME |
Modifier and Type | Method and Description |
---|---|
default CompletableFuture<AddTagsResponse> |
addTags(AddTagsRequest addTagsRequest)
Adds one or more tags to an object, up to a limit of 10.
|
default CompletableFuture<AddTagsResponse> |
addTags(Consumer<AddTagsRequest.Builder> addTagsRequest)
Adds one or more tags to an object, up to a limit of 10.
|
static MachineLearningAsyncClientBuilder |
builder()
Create a builder that can be used to configure and create a
MachineLearningAsyncClient . |
static MachineLearningAsyncClient |
create()
Create a
MachineLearningAsyncClient with the region loaded from the
DefaultAwsRegionProviderChain and credentials loaded from the
DefaultCredentialsProvider . |
default CompletableFuture<CreateBatchPredictionResponse> |
createBatchPrediction(Consumer<CreateBatchPredictionRequest.Builder> createBatchPredictionRequest)
Generates predictions for a group of observations.
|
default CompletableFuture<CreateBatchPredictionResponse> |
createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest)
Generates predictions for a group of observations.
|
default CompletableFuture<CreateDataSourceFromRdsResponse> |
createDataSourceFromRDS(Consumer<CreateDataSourceFromRdsRequest.Builder> createDataSourceFromRdsRequest)
Creates a
DataSource object from an Amazon Relational Database
Service (Amazon RDS). |
default CompletableFuture<CreateDataSourceFromRdsResponse> |
createDataSourceFromRDS(CreateDataSourceFromRdsRequest createDataSourceFromRdsRequest)
Creates a
DataSource object from an Amazon Relational Database
Service (Amazon RDS). |
default CompletableFuture<CreateDataSourceFromRedshiftResponse> |
createDataSourceFromRedshift(Consumer<CreateDataSourceFromRedshiftRequest.Builder> createDataSourceFromRedshiftRequest)
Creates a
DataSource from a database hosted on an Amazon Redshift cluster. |
default CompletableFuture<CreateDataSourceFromRedshiftResponse> |
createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
Creates a
DataSource from a database hosted on an Amazon Redshift cluster. |
default CompletableFuture<CreateDataSourceFromS3Response> |
createDataSourceFromS3(Consumer<CreateDataSourceFromS3Request.Builder> createDataSourceFromS3Request)
Creates a
DataSource object. |
default CompletableFuture<CreateDataSourceFromS3Response> |
createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request)
Creates a
DataSource object. |
default CompletableFuture<CreateEvaluationResponse> |
createEvaluation(Consumer<CreateEvaluationRequest.Builder> createEvaluationRequest)
Creates a new
Evaluation of an MLModel . |
default CompletableFuture<CreateEvaluationResponse> |
createEvaluation(CreateEvaluationRequest createEvaluationRequest)
Creates a new
Evaluation of an MLModel . |
default CompletableFuture<CreateMlModelResponse> |
createMLModel(Consumer<CreateMlModelRequest.Builder> createMlModelRequest)
Creates a new
MLModel using the DataSource and the recipe as information sources. |
default CompletableFuture<CreateMlModelResponse> |
createMLModel(CreateMlModelRequest createMlModelRequest)
Creates a new
MLModel using the DataSource and the recipe as information sources. |
default CompletableFuture<CreateRealtimeEndpointResponse> |
createRealtimeEndpoint(Consumer<CreateRealtimeEndpointRequest.Builder> createRealtimeEndpointRequest)
Creates a real-time endpoint for the
MLModel . |
default CompletableFuture<CreateRealtimeEndpointResponse> |
createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
Creates a real-time endpoint for the
MLModel . |
default CompletableFuture<DeleteBatchPredictionResponse> |
deleteBatchPrediction(Consumer<DeleteBatchPredictionRequest.Builder> deleteBatchPredictionRequest)
Assigns the DELETED status to a
BatchPrediction , rendering it unusable. |
default CompletableFuture<DeleteBatchPredictionResponse> |
deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
Assigns the DELETED status to a
BatchPrediction , rendering it unusable. |
default CompletableFuture<DeleteDataSourceResponse> |
deleteDataSource(Consumer<DeleteDataSourceRequest.Builder> deleteDataSourceRequest)
Assigns the DELETED status to a
DataSource , rendering it unusable. |
default CompletableFuture<DeleteDataSourceResponse> |
deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest)
Assigns the DELETED status to a
DataSource , rendering it unusable. |
default CompletableFuture<DeleteEvaluationResponse> |
deleteEvaluation(Consumer<DeleteEvaluationRequest.Builder> deleteEvaluationRequest)
Assigns the
DELETED status to an Evaluation , rendering it unusable. |
default CompletableFuture<DeleteEvaluationResponse> |
deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest)
Assigns the
DELETED status to an Evaluation , rendering it unusable. |
default CompletableFuture<DeleteMlModelResponse> |
deleteMLModel(Consumer<DeleteMlModelRequest.Builder> deleteMlModelRequest)
Assigns the
DELETED status to an MLModel , rendering it unusable. |
default CompletableFuture<DeleteMlModelResponse> |
deleteMLModel(DeleteMlModelRequest deleteMlModelRequest)
Assigns the
DELETED status to an MLModel , rendering it unusable. |
default CompletableFuture<DeleteRealtimeEndpointResponse> |
deleteRealtimeEndpoint(Consumer<DeleteRealtimeEndpointRequest.Builder> deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an
MLModel . |
default CompletableFuture<DeleteRealtimeEndpointResponse> |
deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an
MLModel . |
default CompletableFuture<DeleteTagsResponse> |
deleteTags(Consumer<DeleteTagsRequest.Builder> deleteTagsRequest)
Deletes the specified tags associated with an ML object.
|
default CompletableFuture<DeleteTagsResponse> |
deleteTags(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags associated with an ML object.
|
default CompletableFuture<DescribeBatchPredictionsResponse> |
describeBatchPredictions()
Returns a list of
BatchPrediction operations that match the search criteria in the request. |
default CompletableFuture<DescribeBatchPredictionsResponse> |
describeBatchPredictions(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest)
Returns a list of
BatchPrediction operations that match the search criteria in the request. |
default CompletableFuture<DescribeBatchPredictionsResponse> |
describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of
BatchPrediction operations that match the search criteria in the request. |
default DescribeBatchPredictionsPublisher |
describeBatchPredictionsPaginator()
Returns a list of
BatchPrediction operations that match the search criteria in the request. |
default DescribeBatchPredictionsPublisher |
describeBatchPredictionsPaginator(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest)
Returns a list of
BatchPrediction operations that match the search criteria in the request. |
default DescribeBatchPredictionsPublisher |
describeBatchPredictionsPaginator(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of
BatchPrediction operations that match the search criteria in the request. |
default CompletableFuture<DescribeDataSourcesResponse> |
describeDataSources()
Returns a list of
DataSource that match the search criteria in the request. |
default CompletableFuture<DescribeDataSourcesResponse> |
describeDataSources(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest)
Returns a list of
DataSource that match the search criteria in the request. |
default CompletableFuture<DescribeDataSourcesResponse> |
describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of
DataSource that match the search criteria in the request. |
default DescribeDataSourcesPublisher |
describeDataSourcesPaginator()
Returns a list of
DataSource that match the search criteria in the request. |
default DescribeDataSourcesPublisher |
describeDataSourcesPaginator(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest)
Returns a list of
DataSource that match the search criteria in the request. |
default DescribeDataSourcesPublisher |
describeDataSourcesPaginator(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of
DataSource that match the search criteria in the request. |
default CompletableFuture<DescribeEvaluationsResponse> |
describeEvaluations()
Returns a list of
DescribeEvaluations that match the search criteria in the request. |
default CompletableFuture<DescribeEvaluationsResponse> |
describeEvaluations(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest)
Returns a list of
DescribeEvaluations that match the search criteria in the request. |
default CompletableFuture<DescribeEvaluationsResponse> |
describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of
DescribeEvaluations that match the search criteria in the request. |
default DescribeEvaluationsPublisher |
describeEvaluationsPaginator()
Returns a list of
DescribeEvaluations that match the search criteria in the request. |
default DescribeEvaluationsPublisher |
describeEvaluationsPaginator(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest)
Returns a list of
DescribeEvaluations that match the search criteria in the request. |
default DescribeEvaluationsPublisher |
describeEvaluationsPaginator(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of
DescribeEvaluations that match the search criteria in the request. |
default CompletableFuture<DescribeMlModelsResponse> |
describeMLModels()
Returns a list of
MLModel that match the search criteria in the request. |
default CompletableFuture<DescribeMlModelsResponse> |
describeMLModels(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest)
Returns a list of
MLModel that match the search criteria in the request. |
default CompletableFuture<DescribeMlModelsResponse> |
describeMLModels(DescribeMlModelsRequest describeMlModelsRequest)
Returns a list of
MLModel that match the search criteria in the request. |
default DescribeMLModelsPublisher |
describeMLModelsPaginator()
Returns a list of
MLModel that match the search criteria in the request. |
default DescribeMLModelsPublisher |
describeMLModelsPaginator(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest)
Returns a list of
MLModel that match the search criteria in the request. |
default DescribeMLModelsPublisher |
describeMLModelsPaginator(DescribeMlModelsRequest describeMlModelsRequest)
Returns a list of
MLModel that match the search criteria in the request. |
default CompletableFuture<DescribeTagsResponse> |
describeTags(Consumer<DescribeTagsRequest.Builder> describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
|
default CompletableFuture<DescribeTagsResponse> |
describeTags(DescribeTagsRequest describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
|
default CompletableFuture<GetBatchPredictionResponse> |
getBatchPrediction(Consumer<GetBatchPredictionRequest.Builder> getBatchPredictionRequest)
Returns a
BatchPrediction that includes detailed metadata, status, and data file information for a
Batch Prediction request. |
default CompletableFuture<GetBatchPredictionResponse> |
getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)
Returns a
BatchPrediction that includes detailed metadata, status, and data file information for a
Batch Prediction request. |
default CompletableFuture<GetDataSourceResponse> |
getDataSource(Consumer<GetDataSourceRequest.Builder> getDataSourceRequest)
Returns a
DataSource that includes metadata and data file information, as well as the current status
of the DataSource . |
default CompletableFuture<GetDataSourceResponse> |
getDataSource(GetDataSourceRequest getDataSourceRequest)
Returns a
DataSource that includes metadata and data file information, as well as the current status
of the DataSource . |
default CompletableFuture<GetEvaluationResponse> |
getEvaluation(Consumer<GetEvaluationRequest.Builder> getEvaluationRequest)
Returns an
Evaluation that includes metadata as well as the current status of the
Evaluation . |
default CompletableFuture<GetEvaluationResponse> |
getEvaluation(GetEvaluationRequest getEvaluationRequest)
Returns an
Evaluation that includes metadata as well as the current status of the
Evaluation . |
default CompletableFuture<GetMlModelResponse> |
getMLModel(Consumer<GetMlModelRequest.Builder> getMlModelRequest)
Returns an
MLModel that includes detailed metadata, data source information, and the current status
of the MLModel . |
default CompletableFuture<GetMlModelResponse> |
getMLModel(GetMlModelRequest getMlModelRequest)
Returns an
MLModel that includes detailed metadata, data source information, and the current status
of the MLModel . |
default CompletableFuture<PredictResponse> |
predict(Consumer<PredictRequest.Builder> predictRequest)
Generates a prediction for the observation using the specified
ML Model . |
default CompletableFuture<PredictResponse> |
predict(PredictRequest predictRequest)
Generates a prediction for the observation using the specified
ML Model . |
default CompletableFuture<UpdateBatchPredictionResponse> |
updateBatchPrediction(Consumer<UpdateBatchPredictionRequest.Builder> updateBatchPredictionRequest)
Updates the
BatchPredictionName of a BatchPrediction . |
default CompletableFuture<UpdateBatchPredictionResponse> |
updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)
Updates the
BatchPredictionName of a BatchPrediction . |
default CompletableFuture<UpdateDataSourceResponse> |
updateDataSource(Consumer<UpdateDataSourceRequest.Builder> updateDataSourceRequest)
Updates the
DataSourceName of a DataSource . |
default CompletableFuture<UpdateDataSourceResponse> |
updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)
Updates the
DataSourceName of a DataSource . |
default CompletableFuture<UpdateEvaluationResponse> |
updateEvaluation(Consumer<UpdateEvaluationRequest.Builder> updateEvaluationRequest)
Updates the
EvaluationName of an Evaluation . |
default CompletableFuture<UpdateEvaluationResponse> |
updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)
Updates the
EvaluationName of an Evaluation . |
default CompletableFuture<UpdateMlModelResponse> |
updateMLModel(Consumer<UpdateMlModelRequest.Builder> updateMlModelRequest)
Updates the
MLModelName and the ScoreThreshold of an MLModel . |
default CompletableFuture<UpdateMlModelResponse> |
updateMLModel(UpdateMlModelRequest updateMlModelRequest)
Updates the
MLModelName and the ScoreThreshold of an MLModel . |
serviceName
close
static final String SERVICE_NAME
static MachineLearningAsyncClient create()
MachineLearningAsyncClient
with the region loaded from the
DefaultAwsRegionProviderChain
and credentials loaded from the
DefaultCredentialsProvider
.static MachineLearningAsyncClientBuilder builder()
MachineLearningAsyncClient
.default CompletableFuture<AddTagsResponse> addTags(AddTagsRequest addTagsRequest)
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.
addTagsRequest
- default CompletableFuture<AddTagsResponse> addTags(Consumer<AddTagsRequest.Builder> addTagsRequest)
Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you
add a tag using a key that is already associated with the ML object, AddTags
updates the tag's
value.
This is a convenience which creates an instance of the AddTagsRequest.Builder
avoiding the need to create
one manually via AddTagsRequest.builder()
addTagsRequest
- A Consumer
that will call methods on AddTagsInput.Builder
to create a request.default CompletableFuture<CreateBatchPredictionResponse> createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest)
Generates predictions for a group of observations. The observations to process exist in one or more data files
referenced by a DataSource
. This operation creates a new BatchPrediction
, and uses an
MLModel
and the data files referenced by the DataSource
as information sources.
CreateBatchPrediction
is an asynchronous operation. In response to
CreateBatchPrediction
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
BatchPrediction
status to PENDING
. After the BatchPrediction
completes,
Amazon ML sets the status to COMPLETED
.
You can poll for status updates by using the GetBatchPrediction operation and checking the
Status
parameter of the result. After the COMPLETED
status appears, the results are
available in the location specified by the OutputUri
parameter.
createBatchPredictionRequest
- default CompletableFuture<CreateBatchPredictionResponse> createBatchPrediction(Consumer<CreateBatchPredictionRequest.Builder> createBatchPredictionRequest)
Generates predictions for a group of observations. The observations to process exist in one or more data files
referenced by a DataSource
. This operation creates a new BatchPrediction
, and uses an
MLModel
and the data files referenced by the DataSource
as information sources.
CreateBatchPrediction
is an asynchronous operation. In response to
CreateBatchPrediction
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
BatchPrediction
status to PENDING
. After the BatchPrediction
completes,
Amazon ML sets the status to COMPLETED
.
You can poll for status updates by using the GetBatchPrediction operation and checking the
Status
parameter of the result. After the COMPLETED
status appears, the results are
available in the location specified by the OutputUri
parameter.
This is a convenience which creates an instance of the CreateBatchPredictionRequest.Builder
avoiding the
need to create one manually via CreateBatchPredictionRequest.builder()
createBatchPredictionRequest
- A Consumer
that will call methods on CreateBatchPredictionInput.Builder
to create a
request.default CompletableFuture<CreateDataSourceFromRdsResponse> createDataSourceFromRDS(CreateDataSourceFromRdsRequest createDataSourceFromRdsRequest)
Creates a DataSource
object from an Amazon Relational Database
Service (Amazon RDS). A DataSource
references data that can be used to perform
CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromRDS
is an asynchronous operation. In response to
CreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
is created and ready
for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in
the COMPLETED
or PENDING
state can be used only to perform
>CreateMLModel
>, CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
createDataSourceFromRdsRequest
- default CompletableFuture<CreateDataSourceFromRdsResponse> createDataSourceFromRDS(Consumer<CreateDataSourceFromRdsRequest.Builder> createDataSourceFromRdsRequest)
Creates a DataSource
object from an Amazon Relational Database
Service (Amazon RDS). A DataSource
references data that can be used to perform
CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromRDS
is an asynchronous operation. In response to
CreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
is created and ready
for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in
the COMPLETED
or PENDING
state can be used only to perform
>CreateMLModel
>, CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
This is a convenience which creates an instance of the CreateDataSourceFromRdsRequest.Builder
avoiding
the need to create one manually via CreateDataSourceFromRdsRequest.builder()
createDataSourceFromRdsRequest
- A Consumer
that will call methods on CreateDataSourceFromRDSInput.Builder
to create a
request.default CompletableFuture<CreateDataSourceFromRedshiftResponse> createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
Creates a DataSource
from a database hosted on an Amazon Redshift cluster. A DataSource
references data that can be used to perform either CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
CreateDataSourceFromRedshift
is an asynchronous operation. In response to
CreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
is created and ready
for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in
COMPLETED
or PENDING
states can be used to perform only CreateMLModel
,
CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified
by a SelectSqlQuery
query. Amazon ML executes an Unload
command in Amazon Redshift to
transfer the result set of the SelectSqlQuery
query to S3StagingLocation
.
After the DataSource
has been created, it's ready for use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
requires a recipe. A recipe describes how each input variable will be used in training an MLModel
.
Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
be combined with another variable or will it be split apart into word combinations? The recipe provides answers
to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
Redshift datasource to create a new datasource. To do so, call GetDataSource
for an existing
datasource and copy the values to a CreateDataSource
call. Change the settings that you want to
change and make sure that all required fields have the appropriate values.
createDataSourceFromRedshiftRequest
- default CompletableFuture<CreateDataSourceFromRedshiftResponse> createDataSourceFromRedshift(Consumer<CreateDataSourceFromRedshiftRequest.Builder> createDataSourceFromRedshiftRequest)
Creates a DataSource
from a database hosted on an Amazon Redshift cluster. A DataSource
references data that can be used to perform either CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
CreateDataSourceFromRedshift
is an asynchronous operation. In response to
CreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
is created and ready
for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in
COMPLETED
or PENDING
states can be used to perform only CreateMLModel
,
CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified
by a SelectSqlQuery
query. Amazon ML executes an Unload
command in Amazon Redshift to
transfer the result set of the SelectSqlQuery
query to S3StagingLocation
.
After the DataSource
has been created, it's ready for use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
requires a recipe. A recipe describes how each input variable will be used in training an MLModel
.
Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
be combined with another variable or will it be split apart into word combinations? The recipe provides answers
to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
Redshift datasource to create a new datasource. To do so, call GetDataSource
for an existing
datasource and copy the values to a CreateDataSource
call. Change the settings that you want to
change and make sure that all required fields have the appropriate values.
This is a convenience which creates an instance of the CreateDataSourceFromRedshiftRequest.Builder
avoiding the need to create one manually via CreateDataSourceFromRedshiftRequest.builder()
createDataSourceFromRedshiftRequest
- A Consumer
that will call methods on CreateDataSourceFromRedshiftInput.Builder
to create a
request.default CompletableFuture<CreateDataSourceFromS3Response> createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request)
Creates a DataSource
object. A DataSource
references data that can be used to perform
CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromS3
is an asynchronous operation. In response to
CreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
has been created and is
ready for use, Amazon ML sets the Status
parameter to COMPLETED
.
DataSource
in the COMPLETED
or PENDING
state can be used to perform only
CreateMLModel
, CreateEvaluation
or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observation data used in a DataSource
should be ready to use; that is, it should have a
consistent structure, and missing data values should be kept to a minimum. The observation data must reside in
one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that
describes the data items by name and type. The same schema must be used for all of the data files referenced by
the DataSource
.
After the DataSource
has been created, it's ready to use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
needs a recipe. A recipe describes how each input variable will be used in training an MLModel
. Will
the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
combined with another variable or will it be split apart into word combinations? The recipe provides answers to
these questions.
createDataSourceFromS3Request
- default CompletableFuture<CreateDataSourceFromS3Response> createDataSourceFromS3(Consumer<CreateDataSourceFromS3Request.Builder> createDataSourceFromS3Request)
Creates a DataSource
object. A DataSource
references data that can be used to perform
CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromS3
is an asynchronous operation. In response to
CreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
has been created and is
ready for use, Amazon ML sets the Status
parameter to COMPLETED
.
DataSource
in the COMPLETED
or PENDING
state can be used to perform only
CreateMLModel
, CreateEvaluation
or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observation data used in a DataSource
should be ready to use; that is, it should have a
consistent structure, and missing data values should be kept to a minimum. The observation data must reside in
one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that
describes the data items by name and type. The same schema must be used for all of the data files referenced by
the DataSource
.
After the DataSource
has been created, it's ready to use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
needs a recipe. A recipe describes how each input variable will be used in training an MLModel
. Will
the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
combined with another variable or will it be split apart into word combinations? The recipe provides answers to
these questions.
This is a convenience which creates an instance of the CreateDataSourceFromS3Request.Builder
avoiding the
need to create one manually via CreateDataSourceFromS3Request.builder()
createDataSourceFromS3Request
- A Consumer
that will call methods on CreateDataSourceFromS3Input.Builder
to create a
request.default CompletableFuture<CreateEvaluationResponse> createEvaluation(CreateEvaluationRequest createEvaluationRequest)
Creates a new Evaluation
of an MLModel
. An MLModel
is evaluated on a set
of observations associated to a DataSource
. Like a DataSource
for an
MLModel
, the DataSource
for an Evaluation
contains values for the
Target Variable
. The Evaluation
compares the predicted result for each observation to
the actual outcome and provides a summary so that you know how effective the MLModel
functions on
the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or
MulticlassAvgFScore based on the corresponding MLModelType
: BINARY
,
REGRESSION
or MULTICLASS
.
CreateEvaluation
is an asynchronous operation. In response to CreateEvaluation
, Amazon
Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING
. After
the Evaluation
is created and ready for use, Amazon ML sets the status to COMPLETED
.
You can use the GetEvaluation
operation to check progress of the evaluation during the creation
operation.
createEvaluationRequest
- default CompletableFuture<CreateEvaluationResponse> createEvaluation(Consumer<CreateEvaluationRequest.Builder> createEvaluationRequest)
Creates a new Evaluation
of an MLModel
. An MLModel
is evaluated on a set
of observations associated to a DataSource
. Like a DataSource
for an
MLModel
, the DataSource
for an Evaluation
contains values for the
Target Variable
. The Evaluation
compares the predicted result for each observation to
the actual outcome and provides a summary so that you know how effective the MLModel
functions on
the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or
MulticlassAvgFScore based on the corresponding MLModelType
: BINARY
,
REGRESSION
or MULTICLASS
.
CreateEvaluation
is an asynchronous operation. In response to CreateEvaluation
, Amazon
Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING
. After
the Evaluation
is created and ready for use, Amazon ML sets the status to COMPLETED
.
You can use the GetEvaluation
operation to check progress of the evaluation during the creation
operation.
This is a convenience which creates an instance of the CreateEvaluationRequest.Builder
avoiding the need
to create one manually via CreateEvaluationRequest.builder()
createEvaluationRequest
- A Consumer
that will call methods on CreateEvaluationInput.Builder
to create a request.default CompletableFuture<CreateMlModelResponse> createMLModel(CreateMlModelRequest createMlModelRequest)
Creates a new MLModel
using the DataSource
and the recipe as information sources.
An MLModel
is nearly immutable. Users can update only the MLModelName
and the
ScoreThreshold
in an MLModel
without creating a new MLModel
.
CreateMLModel
is an asynchronous operation. In response to CreateMLModel
, Amazon
Machine Learning (Amazon ML) immediately returns and sets the MLModel
status to PENDING
. After the MLModel
has been created and ready is for use, Amazon ML sets the status to
COMPLETED
.
You can use the GetMLModel
operation to check the progress of the MLModel
during the
creation operation.
CreateMLModel
requires a DataSource
with computed statistics, which can be created by
setting ComputeStatistics
to true
in CreateDataSourceFromRDS
,
CreateDataSourceFromS3
, or CreateDataSourceFromRedshift
operations.
createMlModelRequest
- default CompletableFuture<CreateMlModelResponse> createMLModel(Consumer<CreateMlModelRequest.Builder> createMlModelRequest)
Creates a new MLModel
using the DataSource
and the recipe as information sources.
An MLModel
is nearly immutable. Users can update only the MLModelName
and the
ScoreThreshold
in an MLModel
without creating a new MLModel
.
CreateMLModel
is an asynchronous operation. In response to CreateMLModel
, Amazon
Machine Learning (Amazon ML) immediately returns and sets the MLModel
status to PENDING
. After the MLModel
has been created and ready is for use, Amazon ML sets the status to
COMPLETED
.
You can use the GetMLModel
operation to check the progress of the MLModel
during the
creation operation.
CreateMLModel
requires a DataSource
with computed statistics, which can be created by
setting ComputeStatistics
to true
in CreateDataSourceFromRDS
,
CreateDataSourceFromS3
, or CreateDataSourceFromRedshift
operations.
This is a convenience which creates an instance of the CreateMlModelRequest.Builder
avoiding the need to
create one manually via CreateMlModelRequest.builder()
createMlModelRequest
- A Consumer
that will call methods on CreateMLModelInput.Builder
to create a request.default CompletableFuture<CreateRealtimeEndpointResponse> createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
Creates a real-time endpoint for the MLModel
. The endpoint contains the URI of the
MLModel
; that is, the location to send real-time prediction requests for the specified
MLModel
.
createRealtimeEndpointRequest
- default CompletableFuture<CreateRealtimeEndpointResponse> createRealtimeEndpoint(Consumer<CreateRealtimeEndpointRequest.Builder> createRealtimeEndpointRequest)
Creates a real-time endpoint for the MLModel
. The endpoint contains the URI of the
MLModel
; that is, the location to send real-time prediction requests for the specified
MLModel
.
This is a convenience which creates an instance of the CreateRealtimeEndpointRequest.Builder
avoiding the
need to create one manually via CreateRealtimeEndpointRequest.builder()
createRealtimeEndpointRequest
- A Consumer
that will call methods on CreateRealtimeEndpointInput.Builder
to create a
request.default CompletableFuture<DeleteBatchPredictionResponse> deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
Assigns the DELETED status to a BatchPrediction
, rendering it unusable.
After using the DeleteBatchPrediction
operation, you can use the GetBatchPrediction operation
to verify that the status of the BatchPrediction
changed to DELETED.
Caution: The result of the DeleteBatchPrediction
operation is irreversible.
deleteBatchPredictionRequest
- default CompletableFuture<DeleteBatchPredictionResponse> deleteBatchPrediction(Consumer<DeleteBatchPredictionRequest.Builder> deleteBatchPredictionRequest)
Assigns the DELETED status to a BatchPrediction
, rendering it unusable.
After using the DeleteBatchPrediction
operation, you can use the GetBatchPrediction operation
to verify that the status of the BatchPrediction
changed to DELETED.
Caution: The result of the DeleteBatchPrediction
operation is irreversible.
This is a convenience which creates an instance of the DeleteBatchPredictionRequest.Builder
avoiding the
need to create one manually via DeleteBatchPredictionRequest.builder()
deleteBatchPredictionRequest
- A Consumer
that will call methods on DeleteBatchPredictionInput.Builder
to create a
request.default CompletableFuture<DeleteDataSourceResponse> deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest)
Assigns the DELETED status to a DataSource
, rendering it unusable.
After using the DeleteDataSource
operation, you can use the GetDataSource operation to verify
that the status of the DataSource
changed to DELETED.
Caution: The results of the DeleteDataSource
operation are irreversible.
deleteDataSourceRequest
- default CompletableFuture<DeleteDataSourceResponse> deleteDataSource(Consumer<DeleteDataSourceRequest.Builder> deleteDataSourceRequest)
Assigns the DELETED status to a DataSource
, rendering it unusable.
After using the DeleteDataSource
operation, you can use the GetDataSource operation to verify
that the status of the DataSource
changed to DELETED.
Caution: The results of the DeleteDataSource
operation are irreversible.
This is a convenience which creates an instance of the DeleteDataSourceRequest.Builder
avoiding the need
to create one manually via DeleteDataSourceRequest.builder()
deleteDataSourceRequest
- A Consumer
that will call methods on DeleteDataSourceInput.Builder
to create a request.default CompletableFuture<DeleteEvaluationResponse> deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest)
Assigns the DELETED
status to an Evaluation
, rendering it unusable.
After invoking the DeleteEvaluation
operation, you can use the GetEvaluation
operation
to verify that the status of the Evaluation
changed to DELETED
.
The results of the DeleteEvaluation
operation are irreversible.
deleteEvaluationRequest
- default CompletableFuture<DeleteEvaluationResponse> deleteEvaluation(Consumer<DeleteEvaluationRequest.Builder> deleteEvaluationRequest)
Assigns the DELETED
status to an Evaluation
, rendering it unusable.
After invoking the DeleteEvaluation
operation, you can use the GetEvaluation
operation
to verify that the status of the Evaluation
changed to DELETED
.
The results of the DeleteEvaluation
operation are irreversible.
This is a convenience which creates an instance of the DeleteEvaluationRequest.Builder
avoiding the need
to create one manually via DeleteEvaluationRequest.builder()
deleteEvaluationRequest
- A Consumer
that will call methods on DeleteEvaluationInput.Builder
to create a request.default CompletableFuture<DeleteMlModelResponse> deleteMLModel(DeleteMlModelRequest deleteMlModelRequest)
Assigns the DELETED
status to an MLModel
, rendering it unusable.
After using the DeleteMLModel
operation, you can use the GetMLModel
operation to verify
that the status of the MLModel
changed to DELETED.
Caution: The result of the DeleteMLModel
operation is irreversible.
deleteMlModelRequest
- default CompletableFuture<DeleteMlModelResponse> deleteMLModel(Consumer<DeleteMlModelRequest.Builder> deleteMlModelRequest)
Assigns the DELETED
status to an MLModel
, rendering it unusable.
After using the DeleteMLModel
operation, you can use the GetMLModel
operation to verify
that the status of the MLModel
changed to DELETED.
Caution: The result of the DeleteMLModel
operation is irreversible.
This is a convenience which creates an instance of the DeleteMlModelRequest.Builder
avoiding the need to
create one manually via DeleteMlModelRequest.builder()
deleteMlModelRequest
- A Consumer
that will call methods on DeleteMLModelInput.Builder
to create a request.default CompletableFuture<DeleteRealtimeEndpointResponse> deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an MLModel
.
deleteRealtimeEndpointRequest
- default CompletableFuture<DeleteRealtimeEndpointResponse> deleteRealtimeEndpoint(Consumer<DeleteRealtimeEndpointRequest.Builder> deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an MLModel
.
This is a convenience which creates an instance of the DeleteRealtimeEndpointRequest.Builder
avoiding the
need to create one manually via DeleteRealtimeEndpointRequest.builder()
deleteRealtimeEndpointRequest
- A Consumer
that will call methods on DeleteRealtimeEndpointInput.Builder
to create a
request.default CompletableFuture<DeleteTagsResponse> deleteTags(DeleteTagsRequest deleteTagsRequest)
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.
deleteTagsRequest
- default CompletableFuture<DeleteTagsResponse> deleteTags(Consumer<DeleteTagsRequest.Builder> deleteTagsRequest)
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
This is a convenience which creates an instance of the DeleteTagsRequest.Builder
avoiding the need to
create one manually via DeleteTagsRequest.builder()
deleteTagsRequest
- A Consumer
that will call methods on DeleteTagsInput.Builder
to create a request.default CompletableFuture<DescribeBatchPredictionsResponse> describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of BatchPrediction
operations that match the search criteria in the request.
describeBatchPredictionsRequest
- default CompletableFuture<DescribeBatchPredictionsResponse> describeBatchPredictions(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest)
Returns a list of BatchPrediction
operations that match the search criteria in the request.
This is a convenience which creates an instance of the DescribeBatchPredictionsRequest.Builder
avoiding
the need to create one manually via DescribeBatchPredictionsRequest.builder()
describeBatchPredictionsRequest
- A Consumer
that will call methods on DescribeBatchPredictionsInput.Builder
to create a
request.default CompletableFuture<DescribeBatchPredictionsResponse> describeBatchPredictions()
Returns a list of BatchPrediction
operations that match the search criteria in the request.
default DescribeBatchPredictionsPublisher describeBatchPredictionsPaginator()
Returns a list of BatchPrediction
operations that match the search criteria in the request.
This is a variant of
describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsPublisher publisher = client.describeBatchPredictionsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsPublisher publisher = client.describeBatchPredictionsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)
operation.
default DescribeBatchPredictionsPublisher describeBatchPredictionsPaginator(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of BatchPrediction
operations that match the search criteria in the request.
This is a variant of
describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsPublisher publisher = client.describeBatchPredictionsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsPublisher publisher = client.describeBatchPredictionsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)
operation.
describeBatchPredictionsRequest
- default DescribeBatchPredictionsPublisher describeBatchPredictionsPaginator(Consumer<DescribeBatchPredictionsRequest.Builder> describeBatchPredictionsRequest)
Returns a list of BatchPrediction
operations that match the search criteria in the request.
This is a variant of
describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsPublisher publisher = client.describeBatchPredictionsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeBatchPredictionsPublisher publisher = client.describeBatchPredictionsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeBatchPredictions(software.amazon.awssdk.services.machinelearning.model.DescribeBatchPredictionsRequest)
operation.
This is a convenience which creates an instance of the DescribeBatchPredictionsRequest.Builder
avoiding
the need to create one manually via DescribeBatchPredictionsRequest.builder()
describeBatchPredictionsRequest
- A Consumer
that will call methods on DescribeBatchPredictionsInput.Builder
to create a
request.default CompletableFuture<DescribeDataSourcesResponse> describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of DataSource
that match the search criteria in the request.
describeDataSourcesRequest
- default CompletableFuture<DescribeDataSourcesResponse> describeDataSources(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest)
Returns a list of DataSource
that match the search criteria in the request.
This is a convenience which creates an instance of the DescribeDataSourcesRequest.Builder
avoiding the
need to create one manually via DescribeDataSourcesRequest.builder()
describeDataSourcesRequest
- A Consumer
that will call methods on DescribeDataSourcesInput.Builder
to create a request.default CompletableFuture<DescribeDataSourcesResponse> describeDataSources()
Returns a list of DataSource
that match the search criteria in the request.
default DescribeDataSourcesPublisher describeDataSourcesPaginator()
Returns a list of DataSource
that match the search criteria in the request.
This is a variant of
describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesPublisher publisher = client.describeDataSourcesPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesPublisher publisher = client.describeDataSourcesPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)
operation.
default DescribeDataSourcesPublisher describeDataSourcesPaginator(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of DataSource
that match the search criteria in the request.
This is a variant of
describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesPublisher publisher = client.describeDataSourcesPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesPublisher publisher = client.describeDataSourcesPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)
operation.
describeDataSourcesRequest
- default DescribeDataSourcesPublisher describeDataSourcesPaginator(Consumer<DescribeDataSourcesRequest.Builder> describeDataSourcesRequest)
Returns a list of DataSource
that match the search criteria in the request.
This is a variant of
describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesPublisher publisher = client.describeDataSourcesPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeDataSourcesPublisher publisher = client.describeDataSourcesPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeDataSources(software.amazon.awssdk.services.machinelearning.model.DescribeDataSourcesRequest)
operation.
This is a convenience which creates an instance of the DescribeDataSourcesRequest.Builder
avoiding the
need to create one manually via DescribeDataSourcesRequest.builder()
describeDataSourcesRequest
- A Consumer
that will call methods on DescribeDataSourcesInput.Builder
to create a request.default CompletableFuture<DescribeEvaluationsResponse> describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of DescribeEvaluations
that match the search criteria in the request.
describeEvaluationsRequest
- default CompletableFuture<DescribeEvaluationsResponse> describeEvaluations(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest)
Returns a list of DescribeEvaluations
that match the search criteria in the request.
This is a convenience which creates an instance of the DescribeEvaluationsRequest.Builder
avoiding the
need to create one manually via DescribeEvaluationsRequest.builder()
describeEvaluationsRequest
- A Consumer
that will call methods on DescribeEvaluationsInput.Builder
to create a request.default CompletableFuture<DescribeEvaluationsResponse> describeEvaluations()
Returns a list of DescribeEvaluations
that match the search criteria in the request.
default DescribeEvaluationsPublisher describeEvaluationsPaginator()
Returns a list of DescribeEvaluations
that match the search criteria in the request.
This is a variant of
describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsPublisher publisher = client.describeEvaluationsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsPublisher publisher = client.describeEvaluationsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)
operation.
default DescribeEvaluationsPublisher describeEvaluationsPaginator(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of DescribeEvaluations
that match the search criteria in the request.
This is a variant of
describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsPublisher publisher = client.describeEvaluationsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsPublisher publisher = client.describeEvaluationsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)
operation.
describeEvaluationsRequest
- default DescribeEvaluationsPublisher describeEvaluationsPaginator(Consumer<DescribeEvaluationsRequest.Builder> describeEvaluationsRequest)
Returns a list of DescribeEvaluations
that match the search criteria in the request.
This is a variant of
describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsPublisher publisher = client.describeEvaluationsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeEvaluationsPublisher publisher = client.describeEvaluationsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeEvaluations(software.amazon.awssdk.services.machinelearning.model.DescribeEvaluationsRequest)
operation.
This is a convenience which creates an instance of the DescribeEvaluationsRequest.Builder
avoiding the
need to create one manually via DescribeEvaluationsRequest.builder()
describeEvaluationsRequest
- A Consumer
that will call methods on DescribeEvaluationsInput.Builder
to create a request.default CompletableFuture<DescribeMlModelsResponse> describeMLModels(DescribeMlModelsRequest describeMlModelsRequest)
Returns a list of MLModel
that match the search criteria in the request.
describeMlModelsRequest
- default CompletableFuture<DescribeMlModelsResponse> describeMLModels(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest)
Returns a list of MLModel
that match the search criteria in the request.
This is a convenience which creates an instance of the DescribeMlModelsRequest.Builder
avoiding the need
to create one manually via DescribeMlModelsRequest.builder()
describeMlModelsRequest
- A Consumer
that will call methods on DescribeMLModelsInput.Builder
to create a request.default CompletableFuture<DescribeMlModelsResponse> describeMLModels()
Returns a list of MLModel
that match the search criteria in the request.
default DescribeMLModelsPublisher describeMLModelsPaginator()
Returns a list of MLModel
that match the search criteria in the request.
This is a variant of
describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsPublisher publisher = client.describeMLModelsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsPublisher publisher = client.describeMLModelsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)
operation.
default DescribeMLModelsPublisher describeMLModelsPaginator(DescribeMlModelsRequest describeMlModelsRequest)
Returns a list of MLModel
that match the search criteria in the request.
This is a variant of
describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsPublisher publisher = client.describeMLModelsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsPublisher publisher = client.describeMLModelsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)
operation.
describeMlModelsRequest
- default DescribeMLModelsPublisher describeMLModelsPaginator(Consumer<DescribeMlModelsRequest.Builder> describeMlModelsRequest)
Returns a list of MLModel
that match the search criteria in the request.
This is a variant of
describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)
operation. The return type is a custom publisher that can be subscribed to request a stream of response pages.
SDK will internally handle making service calls for you.
When the operation is called, an instance of this class is returned. At this point, no service calls are made yet
and so there is no guarantee that the request is valid. If there are errors in your request, you will see the
failures only after you start streaming the data. The subscribe method should be called as a request to start
streaming data. For more info, see
Publisher.subscribe(org.reactivestreams.Subscriber)
. Each call to the subscribe
method will result in a new Subscription
i.e., a new contract to stream data from the
starting request.
The following are few ways to use the response class:
1) Using the forEach helper method
software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsPublisher publisher = client.describeMLModelsPaginator(request);
CompletableFuture<Void> future = publisher.forEach(res -> { // Do something with the response });
future.get();
2) Using a custom subscriber
software.amazon.awssdk.services.machinelearning.paginators.DescribeMLModelsPublisher publisher = client.describeMLModelsPaginator(request);
publisher.subscribe(new Subscriber<software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse>() {
public void onSubscribe(org.reactivestreams.Subscriber subscription) { //... };
public void onNext(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsResponse response) { //... };
});
As the response is a publisher, it can work well with third party reactive streams implementations like RxJava2.
Note: If you prefer to have control on service calls, use the
describeMLModels(software.amazon.awssdk.services.machinelearning.model.DescribeMlModelsRequest)
operation.
This is a convenience which creates an instance of the DescribeMlModelsRequest.Builder
avoiding the need
to create one manually via DescribeMlModelsRequest.builder()
describeMlModelsRequest
- A Consumer
that will call methods on DescribeMLModelsInput.Builder
to create a request.default CompletableFuture<DescribeTagsResponse> describeTags(DescribeTagsRequest describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
describeTagsRequest
- default CompletableFuture<DescribeTagsResponse> describeTags(Consumer<DescribeTagsRequest.Builder> describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
This is a convenience which creates an instance of the DescribeTagsRequest.Builder
avoiding the need to
create one manually via DescribeTagsRequest.builder()
describeTagsRequest
- A Consumer
that will call methods on DescribeTagsInput.Builder
to create a request.default CompletableFuture<GetBatchPredictionResponse> getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)
Returns a BatchPrediction
that includes detailed metadata, status, and data file information for a
Batch Prediction
request.
getBatchPredictionRequest
- default CompletableFuture<GetBatchPredictionResponse> getBatchPrediction(Consumer<GetBatchPredictionRequest.Builder> getBatchPredictionRequest)
Returns a BatchPrediction
that includes detailed metadata, status, and data file information for a
Batch Prediction
request.
This is a convenience which creates an instance of the GetBatchPredictionRequest.Builder
avoiding the
need to create one manually via GetBatchPredictionRequest.builder()
getBatchPredictionRequest
- A Consumer
that will call methods on GetBatchPredictionInput.Builder
to create a request.default CompletableFuture<GetDataSourceResponse> getDataSource(GetDataSourceRequest getDataSourceRequest)
Returns a DataSource
that includes metadata and data file information, as well as the current status
of the DataSource
.
GetDataSource
provides results in normal or verbose format. The verbose format adds the schema
description and the list of files pointed to by the DataSource to the normal format.
getDataSourceRequest
- default CompletableFuture<GetDataSourceResponse> getDataSource(Consumer<GetDataSourceRequest.Builder> getDataSourceRequest)
Returns a DataSource
that includes metadata and data file information, as well as the current status
of the DataSource
.
GetDataSource
provides results in normal or verbose format. The verbose format adds the schema
description and the list of files pointed to by the DataSource to the normal format.
This is a convenience which creates an instance of the GetDataSourceRequest.Builder
avoiding the need to
create one manually via GetDataSourceRequest.builder()
getDataSourceRequest
- A Consumer
that will call methods on GetDataSourceInput.Builder
to create a request.default CompletableFuture<GetEvaluationResponse> getEvaluation(GetEvaluationRequest getEvaluationRequest)
Returns an Evaluation
that includes metadata as well as the current status of the
Evaluation
.
getEvaluationRequest
- default CompletableFuture<GetEvaluationResponse> getEvaluation(Consumer<GetEvaluationRequest.Builder> getEvaluationRequest)
Returns an Evaluation
that includes metadata as well as the current status of the
Evaluation
.
This is a convenience which creates an instance of the GetEvaluationRequest.Builder
avoiding the need to
create one manually via GetEvaluationRequest.builder()
getEvaluationRequest
- A Consumer
that will call methods on GetEvaluationInput.Builder
to create a request.default CompletableFuture<GetMlModelResponse> getMLModel(GetMlModelRequest getMlModelRequest)
Returns an MLModel
that includes detailed metadata, data source information, and the current status
of the MLModel
.
GetMLModel
provides results in normal or verbose format.
getMlModelRequest
- default CompletableFuture<GetMlModelResponse> getMLModel(Consumer<GetMlModelRequest.Builder> getMlModelRequest)
Returns an MLModel
that includes detailed metadata, data source information, and the current status
of the MLModel
.
GetMLModel
provides results in normal or verbose format.
This is a convenience which creates an instance of the GetMlModelRequest.Builder
avoiding the need to
create one manually via GetMlModelRequest.builder()
getMlModelRequest
- A Consumer
that will call methods on GetMLModelInput.Builder
to create a request.default CompletableFuture<PredictResponse> predict(PredictRequest predictRequest)
Generates a prediction for the observation using the specified ML Model
.
Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
predictRequest
- DataSource
.MLModel
.default CompletableFuture<PredictResponse> predict(Consumer<PredictRequest.Builder> predictRequest)
Generates a prediction for the observation using the specified ML Model
.
Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
This is a convenience which creates an instance of the PredictRequest.Builder
avoiding the need to create
one manually via PredictRequest.builder()
predictRequest
- A Consumer
that will call methods on PredictInput.Builder
to create a request.DataSource
.MLModel
.default CompletableFuture<UpdateBatchPredictionResponse> updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)
Updates the BatchPredictionName
of a BatchPrediction
.
You can use the GetBatchPrediction
operation to view the contents of the updated data element.
updateBatchPredictionRequest
- default CompletableFuture<UpdateBatchPredictionResponse> updateBatchPrediction(Consumer<UpdateBatchPredictionRequest.Builder> updateBatchPredictionRequest)
Updates the BatchPredictionName
of a BatchPrediction
.
You can use the GetBatchPrediction
operation to view the contents of the updated data element.
This is a convenience which creates an instance of the UpdateBatchPredictionRequest.Builder
avoiding the
need to create one manually via UpdateBatchPredictionRequest.builder()
updateBatchPredictionRequest
- A Consumer
that will call methods on UpdateBatchPredictionInput.Builder
to create a
request.default CompletableFuture<UpdateDataSourceResponse> updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)
Updates the DataSourceName
of a DataSource
.
You can use the GetDataSource
operation to view the contents of the updated data element.
updateDataSourceRequest
- default CompletableFuture<UpdateDataSourceResponse> updateDataSource(Consumer<UpdateDataSourceRequest.Builder> updateDataSourceRequest)
Updates the DataSourceName
of a DataSource
.
You can use the GetDataSource
operation to view the contents of the updated data element.
This is a convenience which creates an instance of the UpdateDataSourceRequest.Builder
avoiding the need
to create one manually via UpdateDataSourceRequest.builder()
updateDataSourceRequest
- A Consumer
that will call methods on UpdateDataSourceInput.Builder
to create a request.default CompletableFuture<UpdateEvaluationResponse> updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)
Updates the EvaluationName
of an Evaluation
.
You can use the GetEvaluation
operation to view the contents of the updated data element.
updateEvaluationRequest
- default CompletableFuture<UpdateEvaluationResponse> updateEvaluation(Consumer<UpdateEvaluationRequest.Builder> updateEvaluationRequest)
Updates the EvaluationName
of an Evaluation
.
You can use the GetEvaluation
operation to view the contents of the updated data element.
This is a convenience which creates an instance of the UpdateEvaluationRequest.Builder
avoiding the need
to create one manually via UpdateEvaluationRequest.builder()
updateEvaluationRequest
- A Consumer
that will call methods on UpdateEvaluationInput.Builder
to create a request.default CompletableFuture<UpdateMlModelResponse> updateMLModel(UpdateMlModelRequest updateMlModelRequest)
Updates the MLModelName
and the ScoreThreshold
of an MLModel
.
You can use the GetMLModel
operation to view the contents of the updated data element.
updateMlModelRequest
- default CompletableFuture<UpdateMlModelResponse> updateMLModel(Consumer<UpdateMlModelRequest.Builder> updateMlModelRequest)
Updates the MLModelName
and the ScoreThreshold
of an MLModel
.
You can use the GetMLModel
operation to view the contents of the updated data element.
This is a convenience which creates an instance of the UpdateMlModelRequest.Builder
avoiding the need to
create one manually via UpdateMlModelRequest.builder()
updateMlModelRequest
- A Consumer
that will call methods on UpdateMLModelInput.Builder
to create a request.Copyright © 2017 Amazon Web Services, Inc. All Rights Reserved.