public class AbstractAmazonMachineLearningAsync extends AbstractAmazonMachineLearning implements AmazonMachineLearningAsync
AmazonMachineLearningAsync
. Convenient
method forms pass through to the corresponding overload that takes a request
object and an AsyncHandler
, which throws an
UnsupportedOperationException
.ENDPOINT_PREFIX
addTags, createBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, deleteTags, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, describeTags, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, setEndpoint, setRegion, shutdown, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModel, waiters
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
addTags, createBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, deleteTags, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, describeTags, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, setEndpoint, setRegion, shutdown, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModel, waiters
public Future<AddTagsResult> addTagsAsync(AddTagsRequest request)
AmazonMachineLearningAsync
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.
addTagsAsync
in interface AmazonMachineLearningAsync
public Future<AddTagsResult> addTagsAsync(AddTagsRequest request, AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
AmazonMachineLearningAsync
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.
addTagsAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest request)
AmazonMachineLearningAsync
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.
createBatchPredictionAsync
in interface AmazonMachineLearningAsync
public Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest request, AsyncHandler<CreateBatchPredictionRequest,CreateBatchPredictionResult> asyncHandler)
AmazonMachineLearningAsync
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.
createBatchPredictionAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest request)
AmazonMachineLearningAsync
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.
createDataSourceFromRDSAsync
in interface AmazonMachineLearningAsync
public Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest request, AsyncHandler<CreateDataSourceFromRDSRequest,CreateDataSourceFromRDSResult> asyncHandler)
AmazonMachineLearningAsync
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.
createDataSourceFromRDSAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest request)
AmazonMachineLearningAsync
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.
createDataSourceFromRedshiftAsync
in interface AmazonMachineLearningAsync
public Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest request, AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
AmazonMachineLearningAsync
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.
createDataSourceFromRedshiftAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request request)
AmazonMachineLearningAsync
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.
createDataSourceFromS3Async
in interface AmazonMachineLearningAsync
public Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request request, AsyncHandler<CreateDataSourceFromS3Request,CreateDataSourceFromS3Result> asyncHandler)
AmazonMachineLearningAsync
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.
createDataSourceFromS3Async
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest request)
AmazonMachineLearningAsync
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.
createEvaluationAsync
in interface AmazonMachineLearningAsync
public Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest request, AsyncHandler<CreateEvaluationRequest,CreateEvaluationResult> asyncHandler)
AmazonMachineLearningAsync
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.
createEvaluationAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest request)
AmazonMachineLearningAsync
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.
createMLModelAsync
in interface AmazonMachineLearningAsync
public Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest request, AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
AmazonMachineLearningAsync
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.
createMLModelAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest request)
AmazonMachineLearningAsync
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
.
createRealtimeEndpointAsync
in interface AmazonMachineLearningAsync
public Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest request, AsyncHandler<CreateRealtimeEndpointRequest,CreateRealtimeEndpointResult> asyncHandler)
AmazonMachineLearningAsync
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
.
createRealtimeEndpointAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest request)
AmazonMachineLearningAsync
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.
deleteBatchPredictionAsync
in interface AmazonMachineLearningAsync
public Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest request, AsyncHandler<DeleteBatchPredictionRequest,DeleteBatchPredictionResult> asyncHandler)
AmazonMachineLearningAsync
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.
deleteBatchPredictionAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest request)
AmazonMachineLearningAsync
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.
deleteDataSourceAsync
in interface AmazonMachineLearningAsync
public Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest request, AsyncHandler<DeleteDataSourceRequest,DeleteDataSourceResult> asyncHandler)
AmazonMachineLearningAsync
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.
deleteDataSourceAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest request)
AmazonMachineLearningAsync
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.
deleteEvaluationAsync
in interface AmazonMachineLearningAsync
public Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest request, AsyncHandler<DeleteEvaluationRequest,DeleteEvaluationResult> asyncHandler)
AmazonMachineLearningAsync
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.
deleteEvaluationAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest request)
AmazonMachineLearningAsync
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.
deleteMLModelAsync
in interface AmazonMachineLearningAsync
public Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest request, AsyncHandler<DeleteMLModelRequest,DeleteMLModelResult> asyncHandler)
AmazonMachineLearningAsync
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.
deleteMLModelAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request)
AmazonMachineLearningAsync
Deletes a real time endpoint of an MLModel
.
deleteRealtimeEndpointAsync
in interface AmazonMachineLearningAsync
public Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest request, AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler)
AmazonMachineLearningAsync
Deletes a real time endpoint of an MLModel
.
deleteRealtimeEndpointAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest request)
AmazonMachineLearningAsync
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.
deleteTagsAsync
in interface AmazonMachineLearningAsync
public Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest request, AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
AmazonMachineLearningAsync
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.
deleteTagsAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest request)
AmazonMachineLearningAsync
Returns a list of BatchPrediction
operations that match the
search criteria in the request.
describeBatchPredictionsAsync
in interface AmazonMachineLearningAsync
public Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest request, AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
AmazonMachineLearningAsync
Returns a list of BatchPrediction
operations that match the
search criteria in the request.
describeBatchPredictionsAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync()
describeBatchPredictionsAsync
in interface AmazonMachineLearningAsync
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest)
public Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
describeBatchPredictionsAsync
in interface AmazonMachineLearningAsync
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest,
com.amazonaws.handlers.AsyncHandler)
public Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest request)
AmazonMachineLearningAsync
Returns a list of DataSource
that match the search criteria
in the request.
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
public Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest request, AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
AmazonMachineLearningAsync
Returns a list of DataSource
that match the search criteria
in the request.
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeDataSourcesResult> describeDataSourcesAsync()
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
describeDataSourcesAsync(DescribeDataSourcesRequest)
public Future<DescribeDataSourcesResult> describeDataSourcesAsync(AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
describeDataSourcesAsync
in interface AmazonMachineLearningAsync
describeDataSourcesAsync(DescribeDataSourcesRequest,
com.amazonaws.handlers.AsyncHandler)
public Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest request)
AmazonMachineLearningAsync
Returns a list of DescribeEvaluations
that match the search
criteria in the request.
describeEvaluationsAsync
in interface AmazonMachineLearningAsync
public Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest request, AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
AmazonMachineLearningAsync
Returns a list of DescribeEvaluations
that match the search
criteria in the request.
describeEvaluationsAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeEvaluationsResult> describeEvaluationsAsync()
describeEvaluationsAsync
in interface AmazonMachineLearningAsync
describeEvaluationsAsync(DescribeEvaluationsRequest)
public Future<DescribeEvaluationsResult> describeEvaluationsAsync(AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
describeEvaluationsAsync
in interface AmazonMachineLearningAsync
describeEvaluationsAsync(DescribeEvaluationsRequest,
com.amazonaws.handlers.AsyncHandler)
public Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest request)
AmazonMachineLearningAsync
Returns a list of MLModel
that match the search criteria in
the request.
describeMLModelsAsync
in interface AmazonMachineLearningAsync
public Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest request, AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
AmazonMachineLearningAsync
Returns a list of MLModel
that match the search criteria in
the request.
describeMLModelsAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<DescribeMLModelsResult> describeMLModelsAsync()
describeMLModelsAsync
in interface AmazonMachineLearningAsync
describeMLModelsAsync(DescribeMLModelsRequest)
public Future<DescribeMLModelsResult> describeMLModelsAsync(AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
describeMLModelsAsync
in interface AmazonMachineLearningAsync
describeMLModelsAsync(DescribeMLModelsRequest,
com.amazonaws.handlers.AsyncHandler)
public Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest request)
AmazonMachineLearningAsync
Describes one or more of the tags for your Amazon ML object.
describeTagsAsync
in interface AmazonMachineLearningAsync
public Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest request, AsyncHandler<DescribeTagsRequest,DescribeTagsResult> asyncHandler)
AmazonMachineLearningAsync
Describes one or more of the tags for your Amazon ML object.
describeTagsAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<GetBatchPredictionResult> getBatchPredictionAsync(GetBatchPredictionRequest request)
AmazonMachineLearningAsync
Returns a BatchPrediction
that includes detailed metadata,
status, and data file information for a Batch Prediction
request.
getBatchPredictionAsync
in interface AmazonMachineLearningAsync
public Future<GetBatchPredictionResult> getBatchPredictionAsync(GetBatchPredictionRequest request, AsyncHandler<GetBatchPredictionRequest,GetBatchPredictionResult> asyncHandler)
AmazonMachineLearningAsync
Returns a BatchPrediction
that includes detailed metadata,
status, and data file information for a Batch Prediction
request.
getBatchPredictionAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<GetDataSourceResult> getDataSourceAsync(GetDataSourceRequest request)
AmazonMachineLearningAsync
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.
getDataSourceAsync
in interface AmazonMachineLearningAsync
public Future<GetDataSourceResult> getDataSourceAsync(GetDataSourceRequest request, AsyncHandler<GetDataSourceRequest,GetDataSourceResult> asyncHandler)
AmazonMachineLearningAsync
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.
getDataSourceAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest request)
AmazonMachineLearningAsync
Returns an Evaluation
that includes metadata as well as the
current status of the Evaluation
.
getEvaluationAsync
in interface AmazonMachineLearningAsync
public Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest request, AsyncHandler<GetEvaluationRequest,GetEvaluationResult> asyncHandler)
AmazonMachineLearningAsync
Returns an Evaluation
that includes metadata as well as the
current status of the Evaluation
.
getEvaluationAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest request)
AmazonMachineLearningAsync
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.
getMLModelAsync
in interface AmazonMachineLearningAsync
public Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest request, AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
AmazonMachineLearningAsync
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.
getMLModelAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<PredictResult> predictAsync(PredictRequest request)
AmazonMachineLearningAsync
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.
predictAsync
in interface AmazonMachineLearningAsync
public Future<PredictResult> predictAsync(PredictRequest request, AsyncHandler<PredictRequest,PredictResult> asyncHandler)
AmazonMachineLearningAsync
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.
predictAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest request)
AmazonMachineLearningAsync
Updates the BatchPredictionName
of a
BatchPrediction
.
You can use the GetBatchPrediction
operation to view the
contents of the updated data element.
updateBatchPredictionAsync
in interface AmazonMachineLearningAsync
public Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest request, AsyncHandler<UpdateBatchPredictionRequest,UpdateBatchPredictionResult> asyncHandler)
AmazonMachineLearningAsync
Updates the BatchPredictionName
of a
BatchPrediction
.
You can use the GetBatchPrediction
operation to view the
contents of the updated data element.
updateBatchPredictionAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest request)
AmazonMachineLearningAsync
Updates the DataSourceName
of a DataSource
.
You can use the GetDataSource
operation to view the contents
of the updated data element.
updateDataSourceAsync
in interface AmazonMachineLearningAsync
public Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest request, AsyncHandler<UpdateDataSourceRequest,UpdateDataSourceResult> asyncHandler)
AmazonMachineLearningAsync
Updates the DataSourceName
of a DataSource
.
You can use the GetDataSource
operation to view the contents
of the updated data element.
updateDataSourceAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest request)
AmazonMachineLearningAsync
Updates the EvaluationName
of an Evaluation
.
You can use the GetEvaluation
operation to view the contents
of the updated data element.
updateEvaluationAsync
in interface AmazonMachineLearningAsync
public Future<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest request, AsyncHandler<UpdateEvaluationRequest,UpdateEvaluationResult> asyncHandler)
AmazonMachineLearningAsync
Updates the EvaluationName
of an Evaluation
.
You can use the GetEvaluation
operation to view the contents
of the updated data element.
updateEvaluationAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.public Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest request)
AmazonMachineLearningAsync
Updates the MLModelName
and the ScoreThreshold
of an MLModel
.
You can use the GetMLModel
operation to view the contents of
the updated data element.
updateMLModelAsync
in interface AmazonMachineLearningAsync
public Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest request, AsyncHandler<UpdateMLModelRequest,UpdateMLModelResult> asyncHandler)
AmazonMachineLearningAsync
Updates the MLModelName
and the ScoreThreshold
of an MLModel
.
You can use the GetMLModel
operation to view the contents of
the updated data element.
updateMLModelAsync
in interface AmazonMachineLearningAsync
asyncHandler
- Asynchronous callback handler for events in the lifecycle of the
request. Users can provide an implementation of the callback
methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Copyright © 2013 Amazon Web Services, Inc. All Rights Reserved.