@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public interface AmazonMachineLearning
Note: Do not directly implement this interface, new methods are added to it regularly. Extend from
AbstractAmazonMachineLearning
instead.
Definition of the public APIs exposed by Amazon Machine Learning
Modifier and Type | Field and Description |
---|---|
static String |
ENDPOINT_PREFIX
The region metadata service name for computing region endpoints.
|
Modifier and Type | Method and Description |
---|---|
AddTagsResult |
addTags(AddTagsRequest addTagsRequest)
Adds one or more tags to an object, up to a limit of 10.
|
CreateBatchPredictionResult |
createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest)
Generates predictions for a group of observations.
|
CreateDataSourceFromRDSResult |
createDataSourceFromRDS(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest)
Creates a
DataSource object from an Amazon Relational Database
Service (Amazon RDS). |
CreateDataSourceFromRedshiftResult |
createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
Creates a
DataSource from a database hosted on an Amazon Redshift cluster. |
CreateDataSourceFromS3Result |
createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request)
Creates a
DataSource object. |
CreateEvaluationResult |
createEvaluation(CreateEvaluationRequest createEvaluationRequest)
Creates a new
Evaluation of an MLModel . |
CreateMLModelResult |
createMLModel(CreateMLModelRequest createMLModelRequest)
Creates a new
MLModel using the DataSource and the recipe as information sources. |
CreateRealtimeEndpointResult |
createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
Creates a real-time endpoint for the
MLModel . |
DeleteBatchPredictionResult |
deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
Assigns the DELETED status to a
BatchPrediction , rendering it unusable. |
DeleteDataSourceResult |
deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest)
Assigns the DELETED status to a
DataSource , rendering it unusable. |
DeleteEvaluationResult |
deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest)
Assigns the
DELETED status to an Evaluation , rendering it unusable. |
DeleteMLModelResult |
deleteMLModel(DeleteMLModelRequest deleteMLModelRequest)
Assigns the
DELETED status to an MLModel , rendering it unusable. |
DeleteRealtimeEndpointResult |
deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an
MLModel . |
DeleteTagsResult |
deleteTags(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags associated with an ML object.
|
DescribeBatchPredictionsResult |
describeBatchPredictions()
Simplified method form for invoking the DescribeBatchPredictions operation.
|
DescribeBatchPredictionsResult |
describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of
BatchPrediction operations that match the search criteria in the request. |
DescribeDataSourcesResult |
describeDataSources()
Simplified method form for invoking the DescribeDataSources operation.
|
DescribeDataSourcesResult |
describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of
DataSource that match the search criteria in the request. |
DescribeEvaluationsResult |
describeEvaluations()
Simplified method form for invoking the DescribeEvaluations operation.
|
DescribeEvaluationsResult |
describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of
DescribeEvaluations that match the search criteria in the request. |
DescribeMLModelsResult |
describeMLModels()
Simplified method form for invoking the DescribeMLModels operation.
|
DescribeMLModelsResult |
describeMLModels(DescribeMLModelsRequest describeMLModelsRequest)
Returns a list of
MLModel that match the search criteria in the request. |
DescribeTagsResult |
describeTags(DescribeTagsRequest describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
|
GetBatchPredictionResult |
getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)
Returns a
BatchPrediction that includes detailed metadata, status, and data file information for a
Batch Prediction request. |
ResponseMetadata |
getCachedResponseMetadata(AmazonWebServiceRequest request)
Returns additional metadata for a previously executed successful request, typically used for debugging issues
where a service isn't acting as expected.
|
GetDataSourceResult |
getDataSource(GetDataSourceRequest getDataSourceRequest)
Returns a
DataSource that includes metadata and data file information, as well as the current status
of the DataSource . |
GetEvaluationResult |
getEvaluation(GetEvaluationRequest getEvaluationRequest)
Returns an
Evaluation that includes metadata as well as the current status of the
Evaluation . |
GetMLModelResult |
getMLModel(GetMLModelRequest getMLModelRequest)
Returns an
MLModel that includes detailed metadata, data source information, and the current status
of the MLModel . |
PredictResult |
predict(PredictRequest predictRequest)
Generates a prediction for the observation using the specified
ML Model . |
void |
setEndpoint(String endpoint)
Deprecated.
use
AwsClientBuilder#setEndpointConfiguration(AwsClientBuilder.EndpointConfiguration) for
example:
builder.setEndpointConfiguration(new EndpointConfiguration(endpoint, signingRegion)); |
void |
setRegion(Region region)
Deprecated.
use
AwsClientBuilder#setRegion(String) |
void |
shutdown()
Shuts down this client object, releasing any resources that might be held open.
|
UpdateBatchPredictionResult |
updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)
Updates the
BatchPredictionName of a BatchPrediction . |
UpdateDataSourceResult |
updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)
Updates the
DataSourceName of a DataSource . |
UpdateEvaluationResult |
updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)
Updates the
EvaluationName of an Evaluation . |
UpdateMLModelResult |
updateMLModel(UpdateMLModelRequest updateMLModelRequest)
Updates the
MLModelName and the ScoreThreshold of an MLModel . |
AmazonMachineLearningWaiters |
waiters() |
static final String ENDPOINT_PREFIX
@Deprecated void setEndpoint(String endpoint)
AwsClientBuilder#setEndpointConfiguration(AwsClientBuilder.EndpointConfiguration)
for
example:
builder.setEndpointConfiguration(new EndpointConfiguration(endpoint, signingRegion));
Callers can pass in just the endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including
the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com"). If the protocol is not specified here, the
default protocol from this client's ClientConfiguration
will be used, which by default is HTTPS.
For more information on using AWS regions with the AWS SDK for Java, and a complete list of all available endpoints for all AWS services, see: https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/java-dg-region-selection.html#region-selection- choose-endpoint
This method is not threadsafe. An endpoint should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.
endpoint
- The endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex:
"https://machinelearning.us-east-1.amazonaws.com") of the region specific AWS endpoint this client will
communicate with.@Deprecated void setRegion(Region region)
AwsClientBuilder#setRegion(String)
setEndpoint(String)
, sets the regional endpoint for this client's
service calls. Callers can use this method to control which AWS region they want to work with.
By default, all service endpoints in all regions use the https protocol. To use http instead, specify it in the
ClientConfiguration
supplied at construction.
This method is not threadsafe. A region should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.
region
- The region this client will communicate with. See Region.getRegion(com.amazonaws.regions.Regions)
for accessing a given region. Must not be null and must be a region where the service is available.Region.getRegion(com.amazonaws.regions.Regions)
,
Region.createClient(Class, com.amazonaws.auth.AWSCredentialsProvider, ClientConfiguration)
,
Region.isServiceSupported(String)
AddTagsResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InvalidTagException
TagLimitExceededException
ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.CreateBatchPredictionResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request
using a parameter that was not present in the original request.CreateDataSourceFromRDSResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request
using a parameter that was not present in the original request.CreateDataSourceFromRedshiftResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request
using a parameter that was not present in the original request.CreateDataSourceFromS3Result 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request
using a parameter that was not present in the original request.CreateEvaluationResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request
using a parameter that was not present in the original request.CreateMLModelResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.IdempotentParameterMismatchException
- A second request to use or change an object was not allowed. This can result from retrying a request
using a parameter that was not present in the original request.CreateRealtimeEndpointResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.DeleteBatchPredictionResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.DeleteDataSourceResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.DeleteEvaluationResult 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
.
Caution: The results of the DeleteEvaluation
operation are irreversible.
deleteEvaluationRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.DeleteMLModelResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an MLModel
.
deleteRealtimeEndpointRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.DeleteTagsResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InvalidTagException
ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of BatchPrediction
operations that match the search criteria in the request.
describeBatchPredictionsRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.DescribeBatchPredictionsResult describeBatchPredictions()
DescribeDataSourcesResult describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of DataSource
that match the search criteria in the request.
describeDataSourcesRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.DescribeDataSourcesResult describeDataSources()
DescribeEvaluationsResult describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of DescribeEvaluations
that match the search criteria in the request.
describeEvaluationsRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.DescribeEvaluationsResult describeEvaluations()
DescribeMLModelsResult describeMLModels(DescribeMLModelsRequest describeMLModelsRequest)
Returns a list of MLModel
that match the search criteria in the request.
describeMLModelsRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.InternalServerException
- An error on the server occurred when trying to process a request.DescribeMLModelsResult describeMLModels()
DescribeTagsResult describeTags(DescribeTagsRequest describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
describeTagsRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.GetBatchPredictionResult getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)
Returns a BatchPrediction
that includes detailed metadata, status, and data file information for a
Batch Prediction
request.
getBatchPredictionRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.GetDataSourceResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.GetEvaluationResult getEvaluation(GetEvaluationRequest getEvaluationRequest)
Returns an Evaluation
that includes metadata as well as the current status of the
Evaluation
.
getEvaluationRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.GetMLModelResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.PredictResult predict(PredictRequest predictRequest)
Generates a prediction for the observation using the specified ML Model
.
Note: Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
predictRequest
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.LimitExceededException
- The subscriber exceeded the maximum number of operations. This exception can occur when listing objects
such as DataSource
.InternalServerException
- An error on the server occurred when trying to process a request.PredictorNotMountedException
- The exception is thrown when a predict request is made to an unmounted MLModel
.UpdateBatchPredictionResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.UpdateDataSourceResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.UpdateEvaluationResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.UpdateMLModelResult 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
- InvalidInputException
- An error on the client occurred. Typically, the cause is an invalid input value.ResourceNotFoundException
- A specified resource cannot be located.InternalServerException
- An error on the server occurred when trying to process a request.void shutdown()
ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic information for an executed request, you should use this method to retrieve it as soon as possible after executing a request.
request
- The originally executed request.AmazonMachineLearningWaiters waiters()