| Name | Description |
Asynchronous operations (methods ending with Async) in the table below are for .NET 4.5 or higher. For .NET 3.5 the SDK follows the standard naming convention of BeginMethodName and EndMethodName to indicate asynchronous operations - these method pairs are not shown in the table below.
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AddTags(AddTagsRequest)
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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.
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AddTagsAsync(AddTagsRequest, CancellationToken)
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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.
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CreateBatchPrediction(CreateBatchPredictionRequest)
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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.
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CreateBatchPredictionAsync(CreateBatchPredictionRequest, CancellationToken)
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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.
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CreateDataSourceFromRDS(CreateDataSourceFromRDSRequest)
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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.
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CreateDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest, CancellationToken)
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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.
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CreateDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest)
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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.
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CreateDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest, CancellationToken)
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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.
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CreateDataSourceFromS3(CreateDataSourceFromS3Request)
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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.
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CreateDataSourceFromS3Async(CreateDataSourceFromS3Request, CancellationToken)
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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.
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CreateEvaluation(CreateEvaluationRequest)
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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.
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CreateEvaluationAsync(CreateEvaluationRequest, CancellationToken)
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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.
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CreateMLModel(CreateMLModelRequest)
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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.
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CreateMLModelAsync(CreateMLModelRequest, CancellationToken)
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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.
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CreateRealtimeEndpoint(string)
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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.
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CreateRealtimeEndpoint(CreateRealtimeEndpointRequest)
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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.
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CreateRealtimeEndpointAsync(string, CancellationToken)
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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.
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CreateRealtimeEndpointAsync(CreateRealtimeEndpointRequest, CancellationToken)
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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.
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DeleteBatchPrediction(string)
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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.
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DeleteBatchPrediction(DeleteBatchPredictionRequest)
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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.
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DeleteBatchPredictionAsync(string, CancellationToken)
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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.
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DeleteBatchPredictionAsync(DeleteBatchPredictionRequest, CancellationToken)
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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.
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DeleteDataSource(string)
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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.
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DeleteDataSource(DeleteDataSourceRequest)
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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.
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DeleteDataSourceAsync(string, CancellationToken)
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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.
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DeleteDataSourceAsync(DeleteDataSourceRequest, CancellationToken)
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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.
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DeleteEvaluation(string)
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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.
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DeleteEvaluation(DeleteEvaluationRequest)
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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.
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DeleteEvaluationAsync(string, CancellationToken)
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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.
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DeleteEvaluationAsync(DeleteEvaluationRequest, CancellationToken)
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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.
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DeleteMLModel(string)
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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.
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DeleteMLModel(DeleteMLModelRequest)
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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.
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DeleteMLModelAsync(string, CancellationToken)
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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.
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DeleteMLModelAsync(DeleteMLModelRequest, CancellationToken)
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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.
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DeleteRealtimeEndpoint(string)
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Deletes a real time endpoint of an MLModel.
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DeleteRealtimeEndpoint(DeleteRealtimeEndpointRequest)
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Deletes a real time endpoint of an MLModel.
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DeleteRealtimeEndpointAsync(string, CancellationToken)
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Deletes a real time endpoint of an MLModel.
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DeleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest, CancellationToken)
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Deletes a real time endpoint of an MLModel.
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DeleteTags(DeleteTagsRequest)
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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.
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DeleteTagsAsync(DeleteTagsRequest, CancellationToken)
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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.
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DescribeBatchPredictions(DescribeBatchPredictionsRequest)
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Returns a list of BatchPrediction operations that match the search criteria
in the request.
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DescribeBatchPredictionsAsync(DescribeBatchPredictionsRequest, CancellationToken)
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Returns a list of BatchPrediction operations that match the search criteria
in the request.
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DescribeDataSources(DescribeDataSourcesRequest)
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Returns a list of DataSource that match the search criteria in the request.
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DescribeDataSourcesAsync(DescribeDataSourcesRequest, CancellationToken)
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Returns a list of DataSource that match the search criteria in the request.
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DescribeEvaluations(DescribeEvaluationsRequest)
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Returns a list of DescribeEvaluations that match the search criteria in the
request.
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DescribeEvaluationsAsync(DescribeEvaluationsRequest, CancellationToken)
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Returns a list of DescribeEvaluations that match the search criteria in the
request.
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DescribeMLModels(DescribeMLModelsRequest)
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Returns a list of MLModel that match the search criteria in the request.
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DescribeMLModelsAsync(DescribeMLModelsRequest, CancellationToken)
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Returns a list of MLModel that match the search criteria in the request.
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DescribeTags(DescribeTagsRequest)
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Describes one or more of the tags for your Amazon ML object.
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DescribeTagsAsync(DescribeTagsRequest, CancellationToken)
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Describes one or more of the tags for your Amazon ML object.
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DetermineServiceOperationEndpoint(AmazonWebServiceRequest)
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Returns the endpoint that will be used for a particular request.
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GetBatchPrediction(string)
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Returns a BatchPrediction that includes detailed metadata, status, and data
file information for a Batch Prediction request.
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GetBatchPrediction(GetBatchPredictionRequest)
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Returns a BatchPrediction that includes detailed metadata, status, and data
file information for a Batch Prediction request.
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GetBatchPredictionAsync(string, CancellationToken)
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Returns a BatchPrediction that includes detailed metadata, status, and data
file information for a Batch Prediction request.
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GetBatchPredictionAsync(GetBatchPredictionRequest, CancellationToken)
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Returns a BatchPrediction that includes detailed metadata, status, and data
file information for a Batch Prediction request.
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GetDataSource(string)
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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.
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GetDataSource(string, bool)
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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.
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GetDataSource(GetDataSourceRequest)
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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.
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GetDataSourceAsync(string, CancellationToken)
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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.
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GetDataSourceAsync(string, bool, CancellationToken)
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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.
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GetDataSourceAsync(GetDataSourceRequest, CancellationToken)
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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.
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GetEvaluation(string)
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Returns an Evaluation that includes metadata as well as the current status
of the Evaluation.
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GetEvaluation(GetEvaluationRequest)
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Returns an Evaluation that includes metadata as well as the current status
of the Evaluation.
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GetEvaluationAsync(string, CancellationToken)
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Returns an Evaluation that includes metadata as well as the current status
of the Evaluation.
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GetEvaluationAsync(GetEvaluationRequest, CancellationToken)
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Returns an Evaluation that includes metadata as well as the current status
of the Evaluation.
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GetMLModel(string)
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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.
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GetMLModel(string, bool)
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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.
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GetMLModel(GetMLModelRequest)
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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.
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GetMLModelAsync(string, CancellationToken)
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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.
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GetMLModelAsync(string, bool, CancellationToken)
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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.
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GetMLModelAsync(GetMLModelRequest, CancellationToken)
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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.
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Predict(string, string, Dictionary<String, String>)
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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.
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Predict(PredictRequest)
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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.
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PredictAsync(string, string, Dictionary<String, String>, CancellationToken)
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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.
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PredictAsync(PredictRequest, CancellationToken)
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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.
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UpdateBatchPrediction(string, string)
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Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated
data element.
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UpdateBatchPrediction(UpdateBatchPredictionRequest)
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Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated
data element.
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UpdateBatchPredictionAsync(string, string, CancellationToken)
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Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated
data element.
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UpdateBatchPredictionAsync(UpdateBatchPredictionRequest, CancellationToken)
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Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated
data element.
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UpdateDataSource(string, string)
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Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated
data element.
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UpdateDataSource(UpdateDataSourceRequest)
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Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated
data element.
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UpdateDataSourceAsync(string, string, CancellationToken)
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Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated
data element.
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UpdateDataSourceAsync(UpdateDataSourceRequest, CancellationToken)
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Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated
data element.
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UpdateEvaluation(string, string)
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Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated
data element.
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UpdateEvaluation(UpdateEvaluationRequest)
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Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated
data element.
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UpdateEvaluationAsync(string, string, CancellationToken)
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Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated
data element.
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UpdateEvaluationAsync(UpdateEvaluationRequest, CancellationToken)
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Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated
data element.
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UpdateMLModel(string, string, Single)
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Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data
element.
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UpdateMLModel(UpdateMLModelRequest)
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Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data
element.
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UpdateMLModelAsync(string, string, Single, CancellationToken)
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Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data
element.
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UpdateMLModelAsync(UpdateMLModelRequest, CancellationToken)
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Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data
element.
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