View Source AWS.MachineLearning (aws-elixir v1.0.4)
Definition of the public APIs exposed by Amazon Machine Learning
Link to this section Summary
Functions
Adds one or more tags to an object, up to a limit of 10.
Generates predictions for a group of observations.
Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS).
Creates a DataSource from a database hosted on an Amazon Redshift cluster.
Creates a DataSource object.
Creates a new Evaluation of an MLModel.
Creates a new MLModel using the DataSource and the recipe as
information sources.
Creates a real-time endpoint for the MLModel.
Assigns the DELETED status to a BatchPrediction, rendering it unusable.
Assigns the DELETED status to a DataSource, rendering it unusable.
Assigns the DELETED status to an Evaluation, rendering it unusable.
Assigns the DELETED status to an MLModel, rendering it unusable.
Deletes a real time endpoint of an MLModel.
Deletes the specified tags associated with an ML object.
Returns a list of BatchPrediction operations that match the search criteria in
the request.
Returns a list of DataSource that match the search criteria in the request.
Returns a list of DescribeEvaluations that match the search criteria in the
request.
Returns a list of MLModel that match the search criteria in the request.
Describes one or more of the tags for your Amazon ML object.
Returns a BatchPrediction that includes detailed metadata, status, and data
file information for a
Batch Prediction request.
Returns a DataSource that includes metadata and data file information, as well
as the current status of the DataSource.
Returns an Evaluation that includes metadata as well as the current status of
the Evaluation.
Returns an MLModel that includes detailed metadata, data source information,
and the current status of the MLModel.
Generates a prediction for the observation using the specified ML Model.
Updates the BatchPredictionName of a BatchPrediction.
Updates the DataSourceName of a DataSource.
Updates the EvaluationName of an Evaluation.
Updates the MLModelName and the ScoreThreshold of an MLModel.
Link to this section Functions
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Deletes a real time endpoint of an MLModel.
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.
Returns a list of BatchPrediction operations that match the search criteria in
the request.
Returns a list of DataSource that match the search criteria in the request.
Returns a list of DescribeEvaluations that match the search criteria in the
request.
Returns a list of MLModel that match the search criteria in the request.
Describes one or more of the tags for your Amazon ML object.
Returns a BatchPrediction that includes detailed metadata, status, and data
file information for a
Batch Prediction request.
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.
Returns an Evaluation that includes metadata as well as the current status of
the Evaluation.
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.
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.
Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the
updated data element.
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated
data element.
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated
data element.
Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data
element.