aws_machinelearning
Definition of the public APIs exposed by Amazon Machine Learning
Summary
Functions
-
add_tags(Client, Input)
Adds one or more tags to an object, up to a limit of 10.
- add_tags(Client, Input, Options)
-
create_batch_prediction(Client, Input)
Generates predictions for a group of observations.
- create_batch_prediction(Client, Input, Options)
-
create_data_source_from_rds(Client, Input)
Creates a
DataSource
object from an Amazon Relational Database Service (Amazon RDS). - create_data_source_from_rds(Client, Input, Options)
-
create_data_source_from_redshift(Client, Input)
Creates a
DataSource
from a database hosted on an Amazon Redshift cluster. - create_data_source_from_redshift(Client, Input, Options)
-
create_data_source_from_s3(Client, Input)
Creates a
DataSource
object. - create_data_source_from_s3(Client, Input, Options)
-
create_evaluation(Client, Input)
Creates a new
Evaluation
of anMLModel
. - create_evaluation(Client, Input, Options)
-
create_ml_model(Client, Input)
Creates a new
MLModel
using theDataSource
and the recipe as information sources. - create_ml_model(Client, Input, Options)
-
create_realtime_endpoint(Client, Input)
Creates a real-time endpoint for the
MLModel
. - create_realtime_endpoint(Client, Input, Options)
-
delete_batch_prediction(Client, Input)
Assigns the DELETED status to a
BatchPrediction
, rendering it unusable. - delete_batch_prediction(Client, Input, Options)
-
delete_data_source(Client, Input)
Assigns the DELETED status to a
DataSource
, rendering it unusable. - delete_data_source(Client, Input, Options)
-
delete_evaluation(Client, Input)
Assigns the
DELETED
status to anEvaluation
, rendering it unusable. - delete_evaluation(Client, Input, Options)
-
delete_ml_model(Client, Input)
Assigns the
DELETED
status to anMLModel
, rendering it unusable. - delete_ml_model(Client, Input, Options)
-
delete_realtime_endpoint(Client, Input)
Deletes a real time endpoint of an
MLModel
. - delete_realtime_endpoint(Client, Input, Options)
-
delete_tags(Client, Input)
Deletes the specified tags associated with an ML object.
- delete_tags(Client, Input, Options)
-
describe_batch_predictions(Client, Input)
Returns a list of
BatchPrediction
operations that match the search criteria in the request. - describe_batch_predictions(Client, Input, Options)
-
describe_data_sources(Client, Input)
Returns a list of
DataSource
that match the search criteria in the request. - describe_data_sources(Client, Input, Options)
-
describe_evaluations(Client, Input)
Returns a list of
DescribeEvaluations
that match the search criteria in the request. - describe_evaluations(Client, Input, Options)
-
describe_ml_models(Client, Input)
Returns a list of
MLModel
that match the search criteria in the request. - describe_ml_models(Client, Input, Options)
-
describe_tags(Client, Input)
Describes one or more of the tags for your Amazon ML object.
- describe_tags(Client, Input, Options)
-
get_batch_prediction(Client, Input)
Returns a
BatchPrediction
that includes detailed metadata, status, and data file information for aBatch Prediction
request. - get_batch_prediction(Client, Input, Options)
-
get_data_source(Client, Input)
Returns a
DataSource
that includes metadata and data file information, as well as the current status of theDataSource
. - get_data_source(Client, Input, Options)
-
get_evaluation(Client, Input)
Returns an
Evaluation
that includes metadata as well as the current status of theEvaluation
. - get_evaluation(Client, Input, Options)
-
get_ml_model(Client, Input)
Returns an
MLModel
that includes detailed metadata, data source information, and the current status of theMLModel
. - get_ml_model(Client, Input, Options)
-
predict(Client, Input)
Generates a prediction for the observation using the specified
ML Model
. - predict(Client, Input, Options)
-
update_batch_prediction(Client, Input)
Updates the
BatchPredictionName
of aBatchPrediction
. - update_batch_prediction(Client, Input, Options)
-
update_data_source(Client, Input)
Updates the
DataSourceName
of aDataSource
. - update_data_source(Client, Input, Options)
-
update_evaluation(Client, Input)
Updates the
EvaluationName
of anEvaluation
. - update_evaluation(Client, Input, Options)
-
update_ml_model(Client, Input)
Updates the
MLModelName
and theScoreThreshold
of anMLModel
. - update_ml_model(Client, Input, Options)
Functions
add_tags(Client, Input)
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.
add_tags(Client, Input, Options)
create_batch_prediction(Client, Input)
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
.
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.
create_batch_prediction(Client, Input, Options)
create_data_source_from_rds(Client, Input)
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.
Status
parameter to FAILED
and includes an error message in the Message
attribute of the GetDataSource
operation response.
create_data_source_from_rds(Client, Input, Options)
create_data_source_from_redshift(Client, Input)
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.
create_data_source_from_redshift(Client, Input, Options)
create_data_source_from_s3(Client, Input)
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
.
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.
create_data_source_from_s3(Client, Input, Options)
create_evaluation(Client, Input)
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
.
GetEvaluation
operation to check progress of the
evaluation during the creation operation.
create_evaluation(Client, Input, Options)
create_ml_model(Client, Input)
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.
create_ml_model(Client, Input, Options)
create_realtime_endpoint(Client, Input)
Creates a real-time endpoint for the MLModel
.
MLModel
; that is, the location to
send real-time prediction requests for the specified MLModel
.
create_realtime_endpoint(Client, Input, Options)
delete_batch_prediction(Client, Input)
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.
DeleteBatchPrediction
operation is
irreversible.
delete_batch_prediction(Client, Input, Options)
delete_data_source(Client, Input)
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.
DeleteDataSource
operation are irreversible.
delete_data_source(Client, Input, Options)
delete_evaluation(Client, Input)
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
.
DeleteEvaluation
operation are irreversible.
delete_evaluation(Client, Input, Options)
delete_ml_model(Client, Input)
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.
DeleteMLModel
operation is irreversible.
delete_ml_model(Client, Input, Options)
delete_realtime_endpoint(Client, Input)
Deletes a real time endpoint of an MLModel
.
delete_realtime_endpoint(Client, Input, Options)
delete_tags(Client, Input)
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.delete_tags(Client, Input, Options)
describe_batch_predictions(Client, Input)
Returns a list of BatchPrediction
operations that match the search
criteria in the request.
describe_batch_predictions(Client, Input, Options)
describe_data_sources(Client, Input)
Returns a list of DataSource
that match the search criteria in the
request.
describe_data_sources(Client, Input, Options)
describe_evaluations(Client, Input)
Returns a list of DescribeEvaluations
that match the search
criteria in the request.
describe_evaluations(Client, Input, Options)
describe_ml_models(Client, Input)
Returns a list of MLModel
that match the search criteria in the
request.
describe_ml_models(Client, Input, Options)
describe_tags(Client, Input)
Describes one or more of the tags for your Amazon ML object.
describe_tags(Client, Input, Options)
get_batch_prediction(Client, Input)
Returns a BatchPrediction
that includes detailed metadata, status,
and data file information for a Batch Prediction
request.
get_batch_prediction(Client, Input, Options)
get_data_source(Client, Input)
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.
get_data_source(Client, Input, Options)
get_evaluation(Client, Input)
Returns an Evaluation
that includes metadata as well as the current
status of the Evaluation
.
get_evaluation(Client, Input, Options)
get_ml_model(Client, Input)
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.
get_ml_model(Client, Input, Options)
predict(Client, Input)
Generates a prediction for the observation using the specified ML
Model
.
predict(Client, Input, Options)
update_batch_prediction(Client, Input)
Updates the BatchPredictionName
of a BatchPrediction
.
GetBatchPrediction
operation to view the contents of the
updated data element.
update_batch_prediction(Client, Input, Options)
update_data_source(Client, Input)
Updates the DataSourceName
of a DataSource
.
GetDataSource
operation to view the contents of the
updated data element.
update_data_source(Client, Input, Options)
update_evaluation(Client, Input)
Updates the EvaluationName
of an Evaluation
.
GetEvaluation
operation to view the contents of the
updated data element.
update_evaluation(Client, Input, Options)
update_ml_model(Client, Input)
Updates the MLModelName
and the ScoreThreshold
of an MLModel
.
GetMLModel
operation to view the contents of the updated
data element.