paws.machine.learning: machinelearning_create_data_source_from_s3 – R documentation

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paws.machine.learning

machinelearning_create_data_source_from_s3

Creates a DataSource object

Description

Creates a DataSource object. A DataSource references data that can be used to perform create_ml_model, create_evaluation, or create_batch_prediction operations.

create_data_source_from_s3 is an asynchronous operation. In response to create_data_source_from_s3, 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 create_ml_model, create_evaluation or create_batch_prediction 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 get_data_source 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.

Usage

machinelearning_create_data_source_from_s3(DataSourceId, DataSourceName,
  DataSpec, ComputeStatistics)

Arguments

`DataSourceId`	[required] A user-supplied identifier that uniquely identifies the `DataSource`.
`DataSourceName`	A user-supplied name or description of the `DataSource`.
`DataSpec`	[required] The data specification of a `DataSource`: DataLocationS3 - The Amazon S3 location of the observation data. DataSchemaLocationS3 - The Amazon S3 location of the `DataSchema`. DataSchema - A JSON string representing the schema. This is not required if `DataSchemaUri` is specified. DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the `Datasource`. Sample - `"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"`
`ComputeStatistics`	The compute statistics for a `DataSource`. The statistics are generated from the observation data referenced by a `DataSource`. Amazon ML uses the statistics internally during `MLModel` training. This parameter must be set to `true` if the `DataSource` needs to be used for `MLModel` training.

Value

A list with the following syntax:

list(
  DataSourceId = "string"
)

Request syntax

svc$create_data_source_from_s3(
  DataSourceId = "string",
  DataSourceName = "string",
  DataSpec = list(
    DataLocationS3 = "string",
    DataRearrangement = "string",
    DataSchema = "string",
    DataSchemaLocationS3 = "string"
  ),
  ComputeStatistics = TRUE|FALSE
)

paws.machine.learning

Amazon Web Services Machine Learning Services

v0.1.11

Apache License (>= 2.0)

Authors

David Kretch [aut, cre], Adam Banker [aut], Amazon.com, Inc. [cph]

Initial release

machinelearning_create_data_source_from_s3

Description

Usage

Arguments

Value

Request syntax

paws.machine.learning

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