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User Guide

First time using the AWS CLI? See the User Guide for help getting started.

[ aws . machinelearning ]



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.

See also: AWS API Documentation


--data-source-id <value>
[--data-source-name <value>]
--data-spec <value>
[--compute-statistics | --no-compute-statistics]
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]


--data-source-id (string)

A user-supplied identifier that uniquely identifies the DataSource .

--data-source-name (string)

A user-supplied name or description of the DataSource .

--data-spec (structure)

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}}"

Shorthand Syntax:


JSON Syntax:

  "DataLocationS3": "string",
  "DataRearrangement": "string",
  "DataSchema": "string",
  "DataSchemaLocationS3": "string"

--compute-statistics | --no-compute-statistics (boolean)

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.

--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI values will override the JSON-provided values.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.


DataSourceId -> (string)

A user-supplied ID that uniquely identifies the DataSource . This value should be identical to the value of the DataSourceID in the request.