Amazon Machine Learning
API Reference (API Version 2014-12-12)


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.

Request Syntax

{ "ComputeStatistics": boolean, "DataSourceId": "string", "DataSourceName": "string", "DataSpec": { "DataLocationS3": "string", "DataRearrangement": "string", "DataSchema": "string", "DataSchemaLocationS3": "string" } }

Request Parameters

For information about the parameters that are common to all actions, see Common Parameters.

The request accepts the following data in JSON format.


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.

Type: Boolean

Required: No


A user-supplied identifier that uniquely identifies the DataSource.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 64.

Pattern: [a-zA-Z0-9_.-]+

Required: Yes


A user-supplied name or description of the DataSource.

Type: String

Length Constraints: Maximum length of 1024.

Pattern: .*\S.*|^$

Required: No


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}}"

Type: S3DataSpec object

Required: Yes

Response Syntax

{ "DataSourceId": "string" }

Response Elements

If the action is successful, the service sends back an HTTP 200 response.

The following data is returned in JSON format by the service.


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

Type: String

Length Constraints: Minimum length of 1. Maximum length of 64.

Pattern: [a-zA-Z0-9_.-]+


For information about the errors that are common to all actions, see Common Errors.


A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

HTTP Status Code: 400


An error on the server occurred when trying to process a request.

HTTP Status Code: 500


An error on the client occurred. Typically, the cause is an invalid input value.

HTTP Status Code: 400


The following is a sample request and response of the CreateDataSourceFromS3 operation.

Sample Request

POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn-requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.CreateDataSourceFromS3 { "DataSourceId": "exampleDataSourceId", "DataSourceName": "exampleDataSourceName", "DataSpec": { "DataLocationS3": "s3://eml-test-EXAMPLE/data.csv", "DataSchemaLocationS3": "s3://eml-test-EXAMPLE/data.csv.schema", "DataRearrangement": "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" } }

Sample Response

HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"DataSourceId":"exampleDataSourceId"}

See Also

For more information about using this API in one of the language-specific AWS SDKs, see the following: