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.