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.