Dependency Management of Asynchronous Operations

Batch operations in Amazon ML depend on other operations in order to complete successfully. To manage these dependencies, Amazon ML identifies requests that have dependencies, and verifies that the operations have completed. If the operations have not completed, Amazon ML sets the initial requests aside until the operations that they depend on have completed.

There are some dependencies between batch operations. For example, before you can create an ML model, you must have created a datasource with which you can train the ML model. Amazon ML cannot train an ML model if there is no datasource available.

However, Amazon ML supports dependency management for asynchronous operations. For example, you do not have to wait until data statistics have been computed before you can send a request to train an ML model on the datasource. Instead, as soon as the datasource is created, you can send a request to train an ML model using the datasource. Amazon ML does not actually start the training operation until the datasource statistics have been computed. The createMLModel request is put into a queue until the statistics have been computed; once that is done, Amazon ML immediately attempts to run the createMLModel operation. Similarly, you can send batch prediction and evaluation requests for ML models that have not finished training.

The following table shows the requirements to proceed with different AmazonML actions

In order to…	You must have…
Create an ML model (createMLModel)	Datasource with computed data statistics
Create a batch prediction (createBatchPrediction)	Datasource ML model
Create a batch evaluation (createBatchEvaluation)	Datasource ML model