All these object, creation operations are, by default, chained by Amazon ML. This means that Amazon ML will wait on the datasources to be ready before launching the model training, and will also wait for the model training to be completed before trying to run the evaluation. However, at this point, we still need to wait for the evaluation to be complete before we can access its results. Similarly, we need to wait for the different objects to have been utilized by the next operation before deleting them.
This is where the waiter methods become useful. Waiters are methods that simply wait for an AWS operation to be completed, to have status Completed. Waiters exists for all AWS operations and services. Amazon ...
