Controlling Data Flowing through Pipelines

There are a few types of estimators w.r.t. how much data they need to see before "being trained."

- Stateless: No training is required
- Incremental: Can be trained on batches. Additional examples will change the learned parameters
- Full Pass: Must see "all" the data.

Given a pipeline with a mixture of these, we have a choice of how data should flow through the pipeline. With stateless and "full pass" it doesn't really matter; our choice is already made. With an incremental estimator, we can train on the entire dataset before moving to the next estimator, or we can do things in blocks. The "correct" choice will depend on a few things. In an out-of-core context, doing things blockwise will minimize IO, as each block can be loaded and passed through the pipeline.