Understanding inference processors and ingest pipelines

You have a trained machine learning model, so now what? Remember from Chapter 11, Classification Analysis, and Chapter 12, Regression, that one of the exciting things about machine learning models is that they learn from a labeled training dataset and then, in a way, encode the knowledge so that they can be used to make predictions on previously unseen data points. This process of labeling or making predictions for previously unseen data points is what we call inference.

How does this happen in practice in the Elastic Stack?

There are a multitude of different architectures that you might build to make use of inference in the Elastic Stack, but the basic building blocks of all of them are inference processors and ingest pipelines. These are the main subjects of our exploration in this chapter.

An ingest pipeline is a special component that lets you manipulate and transform your data in various ways before it is written...