Summary

In this chapter, we first introduced the Models module in MLflow and the support for different algorithms, from tree-based to linear to neural. We were exposed to the support in terms of the logging and metrics of models and the creation of custom metrics.

In the last two sections, we introduced the Model Registry model and how to use it to implement a model life cycle to manage our models.

In the next chapters and section of the book, we will focus on applying the concepts learned so far in terms of real-life systems and we will architect a machine learning system for production environments.