Summary

In this chapter, we introduced MLflow, and explored some of the motivation behind adopting a ML platform to reduce the time from model development to production in ML development. With the knowledge and experience acquired in this chapter, you can start improving and making your ML development workflow reproducible and trackable.

We delved into each of the important modules of the platform: projects, models, trackers, and model registry. A particular emphasis was given to practical examples to illustrate each of the core capabilities, allowing you to have a hands-on approach to the platform. MLflow offers multiple out-of-the-box features that will reduce friction in the ML development life cycle with minimum code and configuration. Out-of-the-box metrics management, model management, and reproducibility are provided by MLflow.

We will build on this introductory knowledge and expand our skills and knowledge in terms of building practical ML platforms in the rest of the chapters.

We briefly introduced in this chapter the use case of stock market prediction, which will be used in the rest of the book. In the next chapter, we will focus on defining rigorously the ML problem of stock market prediction.