Summary

In this chapter, we learned how to set up MLflow to work with either a local MLflow tracking server or a remote MLflow tracking server. Then, we implemented our first DL model with MLflow autologging enabled. This allowed us to explore MLflow in a hands-on way to understand a few central concepts and foundational components such as experiments, runs, metadata about experiments and runs, code tracking, model logging, and model flavor. The knowledge and first-round experiences gained in this chapter will help us to learn more in-depth MLflow tracking APIs in the next chapter.