Summary

In this chapter, we covered the various components of MLflow and how they work together to make the end-to-end ML project life cycle easy to manage. We learned about MLflow Tracking, Projects, Models, and Model Registry.

This chapter covered some key components of MLFlow and their purpose. Understanding these concepts is essential in effectively managing end-to-end ML projects in the Databricks environment.

In the next chapter, we will look at the AutoML capabilities of Databricks in detail and how we can utilize them to create our baseline models for ML projects.