Summary

In this chapter, we learned about ML, including the ML process, the personas involved, and the challenges organizations face in productionizing ML models. Then, we learned about the Lakehouse architecture and how the Databricks Lakehouse Platform can potentially simplify MLOps for organizations. These topics give us a solid foundation to develop a more profound understanding of how different Databricks ML-specific tools fit in the ML life cycle.

For in-depth learning about the various features and staying up to date with announcements, the Databricks documentation is the ideal resource. You can access the documentation via the link provided in the Further reading section. Moreover, on the documentation page, you can easily switch to different cloud-specific documentation to explore platform-specific details and functionalities.

In the next chapter, we will dive deeper into the ML-specific features of the Databricks Lakehouse Platform.