Summary

This chapter covered the various deployment options in Databricks for your ML models. We also learned about the multiple deployment paradigms and how you can implement them using the Databricks workspace. The book’s subsequent editions will detail the many new features that Databricks is working on to simplify the MLOps journey for its users.

In the next chapter, we will dive deeper into Databricks Workflows to schedule and automate ML workflows. We will go over how to set up ML training using the Jobs API. We will also take a look at the Jobs API’s integration with webhooks to trigger automated testing for your models when a model is transitioned from one registry stage to another.