Summary

In this chapter, we covered how you can utilize the Databricks Model Registry to manage ML model versioning and life cycles. We also learned how you can manage ML model versioning using the MLflow Model Registry and transition models from one stage to another while managing access control. We then learned how you can use MLflow-supported webhook callbacks to set up automated Slack notifications to track changes around models in your Model Registry.

In the next chapters, we will cover various model deployment approaches.