Summary
In this chapter, we covered the importance of AutoML and how it can help data scientists get started and become productive with the problem at hand. We then covered the Databricks AutoML glassbox approach, which makes it easy to interpret model results and automatically capture lineage. We also learned how Databricks AutoML is integrated with the MLflow tracking server within the Databricks workspace.
In the next chapters, we will go over managing your ML model’s life cycle using the MLflow model registry and Webhooks in more detail.
