Model Drift Detection and Retraining

In the last chapter, we covered various workflow management options available in Databricks for automating machine learning (ML) tasks. Now, we will expand upon our understanding of the ML life cycle up to now and introduce the fundamental concept of drift. We will discuss why model monitoring is essential and how you can ensure your ML models perform as expected over time.

At the time of writing this book, Databricks has a product that is in development that will simplify monitoring model performance and data out of the box. In this chapter, we will go through an example of how to use the existing Databricks functionalities to implement drift detection and monitoring.

We will be covering the following topics:

• Motivation for model monitoring
• Introduction to model drift
• Introduction to Statistical Drift
• Techniques for drift detection
• Implementing drift detection on Databricks

Let’s go through the technical...