Model Monitoring and Drift

Your model isn’t done once deployed—it stays in a changing environment. This lesson shows you how to identify shifts in data, track performance degradation, and build monitoring systems that keep your models reliable over time. Let’s begin.

Managing data drift

Can you explain how you would detect and handle data drift in a deployed machine learning model? Discuss the methods you would use to:

• Identify if drift is affecting input features or target distributions.

• Quantify the extent of the drift.

• Decide whether retraining the model is necessary.

• Address challenges that might arise in this process, and how you would mitigate them.

Sample answer

To detect data drift, I would use these approaches:

• Covariate drift (feature drift): Compare the distributions of input features in the training dataset vs. the live production data. For detection:

  • Use statistical tests like the Kolmogorov-Smirnov (KS) test or the Population Stability Index (PSI). For example, a PSI value above 0.2 may indicate significant drift.

  • Deploy real-time monitoring tools, such as Evidently AI, WhyLabs, or River, to track feature distributions continuously and detect anomalies dynamically.

  • Perform feature importance analysis to identify which features contribute most to the observed drift. This helps in prioritizing mitigation efforts and ...