Model Monitoring & Drift Detection

Summary

Watching a deployed model's performance and input data distribution over time — real-world data shifts (drift) can silently degrade a model that performed well at launch.

• 1Learn the difference between data drift (input distribution changes) and concept drift (the relationship itself changes).
• 2Design a simple monitoring plan: which metrics, checked how often, with what alert threshold.
• 3Read a case study of a model that degraded in production due to drift.