ML Monitoring in Production
Explore techniques for monitoring machine learning models in production, including data drift detection with PSI and KS tests, concept drift signals, prediction monitoring, and designing alert strategies. Understand how to identify model degradation early and develop operational workflows to ensure system reliability over time.
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The previous lesson established continual learning mechanisms, including online learning, periodic retraining, and champion/challenger promotion, but every one of those strategies assumes you can detect when the model is degrading in the first place. Without that detection capability, a model silently rots in production while serving increasingly unreliable predictions to millions of users.
Consider an Uber ETA model that silently degrades after a city adds new road infrastructure. Offline metrics looked fine at deployment time, but production behavior diverged because the feature distributions shifted in ways the training data never captured. Or consider a fraud detection model whose precision drops because fraudsters shift tactics. The same input features now map to different outcomes. In both cases, no alarm fires. No engineer investigates. Users absorb the damage.
ML monitoring is the critical infrastructure that closes this loop. It operates as four interdependent layers: data drift detection, concept drift detection, prediction monitoring, and alerting strategy. In ML system design interviews, candidates who articulate a monitoring plan demonstrate the production maturity that distinguishes senior-level thinking from textbook answers. This lesson covers concrete statistical methods and operational playbook design, not abstract principles.
Data drift detection methods
Data drift is a change in the input feature distribution