ML Monitoring in Production
Explore essential ML monitoring methods to detect data and concept drift in production systems. Understand how to use statistical tests like PSI and KS, track prediction shifts, and design alerting strategies that prevent silent model degradation. Gain insights into building reliable monitoring frameworks and on-call playbooks to maintain model health and operational stability.
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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