Deployment, Drift & Monitoring
Monitoring checks whether data, predictions, performance and operational assumptions remain valid after deployment. The topic is split into focused lessons so definitions, implementation decisions, diagnostics and common failure modes can be learned separately.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Data driftFeature distributions change relative to the development baseline. Drift is a warning signal, not proof of performance degradation. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
02Concept driftThe relationship between inputs and outcomes changes, so historical decision boundaries or regression functions become stale. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
03Performance monitoringWhen labels arrive, track task metrics by time and important segments. Delayed labels require proxy and drift signals in the meantime. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
04Calibration and threshold monitoringProbability calibration and optimal decision thresholds can shift even when ranking remains stable. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
05Retraining governanceDefine triggers, approval, rollback, versioning and reproducible evaluation before automated retraining. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.