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Monitoring, Retraining, and Lifecycle
Data drift vs concept drift, performance monitoring when labels are delayed, retraining strategies (scheduled / triggered / continual), incident response and rollback playbooks.
Module Content
Drift Detection — Data Drift vs Concept Drift
Two distinct kinds of drift. Different symptoms, different fixes. FIND_VIDEO: search 'data drift concept drift machine learning' — recommended channel: Evidently / Made with ML. Aim for 10 min or under.
Recap — Detecting and Acting on Drift
Most production ML systems degrade slowly. The slow degradation has two causes; detection is different for each.
Performance Monitoring When Labels Are Delayed
Most real ML labels arrive in days, weeks, or months. Don't wait — use proxies. FIND_VIDEO: search 'ml monitoring delayed labels proxy metrics' — recommended channel: Made with ML / Chip Huyen. Aim for 10 min or under.
Recap — Proxies and Indirect Metrics
When you can't measure accuracy directly, you measure things that correlate with accuracy. Done well, this catches problems before labels arrive.
Retraining Strategies — Scheduled, Triggered, Continual
Three retraining patterns. Pick by your data velocity and drift rate. FIND_VIDEO: search 'retraining strategy scheduled triggered continual learning' — recommended channel: Made with ML / Chip Huyen. Aim for 10 min or under.
Recap — When and How to Retrain
Retraining cadence is a system design decision, not a script-rerun. Each strategy has cost and risk profiles.
Incidents and Rollbacks — When Models Go Wrong
The plan you wish you had before the model started misbehaving in production. FIND_VIDEO: search 'ml incident response model rollback' — recommended channel: Made with ML / Chip Huyen. Aim for 10 min or under.
Recap — Incident Response Playbook for ML
ML incidents are different from software incidents — silent failures, cascading errors. The playbook that handles both.