MODEL MONITORING DRIFT, METRICS & RETRAINING

Production ML systems degrade gradually-through data drift, concept drift, misleading metrics, feedback loops, and poorly timed retraining. By the time accuracy drops, damage is already done.
Model Monitoring: Drift, Metrics & Retraining is the definitive, end-to-end guide to building reliable, trustworthy, and production-grade ML systems that survive real-world change.
This book goes far beyond dashboards and alerts. You'll learn how to:Detect silent failure before business or ethical harm occursMonitor data drift, concept drift, fairness, and explainability at scaleDesign SLIs, SLOs, and error budgets for ML systemsDecide when NOT to retrain-and when retraining is dangerousBuild human-in-the-loop, adaptive, and autonomous monitoring systemsOperate ML safely in regulated, high-stakes, and real-time environmentsMonitor LLMs, foundation models, agents, edge AI, and autonomous systemsPacked with real-world case studies, hands-on labs, mini projects, runbooks, and exam-ready insights, this book bridges the gap between theory and production reality.
If you build, deploy, govern, or certify machine learning systems, this book shows you how to keep them safe, fair, reliable, and trustworthy-over time.
Accuracy gets models deployed.
Monitoring keeps them alive.

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