Machine learning models do not fail silently — they degrade, drift, and mislead unless continuously monitored. Model Monitoring & Drift Detection is a hands-on guide for ML engineers, SREs, and platform teams responsible for operating AI systems in production. This book focuses on practical observability techniques that detect performance degradation, data drift, and behavioral anomalies before they become business or regulatory incidents. Rather than treating monitoring as a dashboard exercise, the book connects technical signals to operational response and governance escalation. Inside, readers will learn how to: Define meaningful model performance, data quality, and stability metrics Detect data drift, concept drift, and prediction anomalies Design alerting thresholds that balance noise and risk Integrate monitoring outputs with incident response and risk workflows Align model observability with compliance, audit, and executive reporting Build sustainable monitoring practices across the model lifecycle This book is designed for real-world production environments where AI reliability, accountability, and trust matter as much as accuracy.