ML systems rarely fail dramatically: they fail quietly. This session cuts through the noise and shows the small set of monitoring signals that prevent the majority of real-world model failures. We’ll build a practical monitoring architecture in Python using Evidently, River, FastAPI, and MLflow, and learn how to detect data drift, feature staleness, performance degradation, pipeline breakage, and silent data shifts. No overcomplexity, no vendor lock-in, just the minimal viable monitoring stack that teams can deploy in days, not months. If you want monitoring that works rather than dashboards that look good, this talk is for you.