Models Erode, Not Explode: Production ML Monitoring Essentials

ML models gradually degrade in production through data drift and label shifts, quietly converting statistical wins into business losses that customers or auditors detect first. Standard dashboards and metrics miss this decay; what's needed is comprehensive telemetry across business outcomes, model quality, and infrastructure—combined with SLOs, alerts, and automated remediation patterns. This operational mindset transforms ML from one-off releases into sustainable, reliable systems that teams can actually maintain.