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Continuous monitoring of machine learning models in production to detect performance degradation, drift, and faulty predictions.
Model monitoring refers to the continuous observation of machine learning models in production to detect performance degradation, data and concept drift, and faulty predictions early. It includes metrics, alerting, explainability checks and retraining triggers, plus processes for root‑cause analysis and governance. The goal is reliable, maintainable model operations.
Tracks performance metrics (e.g. AUC, F1) historically to detect regressions.
Measures changes in input feature distributions versus training data.
Monitors latency and capacity limits of the inference infrastructure.
Production scoring monitors bias, performance regression and data shift relative to training data.
A/B tests combined with drift monitoring ensure relevance and user signal integrity.
Sensor data monitoring detects distribution changes that lead to false alarms or missed events.
Define metrics and SLOs (performance, drift, latency).
Set up telemetry pipelines for features, predictions and labels.
Implement dashboarding, alerting and retraining triggers.
Establish operational processes for incident handling and governance.