model observability

A practical guide to monitoring AI in production: set KPIs (accuracy, latency, cost), watch data/concept drift, fairness and bias, and reliability SLAs. Shows how to instrument models, build dashboards and alerts, run A/B tests, add human review, and trigger retraining. Emphasizes governance, privacy, and MLOps practices to turn monitoring into continuous improvement.