AI System Observability and Monitoring
Model performance dashboards, data drift alerts, feedback loops, shadow mode deployment.
Why Monitor ML Systems
A deployed model is not done. Traffic spikes, latency creeps up, and the world changes so the data drifts away from training. Observability tells you whether the system is healthy and whether the model is still accurate, before users feel the pain.
Two Kinds of Monitoring
ML observability covers two layers:
- Operational: latency, throughput, errors, resource usage (like any service)
- Model: data drift, prediction distribution, and accuracy over time (ML-specific)
All lessons in this course
- AI System Architecture Patterns
- Scalable ML Pipelines with Airflow
- Feature Stores: Feast and Tecton
- AI System Observability and Monitoring