Monitoring and Maintenance
Ensuring model reliability and performance in production.
Chapter 17: Model Monitoring Data drift, concept drift Tools: Evidently AI, Prometheus, Grafana Chapter 18: Model Retraining Triggers: Time-based, performance-based Incremental learning, online learning Chapter 19: Incident Response Root cause analysis Rollback strategies Postmortems for ML failures Chapter 20: Model Retirement Graceful shutdown Data archival Managing technical debt