Monitoring ML Systems

Prerequisites

• Basic understanding of machine learning concepts
• Familiarity with production deployment practices
• Experience with logging and metrics tools

Course Outline

1. 1ML system observability fundamentals and key metrics
2. 2Model performance monitoring and drift detection
3. 3Data quality monitoring and validation pipelines
4. 4Infrastructure and resource monitoring for ML workloads
5. 5Alerting strategies and incident response for ML systems
6. 6Monitoring tools and platforms (Prometheus, Grafana, custom solutions)
7. 7Feature distribution tracking and data pipeline monitoring
8. 8Model explainability and interpretability in production

Learning Outcomes

• Design comprehensive monitoring strategies for ML systems
• Implement drift detection and model performance tracking
• Build alerting pipelines for anomaly detection in production
• Deploy monitoring dashboards using industry-standard tools
• Evaluate data quality and feature distributions at scale
• Troubleshoot production ML issues using observability data