Chapter 4 Notes: ML Monitoring and Maintenance

Overview

This chapter covers the critical aspects of monitoring ML systems in production and maintaining their performance over time. It discusses various types of monitoring, alerting strategies, and approaches to handle model drift and performance degradation.

Key Concepts

• Model Performance Monitoring: Tracking accuracy, precision, recall, and other metrics
• Data Drift Detection: Identifying when input data distribution changes
• Model Drift: Detecting when model performance degrades over time
• Infrastructure Monitoring: System health, resource utilization, and availability

Main Topics Covered

1. Types of ML monitoring (performance, data, infrastructure)
2. Alerting and notification systems
3. Model retraining strategies
4. Feedback loops and continuous learning
5. Debugging ML systems in production

Common Monitoring Metrics

• Business Metrics: ROI, conversion rates, user engagement
• Model Metrics: Accuracy, precision, recall, F1-score
• System Metrics: Latency, throughput, error rates
• Data Quality Metrics: Missing values, distribution shifts

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