Machine Learning Monitoring

Machine Learning Monitoring is the practice of tracking, measuring, and analyzing the performance and behavior of machine learning models in production to ensure they continue to operate as intended. It involves monitoring metrics like prediction accuracy, data drift, model drift, and system performance to detect issues such as degradation, bias, or failures. This process is critical for maintaining model reliability, compliance, and business value over time.

Also known as: ML Monitoring, Model Monitoring, AI Monitoring, Production ML Monitoring, MLOps Monitoring

Why learn Machine Learning Monitoring?

Developers should learn and implement ML monitoring when deploying models to production, as models can degrade due to changing data patterns, concept drift, or operational issues. It is essential for use cases like fraud detection, recommendation systems, and autonomous systems where model failures can have significant financial or safety impacts. Monitoring helps ensure models remain accurate, fair, and efficient, supporting continuous improvement and regulatory compliance.

Learning Resources

• docs: MLOps: Machine Learning Operations
• tutorial: Monitoring Machine Learning Models in Production
• course: Machine Learning Engineering for Production (MLOps) Specialization
• video: Why You Need to Monitor Your Machine Learning Models
• book: Building Machine Learning Powered Applications