Model Monitoring

Model monitoring is a systematic process in machine learning operations (MLOps) that involves tracking the performance, behavior, and health of deployed machine learning models in production environments. It ensures models continue to deliver accurate and reliable predictions by detecting issues like data drift, concept drift, model degradation, and anomalies. This practice is critical for maintaining model integrity, compliance, and business value over time.

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

Why learn Model Monitoring?

Developers should implement model monitoring when deploying machine learning models to production to prevent performance decay and ensure consistent outcomes, especially in dynamic real-world applications like fraud detection, recommendation systems, or financial forecasting. It is essential for identifying when models need retraining or updates due to changes in input data patterns or business requirements, reducing risks and operational costs.

Learning Resources

• docs: MLOps: Machine Learning Operations - Model Monitoring
• tutorial: A Guide to Model Monitoring in Production
• docs: Model Monitoring with Evidently AI
• course: Coursera: MLOps Fundamentals
• video: Model Monitoring in Machine Learning - YouTube Tutorial