Model monitoring

The process of maintaining and managing machine learning models in production by tracking their performance and behavior over time. It involves monitoring key performance metrics, detecting data and concept drift, ensuring acceptable latency and throughput, and setting up alerts for performance degradation. Model monitoring is essential for data scientists and machine learning engineers to ensure models remain effective and reliable in real-world applications.