Model Monitoring

Model monitoring is the process of continuously observing the behavior and prediction results of a machine learning or AI model in a production environment, detecting anomalies and performance degradation. It is an essential element of MLOps (Machine Learning Operations) for maintaining quality not just at deployment, but throughout the operational life of the model. Over time, AI models are exposed to data that differs from what they were trained on, leading to issues such as accuracy degradation, bias drift, and distribution shift (concept drift). Ignoring these problems can lead to flawed decision-making and loss of trust. Model monitoring therefore tracks metrics and behaviors such as: • Changes in prediction accuracy (Accuracy, F1 score, etc.) • Data drift and concept drift (changes in input data or its relationship to ground truth) • Abnormal output patterns (skewed label distribution, excessive confidence, etc.) • Response time and throughput (performance-level monitoring) • User feedback and A/B test results Based on monitoring results, teams decide when to retrigger training, or issue alerts for manual review and model replacement. Representative tools include Evidently AI, Arize AI, WhyLabs, and Prometheus + Grafana (metrics visualization), and they are commonly integrated into MLOps pipelines. Model monitoring is the "maintenance and trust framework" for using AI safely and sustainably in business, and is indispensable in highly regulated industries such as finance and healthcare.