Model monitoring is the process of tracking an AI or machine learning model after deployment to ensure it performs as expected. It examines prediction accuracy, data changes, behavior, system performance, and bias. Monitoring tools collect metrics and alerts, helping teams identify problems and determine when a model needs investigation, adjustment, or retraining.
For example, a fraud detection model can be monitored for declining accuracy as transaction patterns change. This helps organizations maintain results, detect model drift, reduce risks, and support consistent decisions. Model monitoring is used in finance, healthcare, retail, cybersecurity, and other AI applications.
