AI Monitoring is the continuous observation of an artificial intelligence system after deployment to determine whether it is performing reliably and behaving as expected. It can track prediction quality, response latency, resource usage, data patterns, errors, and changes in model behavior.
Monitoring is especially important because an AI model can degrade as real-world conditions change. By collecting operational and performance signals, teams can identify issues such as model drift, unusual inputs, rising error rates, or infrastructure problems and take corrective action. In production environments, AI monitoring supports reliability, performance management, and ongoing model maintenance.
