AI Performance Monitoring is the continuous measurement of an AI model’s behavior and operational performance after deployment. It evaluates factors such as prediction accuracy, response latency, throughput, error rates, resource consumption, and changes in data patterns to determine whether the system continues to meet its expected performance levels.
Monitoring can reveal issues that are not immediately visible, including model degradation, data drift, unstable response times, or inefficient infrastructure usage. Teams can use these signals to investigate problems, optimize workloads, retrain models, or adjust deployment resources. This makes performance monitoring an important part of maintaining reliable AI applications in production.
