How Do I Monitor Inference Health?

In the realm of computer vision, maintaining the health of inference systems is crucial as models often face challenges once deployed due to environmental variables like lighting changes, camera shifts, or data drift. Inference, the process where a trained model makes predictions on new images, requires careful monitoring of latency, uptime, and confidence trends to ensure system reliability and effectiveness. Tracking these metrics helps identify issues such as increased latency, resource saturation, or a decline in prediction confidence, which can indicate problems like data drift or bottlenecks in the pipeline. Roboflow offers tools for monitoring inference health, including dashboards to observe inference activity and alerts for deviations in performance metrics, enabling teams to respond proactively to issues before they affect production. Monitoring practices, such as tracking real-world performance and retraining models with high-value samples, are emphasized as vital for the sustained success of AI systems in production environments.