When building AI-powered embedded vision solutions, training and deploying AI models is just the beginning. Once deployed, engineers must continuously monitor model behavior to ensure expected performance. However, real-time visibility into model accuracy and behavior in production remains a major challenge. Issues such as network constraints, the scale of edge deployments and nonexpert end users make AI monitoring difficult, especially when diagnosing failures remotely. In this talk, we’ll explore Gimlet’s comprehensive platform for monitoring fleets of edge AI systems. We will show how Gimlet enables engineers to track model performance in production, detect deviations and adapt to changing environmental conditions by sampling important data from deployed devices. We’ll also discuss how the Gimlet architecture handles intermittent network connectivity, how to detect anomalies across edge devices and techniques for training specialized models based on real-world conditions. You’ll learn how Gimlet simplifies edge AI monitoring and ensures robust AI performance across dynamic environments.