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The analysis of data at the edge of the network, near the source of data generation, reducing latency and bandwidth usage.
Extends Edge AI by performing not only inference but also real-time analytics - aggregations, threshold detections, simple visualizations - on devices or local gateways. Governance considerations include ensuring analytics code integrity, timely model updates, and secure transmission of aggregated results to central monitoring systems.
Real world example:
A smart-city deployment uses edge analytics on traffic sensors to compute vehicle counts per 5-minute intervals and only sends aggregated traffic stats to the central dashboard, reducing data load by 95% and providing city planners with near-instant congestion insights.
