In short
Edge AI means running AI models on devices close to where data is produced (industrial PCs, gateways, cameras) without sending the data to the cloud.
Edge AI is preferred where low latency, limited connectivity or data privacy matter. Vision-based quality inspection and anomaly detection are typical examples.
Models are usually trained centrally, then deployed to and monitored on edge devices. Managing this lifecycle requires adapting MLOps practices to the edge.