Edge-Inference

Edge AI: Intelligence at the Edge Edge AI brings intelligence close to where data is produced. It runs machine learning models on devices, gateways, or local servers. This arrangement reduces reliance on a distant data center and helps machines react in real time. For many products, it means faster decisions, less network traffic, and stronger privacy. But not every task fits on the edge. Small, efficient models work best; larger networks may still rely on cloud processing for heavy analysis. ...

Edge AI: Bringing Intelligence to the Edge Edge AI means running machine learning directly on devices near data sources, instead of sending everything to a distant cloud. This reduces response time, lowers bandwidth needs, and helps keep data local. For example, a smart camera can detect people on-device, without uploading video to a server. Benefits are clear. Lower latency enables real-time decisions. Offline operation is possible when internet access is unstable. Privacy improves when data stays on the device. Bandwidth savings help when many devices send small updates rather than full streams. ...