Edge processing

Edge processing is a term referring to running AI algorithms and data processing locally on edge devices. This means that the devices are on the “edge” of the network, in close proximity to where the data is generated. This is different than when using centralized cloud servers, where processing occurs somewhere else.
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Edge devices include things like a smartphone, smart camera, or autonomous vehicle. Within the device itself, AI models run locally, doing things like recognizing faces, monitoring vital signs, or understanding voice commands. Edge processing is particularly helpful for processing AI for a few reasons:
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Privacy and security. This is particularly helpful when implementing AI into the health and medical practices, where patient confidentiality is paramount. In these cases, data stays on the device, minimizing the risk of data breaches.
Less bandwidth. When processing is localized, you don’t rely on sending large volumes of data to the cloud, minimizing bandwidth.
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Offline functionality. Edge processing allows for AI implementation without requiring internet access.
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Low latency. With edge processing, outputs are instant, allowing you to get insights and make decisions in real time.