An edge model refers to an AI or machine learning model that processes data locally on devices or at the “edge” of a network, closer to where the data is generated, rather than relying solely on centralized cloud data centers. This approach is particularly useful for applications involving the Internet of Things (IoT), wearables, sensors, and actuators, where real-time data processing is crucial and bandwidth or latency issues may be a concern.
By performing computations and analytics on the device or near the data source, edge models reduce the need for constant data transmission to the cloud, improving response times, saving bandwidth, and enabling faster decision-making. This is especially valuable in applications like autonomous vehicles, smart home devices, healthcare wearables, and industrial IoT, where quick, local decision-making is necessary for functionality.