Edge AI explained

Edge AI processes machine learning models directly on user devices like smartphones, IoT sensors, and autonomous vehicles instead of sending data to remote servers. This approach emerged in the mid-2010s with advances in mobile processors and frameworks like TensorFlow Lite. Edge AI relies on specialized hardware (NPUs, GPUs), optimized software frameworks, and communication networks. Key benefits include reduced latency for real-time processing, enhanced privacy by keeping data local, lower bandwidth usage, and better scalability across distributed devices. Applications range from wearables and self-driving cars to industrial automation and smart cities.