Why Your AI Models Keep Failing at the Edge (And How to Fix It)
Deploy your first edge AI model by selecting a lightweight framework like TensorFlow Lite or ONNX Runtime, then compress your model through quantization to reduce its size by up to 75% without significant accuracy loss. Test your deployment on a Raspberry Pi or similar device before committing to production hardware, as this reveals real-world performance bottlenecks that cloud testing misses.
Edge AI computing transforms how we build intelligent systems by processing data directly on devices rather than sending it to distant servers. Your smartphone recognizing your face, a security camera detecting package theft, or a factory robot identifying defective parts—these all rely on edge AI. The …










