Edge AI runs machine learning inference directly on local hardware (cameras, phones, industrial controllers, embedded boards) instead of sending data to cloud servers. This matters when you need low latency (real-time decisions on a production line), data privacy (medical images that can't leave the hospital), bandwidth savings (thousands of cameras streaming to the cloud is expensive), or offline operation (agricultural drones with no connectivity).

The hardware landscape ranges from low-power microcontrollers (ESP32, Arduino with TFLite Micro) through single-board computers (Raspberry Pi, Google Coral with Edge TPU) to powerful edge GPUs (NVIDIA Jetson Orin, Hailo-8). Each tier supports different model sizes and frame rates. A Jetson Orin can run full YOLO detection at 30+ FPS, while a Coral Edge TPU handles lightweight MobileNet classifiers in single-digit milliseconds.

Getting models onto edge hardware requires optimization: quantization (FP32 to INT8 reduces model size 4x), pruning (removing unimportant weights), knowledge distillation (training a small model to mimic a large one), and framework-specific compilation (TensorRT for NVIDIA, LiteRT/TFLite for ARM, OpenVINO for Intel). Datature's Outpost deploys trained models directly to edge devices, handling the optimization and runtime management automatically.