Edge Impulse and AWS: Combining Edge Inference with Cloud Intelligence for Physical AI

This article describes a hybrid edge-to-cloud architecture combining Edge Impulse's on-device object detection with AWS cloud services for real-time asset tracking in manufacturing and logistics facilities.

• Two-stage cascade inference: lightweight detection runs continuously on edge devices, triggering deeper contextual analysis only when needed
• Edge Impulse YOLO Pro models detect objects; Qwen2-VL 8B vision language models provide contextual descriptions on edge devices
• Amazon Bedrock AgentCore with Amazon Nova Lite provides natural language query interface for workers to ask "Where is the forklift?"
• AWS IoT Core manages device connectivity, fleet telemetry, and MQTT communication across distributed camera devices
• AWS IoT Greengrass deploys and manages Edge Impulse models; Amazon S3 stores images for continuous model retraining
• Pattern extends to production monitoring, license plate recognition, wildlife monitoring, and safety applications

This architecture reduces latency, bandwidth costs, and connectivity dependencies while making AI accessible to frontline workers through natural language interfaces.