You are building an embedded ML system for a wearable device that decides which activity insights, alerts, and recommendations to surface on device and in the companion app. The system should be modular so models, features, and serving logic can evolve across device generations without rewriting the full stack.
How would you design a modular embedded system?
Multi-stage ML architecture under embedded constraintsRetrieval, ranking, and re-ranking decompositionOn-device versus Garmin Connect Mobile versus cloud servingCold start, feature drift, and monitoring strategy