What are the main challenges in integrating IoT hardware with AI models?
The main challenges include orchestrating data flow from edge devices through preprocessing layers to AI inference engines, managing latency and data drift that can corrupt decision-making, and underestimating compute and bandwidth costs at scale. A critical mistake is treating AI models and IoT networks as separate projects, which creates handover failures when data scientists' models break on noisy, incomplete data streams from production sensors.