Tuning custom Gemma models for high speed computer vision

This Google I/O interview showcases how Mill deploys custom-tuned Gemma models directly on Nvidia Jetson edge devices to achieve 120-240 FPS computer vision processing for food waste detection. The system handles 5 terabytes of labeled data with edge-to-cloud data loops and local mass estimation, feeding predictive pipelines into enterprise Google Cloud environments to drive agentic procurement workflows for commercial kitchens.