Duration: 50 mins. 20 mins of slides, 20 mins live demo, and 10 mins Q&A.

Content: we briefly present and demonstrate the following frameworks:

1. Edge2Train to enable onboard resource-friendly training of SVM models on MCUs.
2. Train++ for ultra-fast incremental onboard classifier training and inference on MCUs.
3. ML-MCU to train up to 50 class ML classifiers on a $ 3 ESP32 board.

Outcome: The audience would have learned how to make their IoT devices/products self-learn/train on-the-fly, using live IoT use-case data. Thus, their devices can self-learn to perform analytics for any target IoT use cases.