AI at the edge is only as good as the data and training behind it. In this session, we will show you how to take existing AI models and make them perform at their best for your application.
Whether you are working with vision, audio, or other sensor-based applications, you will learn how retraining, transfer learning, and synthetic data generation can push accuracy higher – without starting from scratch!
What we’ll cover:
- Why off-the-shelf models often fail in real-world embedded use-cases
- Application specific dataset collection and preparation
- Using synthetic data to fill training data coverage gaps and improve model robustness
- Deploying optimised, quantised models to Alif’s Ensemble and Balletto MCUs
- Practical steps to evaluate and refine accuracy before deployment
This is a hands-on, engineer-focused guide to building better embedded AI models using proven workflows on the Edge Impulse platform with Alif’s fusion processors.
This webinar is presented by
Henrik Flodell
Alif Semiconductor
Joshua Buck
Edge Impulse
Elliott Lee-Hearn
ipXchange
Free