How to Optimize, Validate and Deploy ML Models On Device

In this workshop, we address the common challenges faced by developers migrating AI workloads from the cloud to edge devices. Qualcomm aims to democratize AI at the edge, easing the transition to the edge by supporting familiar frameworks and data types.   We'll talk through why ML is best done on device and how to easily select a model for your use case, train (or fine-tune), and then compile for the device of your choice.
You will learn how to get started with the Qualcomm® AI Hub, iterate on your model and meet performance requirements to deploy on device. You’ll learn how to use Qualcomm AI Hub and practice through tangible use cases. The Qualcomm AI Hub team will be there to teach you the ins and outs, enabling you to use the platform and bring your ML use case on device quickly and easily.