Meghan is a PM on Qualcomm AI Hub. Born and raised in the Toronto area, she graduated from the University of Waterloo in Management Engineering and has spent her time at companies of various sizes, from big (Apple, Qualcomm) to small startups. She enjoys working with teams of various sizes, ensuring the most effective decision is made, and talking with customers to learn their roadblocks of adopting ML on device.
Meghan Stronach
How to Optimize, Validate and Deploy ML Models On Device (Part II)
We'll walk through the steps to bring your ML model on device. In this hands on section of the workshop we will demonstrate the end to end workflow for a sample use case, using Qualcomm AI Hub to optimize a model and deploy it on device.
We'll then help you get set up and walk through various examples on how to use Qualcomm AI Hub. 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.
Talk Title
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.
We'll walk through how to get started, iterate on your model and meet performance requirements to deploy on device! We'll show examples on how to optimize models and bundle the model into your application.