Speaker: Jeff Smith

PyTorch is a powerful, flexible deep learning platform that enables engineers and researchers to move quickly from research to production. Since the 1.0 release a few months ago, researchers and engineers are already seeing success in taking advantage of the new capabilities to take deep learning models from research into production. With the 1.1 release, yet more new features have been released and ecosystem projects launched.    

This talk will cover some of the latest features from PyTorch including the TorchScript JIT compiler, distributed data parallel training, TensorBoard integration, new APIs, and more. We’ll also discuss some of the most exciting projects coming out of the PyTorch ecosystem like BoTorch, Ax, and PyTorch BigGraph. Finally, we’ll dig into some of the use cases and industries where people are successfully taking PyTorch models to production, from cars to cancer treatments.

Throughout the day, we'll have speakers cover how they've adopted applied machine learning to software engineering. The day wraps with a discussion from the speakers on taking an applied, pragmatic approach to adding ML to your systems and how they solved challenges. Eager to deploy ML and have questions? This is a forum to discuss, learn, and help crystalize that roadmap. Join us discussing first principles adding ML to your systems.