Dmytro Dzhulgakov, PyTorch: Bridging AI Research and Production.  

Talking PyTorch is always interesting, as the Facebook ML OSS project is one of the most important parts of the machine learning tooling ecosystem. This week, we talked to Dmytro Dzhulgakov, a tech lead for PyTorch.

We started off talking about Dmytro's journey to being an engineer and tech lead at Facebook, and what his role entails. Dmytro has been at Facebook for 10+ years, so he gave some very interesting advice on how to manage a career in software engineering for the machine learning world. After that, we got deep into the present and future of PyTorch and what improvements the project is making to support MLOps workflows. PyTorch is a large project, and Dmytro shared with us the valuable lessons he learned from confronting multifaceted scaling challenges while working on PyTorch. Finally, we talked about the future of machine learning engineering, especially as relates to how software engineers work by comparison.

// Abstract
Over the past few years, PyTorch became the tool of choice for many AI developers ranging from academia to industry. With the fast evolution of state-of-the-art in many AI domains, the key desired property of the software toolchain is to enable the swift transition of the latest research advances to practical applications.

In this coffee session, Dmytro discusses some of the design principles that contributed to this popularity, how PyTorch navigates inherent tension between research and production requirements, and how AI developers can leverage PyTorch and PyTorch ecosystem projects for bringing AI models to their domain.

// Bio
Dmytro Dzhulgakov is a technical lead of PyTorch at Facebook where he focuses on the framework core development and building the toolchain for bringing AI from research to production.

Previously he was one of the creators of ONNX, a joint initiative aimed at making AI development more interoperable. Before that Dmytro built several generations of large-scale machine learning infrastructure that powered products like Ads or News Feed.

// Relevant Links
https://pytorch.org/
https://pytorch.org/blog/
https://ai.facebook.com/blog/pytorch-builds-the-future-of-ai-and-machine-learning-at-facebook/

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