Stanford MLSys Seminar Episode 22: Jason Knight [Th, 1-2pm PT]

[Karan Goel]

Hi everyone,

We're back with the twenty-second episode of the MLSys Seminar on Thursday from 1-2pm PT. 

We'll be joined by Jason, who will talk about the role that compilers play in the ML software stack. The format is a 30 minute talk followed by a 30 minute podcast-style discussion, where the live audience can ask questions.

Livestream Link: https://www.youtube.com/watch?v=KN4r_oVpfI0

Guest: Jason Knight

Title: Reshaping the ML software bedrock with compilers
Abstract: The rate of change for ML software, hardware, and algorithms improves our lives daily, but how sturdy are the foundations we rely on? From my experience at one of the first ML accelerator startups (Nervana), applying ML to biology and medicine, leading the ML SW product team at Intel, and then co-founding OctoML I'll describe: 1) The pains of developing ML SW stacks for CPUs, GPUs and accelerators, and how these pains radiate outwards to both practitioners and hardware vendors, 2) How that led me to find the Apache TVM project, what it is, and why it matters, 3) Challenges and opportunities ahead ML compilation and TVM specifically, and what it can enable for ML end users everywhere.

Bio: Jason Knight is co-founder and CPO at OctoML building the machine learning acceleration platform for deploying ML anywhere. From the founders of the Apache TVM project, OctoML uses machine learning to generate efficient binaries for ML model deployment on any hardware. Before starting OctoML, Jason previously drove Intel’s AI software strategy, built large scale human sequencing data pipelines in the biotech industry, and earned a PhD in machine learning and computational biology.

See you all there!

Best,

Karan

[Karan Goel]

Reminder: this is starting in 5 minutes!