Reshaping ML with Compilers feat. Jason Knight | Stanford MLSys Seminar Episode 22

Episode 22 of the Stanford MLSys Seminar Series!
Reshaping the ML software bedrock with compilers
Speaker: Jason Knight
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.
Speaker 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.
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The Stanford MLSys Seminar is hosted by Dan Fu, Karan Goel, Fiodar Kazhamiaka, and Piero Molino, Chris Ré, and Matei Zaharia.
Twitter:
/ realdanfu​
/ krandiash​
/ w4nderlus7
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Check out our website for the schedule: mlsys.stanford.edu
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#machinelearning #ai #artificialintelligence #systems #mlsys #computerscience #stanford #octoml #compilers