Bringing DevOps Agility to ML// Luis Ceze // Coffee Sessions #121
MLOps.community
How to Train a Language Model
For every popular model, you're going to do inference three years of time. So people don't think about this in general. The second thing is, I hope I'm not going to sound like an activist here, but I think that it's important to make these huge computational jobs to train models to be as accessible, as many people as possible.
MLOps Coffee Sessions #121 with Luis Ceze, CEO and Co-founder of OctoML, Bringing DevOps Agility to ML, co-hosted by Mihail Eric.
// Abstract
There's something about this idea where people see a future where you don't need to think about infrastructure. You should just be able to do what you do, and infrastructure happens.
People understand that there is a lot of complexity underneath the hood, and most data scientists or machine learning engineers start deploying things, and shouldn't have to worry about the most efficient way of doing this.
// Bio
Luis Ceze is Co-Founder and CEO of OctoML, which enables businesses to seamlessly deploy ML models to production, making the most out of the hardware. OctoML is backed by Tiger Global, Addition, Amplify Partners, and Madrona Venture Group. Ceze is the Lazowska Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he has taught for 15 years.
Luis co-directs the Systems and Architectures for Machine Learning lab (sampl.ai), which co-authored Apache TVM, a leading open-source ML stack for performance and portability that is used in widely deployed AI applications.
Luis is also co-director of the Molecular Information Systems Lab (misl.bio), which led pioneering research in the intersection of computing and biology for IT applications such as DNA data storage. His research has been featured prominently in the media, including the New York Times, Popular Science, MIT Technology Review, and the Wall Street Journal. Ceze is a Venture Partner at Madrona Venture Group and leads their technical advisory board.
// MLOps Jobs board
MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Landing page: https://octoml.ai/
The Boys in the Boat: Nine Americans and Their Epic Quest for Gold at the 1936 Berlin Olympics by Daniel James Brown:
https://www.amazon.com/Boys-Boat-Americans-Berlin-Olympics/dp/0143125478
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Connect with Luis on LinkedIn: https://www.linkedin.com/in/luis-ceze-50b2314/
Timestamps:
[00:00] Introduction to Luis Ceze
[06:28] MLOps does not exist
[10:41] Semantics argument
[16:25] Parallel programming standpoint
[18:09] TVM
[22:51] Optimizations
[24:18] TVM in the ecosystem
[27:10] OctoML's further step
[30:42] Value chain
[33:58] Mature players
[35:48] Talking to SREs and Machine Learning Engineers
[36:32] Building OctoML
[40:20] My Octopus Teacher
[42:15] Environmental effects of Sustainable Machine Learning
[44:50] Bridging the gap from OctoML to biological mechanisms
[50:02] Programmability
[57:13] Academia making an impact
[59:40] Rapid-fire questions
[1:03:39] Wrap up
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