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MLOps and tracking experiments with Allegro AI (Practical AI #97)

Podcast |: Changelog Master Feed
Publisher |: Changelog Media
Media Type |: audio
Podknife tags |: Business,; Entrepreneurship,; Interview,; Master Feeds,; Software How-To,; Tech News,; Technology,; Web Development
Categories Via RSS |: Technology
Publication Date |: Jul 20, 2020
Episode Duration |: 00:51:08

Description
Content Encoded

DevOps for deep learning is well... different. You need to track both data and code, and you need to run multiple different versions of your code for long periods of time on accelerated hardware. Allegro AI is helping data scientists manage these workflows with their open source MLOps solution called Trains. Nir Bar-Lev, Allegro's CEO, joins us to discuss their approach to MLOps and how to make deep learning development more robust.

DevOps for deep learning is well… different. You need to track both data and code, and you need to run multiple different versions of your code for long periods of time on accelerated hardware. Allegro AI is helping data scientists manage these workflows with their open source MLOps solution called Trains. Nir Bar-Lev, Allegro’s CEO, joins us to discuss their approach to MLOps and how to make deep learning development more robust.

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Featuring:

Nir Bar-Lev – LinkedIn
Chris Benson – Twitter, GitHub, LinkedIn, Website
Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

Something missing or broken? ai-97.md">PRs welcome!

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