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

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.

Leave us a comment

Changelog++ members save 3 minutes on this episode because they made the ads disappear. Join today!

Sponsors:

DigitalOcean – DigitalOcean’s developer cloud makes it simple to launch in the cloud and scale up as you grow. They have an intuitive control panel, predictable pricing, team accounts, worldwide availability with a 99.99% uptime SLA, and 24/7/365 world-class support to back that up. Get your $100 credit at do.co/changelog.
Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com.
Rollbar – We move fast and fix things because of Rollbar. Resolve errors in minutes. Deploy with confidence. Learn more at rollbar.com/changelog.

Featuring:

Nir Bar-Lev – LinkedInChris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel Whitenack – Twitter, GitHub, Website

Show Notes:

Allegro AI
The “Trains” Platform
Trains demo server
Trains video tutorials on YouTube

Something missing or broken? PRs welcome!