End-to-end cloud compute for AI/ML

Practical AI: Machine Learning, Data Science

We’ve all experienced pain moving from local development, to testing, and then on to production. This cycle can be long and tedious, especially as AI models and datasets are integrated. Modal is trying to make this loop of development as seamless as possible for AI practitioners, and their platform is pretty incredible!

Erik from Modal joins us in this episode to help us understand how we can run or deploy machine learning models, massively parallel compute jobs, task queues, web apps, and much more, without our own infrastructure.

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Sponsors:

• 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
• Fly.io – The home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs.

Featuring:

• Erik Bernhardsson – Twitter, GitHub, Website
• Chris Benson – Twitter, GitHub, LinkedIn, Website
• Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

• Modal
• Episode 142 discussing Erik’s “building a data team” article

Something missing or broken? PRs welcome!

Timestamps:

(00:00) - Welcome to Practical AI
(00:42) - Erik Bernhardsson
(02:29) - What got Modal started
(08:55) - What makes Modal different?
(12:13) - Pros and cons of this workflow
(15:36) - What it's like in my experience
(21:37) - Most unexpected uses for Modal
(23:57) - The classic Modal workflow
(31:04) - Tips for migrating into Modal
(34:35) - How will Modal grow?
(37:56) - Will you take Modal to the edge?
(40:03) - Wrap up
(43:24) - Outro