Practical AI

End-to-end cloud compute for AI/ML

Mar 7, 2023

Erik Bernhardsson, founder of Modal, shares insights on simplifying the often cumbersome transition from local AI development to production. He discusses the limitations of mainstream cloud solutions and how Modal's platform enhances deployment efficiency. The conversation covers serverless workflows for AI applications, focusing on how they help optimize costs and manage dependencies. Erik also highlights the importance of rapid feedback loops in enhancing developer productivity and explores future advancements in cloud AI solutions.

44:19

Creator website

Episode guests

Erik Bernhardsson

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Episode notes

Intro

2min

Navigating Cloud Complexities for AI and ML

10min

Exploring Serverless Workflows for AI and ML Applications

4min

Streamlining AI Workflows in the Cloud

12min

Enhancing Deployment: Speed and Feedback Loops in Software Engineering

3min

Navigating Cloud Integration Challenges

9min

Future Enhancements and Challenges in Cloud AI Solutions

4min

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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Featuring:

Erik Bernhardsson – Website, GitHub, X
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X

Show Notes:

Something missing or broken? PRs welcome!

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