Compositional ML and the Future of Software Development with Dillon Erb

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

About This Episode

Today we’re joined by Dillon Erb, CEO of Paperspace.

If you’re not familiar with Dillon, he joined us about a year ago to discuss Machine Learning as a Software Engineering Discipline; we strongly encourage you to check out that interview as well. In our conversation, we explore the idea of compositional AI, and if it is the next frontier in a string of recent game-changing machine learning developments. We also discuss a source of constant back and forth in the community around the role of notebooks, and why Paperspace made the choice to pivot towards a more traditional engineering code artifact model after building a popular notebook service. Finally, we talk through their newest release Workflows, an automation and build system for ML applications, which Dillon calls their “most ambitious and comprehensive project yet.”

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Thanks to our Sponsor!

Today’s episode is brought to you by Gradient, a platform for building and scaling real-world machine learning applications. Gradient is built by our friends at Paperspace, and we are grateful for their continued support of the show and their sponsorship of this episode. I’ve personally been following Paperspace since their origins offering GPU compute in the cloud and many of you have probably tried Gradient’s web-based Jupyter notebooks with free GPUs.

This month they are rolling out two products that I’m really excited about — Gradient Workflows, which is a simple way to automate machine learning tasks, and Gradient Deployments, which is a sophisticated model deployment service. I’m especially excited about Workflows which allows users to create arbitrarily complex ML pipelines using simple Github-actions like syntax. I think of it kind of like Zapier for the machine learning world.

To learn more about Gradient and to check out Notebooks, Workflows, and Deployments, visit and get $15 in free credit toward your next machine learning project.

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