Machine Learning as a Software Engineering Discipline with Dillon Erb

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

Today we’re joined by Dillon Erb, Co-founder & CEO of Paperspace.

Our conversation with Dillon centers on the challenges that organizations face building and scaling repeatable machine learning workflows, and how they’ve done this in their own platform by applying time-tested software engineering practices.

We also discuss the importance of reproducibility in production machine learning pipelines, how the processes and tools of software engineering map to the machine learning workflow, and technical issues that ML teams run into when trying to scale the ML workflow.

Thanks to our sponsor!

Paperspace Logo
I’d like to send a quick thanks to our friends at Paperspace for their support of the show, and their sponsorship of this episode. I’ve personally been following Paperspace since their origins offering GPU-enabled compute resources to data scientists and machine learning developers. Their latest offering, Paperspace Gradient, is an advanced machine learning platform that helps machine learning teams organize and execute their projects around familiar ideas like cloud notebooks, experiments, models, jobs, deployments, and more. Machine learning teams using Gradient run training and inference on AWS, GCP, Azure, or the Paperspace cloud, and report faster time-to-value when using Gradient’s advanced continuous integration and continuous delivery methodology.

To learn more about Paperspace Gradient, visit When you’re ready to get started with Gradient, send a note to, with TWIML in the subject line to unlock $500 in free credits towards your company’s next enterprise machine learning project.

Connect with Dillon!


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“More On That Later” by Lee Rosevere licensed under CC By 4.0

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