Supporting The Full Lifecycle Of Machine Learning Projects With Metaflow - Episode 274

Summary

Netflix uses machine learning to power every aspect of their business. To do this effectively they have had to build extensive expertise and tooling to support their engineers. In this episode Savin Goyal discusses the work that he and his team are doing on the open source machine learning operations platform Metaflow. He shares the inspiration for building an opinionated framework for the full lifecycle of machine learning projects, how it is implemented, and how they have designed it to be extensible to allow for easy adoption by users inside and outside of Netflix. This was a great conversation about the challenges of building machine learning projects and the work being done to make it more achievable.

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Announcements

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  • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
  • Your host as usual is Tobias Macey and today I’m interviewing Savin Goyal about Netflix’s infrastructure for machine learning

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing the work you are doing at Netflix to support their machine learning workloads?
  • How are you addressing the impedance mismatch of machine learning/data science work between local experimentation and production deployment?
  • What was the motivation for building Metaflow?
    • How does Metaflow compare to other tools in the ecosystem such as MLFlow?
    • What was missing in the other available tools that made Metaflow necessary?
  • workflow for someone using Metaflow
  • How do you approach the design of the developer interface to make it approachable to machine learning engineers?
  • level of coupling with overall Netflix data stack
  • How is Metaflow implemented?
    • How has the architecture and design of the system evolved since you first began working on it?
  • supporting infrastructure/integration points
  • motivation/benefits of releasing it as open source
  • What are some of the most interesting, unexpected, or challenging lessons that you have learned while building infrastructure and tooling for machine learning?
  • When is Metaflow the wrong choice?
  • What do you have planned for the future of Metaflow and

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Picks

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Links

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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