In this conversation, Savin talked about some of the challenges encountered and choices made by the Netflix ML Infrastructure team while developing tooling for data scientists.

Savin is an engineer on the ML Infrastructure team at Netflix. He focuses on building generalizable infrastructure to accelerate the impact of data science at Netflix.

https://www.usenix.org/conference/opml20/presentation/cepoi

https://www.youtube.com/watch?v=lakPlz8GJcA&ab_channel=RConsortium

https://www.youtube.com/watch?v=-oMZAS9qfrE&ab_channel=AnalyticsIndiaMagazine

https://www.youtube.com/watch?v=yyWirT279tY&ab_channel=FunctionalTV

https://www.youtube.com/watch?v=QkRJ24Q0E-k&ab_channel=Matroid

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Connect with Savin on LinkedIn: https://www.linkedin.com/in/savingoyal/

[00:00] Background of Savin Goyal

[02:41] Breakdown of Metaflow

[05:44] In the stack, where does Metaflow stand?

[13:23] Where does Metaflow start in Runway Project?

[15:27] What tools or storage does Netflix use for DataOps, ie: the front-end management of data sets and how does that integrate with Metaflow? [18:56] Recommender Systems: Can you explain the other areas that you're using Machine Learning?

[22:27] What do you feel is the hardest part of building an operating  Machine Learning workflow? [28:45] 3 Pillars: Reproducibility, Scalability, Usability.

[36:05] You give so much power to people. How do you keep them from going overboard?

[37:47] Can you explain this Pillar of Usability?

[41:09] Road-based access control has been coming up a lot recently. Does Metaflow do something specific for that?

[44:49] What are some learnings that come across that you didn't have since you open-sourced when you were working at Netflix?

[48:10] What kind of trends you have been seeing? Where do you feel like the market is going?

[50:33] Have you seen some companies really interested in Metaflow? How have you been seeing them combine other tools that are out there?