Agentic Conversations (formally mlops.community)
Human-centric ML Infrastructure: A Netflix Original // Savin Goyal // MLOps Meetup #44
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MLOps community meetup #44! Last Wednesday, we talked to Savin Goyal, Tech Lead for the ML Infra team at Netflix.
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// Abstract:
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.
// Bio:
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.
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Savin on LinkedIn: https://www.linkedin.com/in/savingoyal/
Timestamps:
[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 the 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 in?
[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 have you 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?