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Supercharging our Data Scientists' Productivity at Netflix

Posted Mar 28
# Data engineering
# Organization and Processes
# Production Use Case
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SPEAKERS
Jan Forjanczyk
Jan Forjanczyk
Jan Forjanczyk
Senior Data Scientist @ Netflix

Jan is a Senior Data Scientist on the Content Demand Modeling team at Netflix, where he supports a variety of content acquisition functions by applying machine learning to offer model driven insights and predictions.

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Jan is a Senior Data Scientist on the Content Demand Modeling team at Netflix, where he supports a variety of content acquisition functions by applying machine learning to offer model driven insights and predictions.

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Ravi Kiran Chirravuri
Ravi Kiran Chirravuri
Ravi Kiran Chirravuri
Software Engineer @ Netflix

Ravi is an individual contributor on the Machine Learning Infrastructure (MLI) team at Netflix. With almost a decade of industry experience, he has been building large scale systems focusing on performance, simplified user journeys and intuitive APIs in MLI and previously Search Indexing and Tensorflow at Google.

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Ravi is an individual contributor on the Machine Learning Infrastructure (MLI) team at Netflix. With almost a decade of industry experience, he has been building large scale systems focusing on performance, simplified user journeys and intuitive APIs in MLI and previously Search Indexing and Tensorflow at Google.

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SUMMARY

Netflix's unique culture affords its data scientists an extraordinary amount of freedom. They are expected to build, deploy, and operate large machine learning workflows autonomously with only limited experience in systems or data engineering. Metaflow, our ML framework (now open-source at metaflow.org), provides them with delightful abstractions to manage their project's lifecycle end-to-end, leveraging the strengths of the cloud: elastic compute and high-throughput storage.

In this talk, we will have one of our data scientists working in Content Demand Modeling present one of the challenges that they faced earlier this year. We will use that as a backdrop to present the human-centric design principles that govern the design of Metaflow and its internals. Finally, we will tie up the presentation outlining the team's experience using Metaflow and the impact of their work.

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