Supercharging the ML and AI Development Experience at Netflix

**Supercharging the ML and AI Development Experience at Netflix**

Supercharging the ML and AI Development Experience at Netflix with MetaflowShashank Srikanth, Romain CledatMetaflow — a framework we started and open-sourced in 2019 — now powers a wide range of ML and AI systems across Netflix and at many other companies. It is well loved by users for helping them take their ML/AI workflows from prototype to production, allowing them to focus on building cutting-edge systems that bring joy and entertainment to audiences worldwide.Metaflow allows users to:Iterate and ship quickly by minimizing frictionOperate systems reliably in production with minimal overhead, at Netflix scale.Metaflow works with many battle-hardened tooling to address the second point — among them Maestro, our newly open-sourced workflow orchestrator that powers nearly every ML and AI system at Netflix and serves as a backbone for Metaflow itself.In this post, we focus on the first point and introduce a new Metaflow functionality, Spin, that helps users accelerate their iterative development process. By the end, you’ll have a solid understanding of Spin’s capabilities and learn how to try it out yourself with Metaflow 2.19.Iterative development in ML and AI workflowsDeveloping a Metaflow flow with cards in VSCodeTo understand our approach to improving the ML and AI development experience, it helps to consider how these workflows differ from traditional software engineering.ML and AI development revolves not just around code but also around data and models, which are large, mutable, and computationally expensive to process. Iteration cycles can involve long-running data transformations, model training, and stochastic processes that yield slightly different results from run to run. These characteristics make fast, stateful iteration a critical part of productive development.This is where notebooks — such as Jupyter, Observable, or Marimo — shine...

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