介绍可配置的 Metaflow
David J. Berg*, David Casler^, Romain Cledat*, Qian Huang*, Rui Lin*, Nissan Pow*, Nurcan Sonmez*, Shashank Srikanth*, Chaoying Wang*, Regina Wang*, Darin Yu**: Model Development Team, Machine Learning Platform
David J. Berg*, David Casler^, Romain Cledat*, Qian Huang*, Rui Lin*, Nissan Pow*, Nurcan Sonmez*, Shashank Srikanth*, Chaoying Wang*, Regina Wang*, Darin Yu**: 模型开发团队，机器学习平台
^: Content Demand Modeling Team
^: 内容需求建模团队
A month ago at QConSF, we showcased how Netflix utilizes Metaflow to power a diverse set of ML and AI use cases, managing thousands of unique Metaflow flows. This followed a previous blog on the same topic. Many of these projects are under constant development by dedicated teams with their own business goals and development best practices, such as the system that supports our content decision makers, or the system that ranks which language subtitles are most valuable for a specific piece of content.
一个月前在 QConSF，我们展示了Netflix 如何利用 Metaflow 支持多样化的 ML 和 AI 用例，管理成千上万个独特的 Metaflow 流。这是继之前在同一主题上的博客之后的又一次展示。许多这些项目由专门团队不断开发，这些团队有自己的业务目标和开发最佳实践，例如支持我们内容决策者的系统，或对特定内容的语言字幕进行排序的系统。
As a central ML and AI platform team, our role is to empower our partner teams with tools that maximize their productivity and effectiveness, while adapting to their specific needs (not the other way around). This has been a guiding design principle with Metaflow since its inception.
作为一个中央的机器学习和人工智能平台团队，我们的角色是为合作团队提供工具，以最大限度地提高他们的生产力和效率，同时适应他们的具体需求（而不是相反）。自 Metaflow 诞生以来，这一直是一个指导设计原则。
Metaflow infrastructure stack
Metaflow 基础设施栈
Standing on the shoulders of our extensive cloud infrastructure, Metaflow facilitates easy access to data, compute, and production-grade workflow orchestration, as well as built-in best practices for common concerns such as collaboration, versioning, dependency management, and observability, which teams use to setup ML/AI experiments and systems that work for them. As a result, Metaflow users at Netflix have been able to run millions of experiments over the past few years without wasting time on low-l...