--- title: "metaflow" type: "tool" slug: "netflix-metaflow" canonical_url: "https://www.graphcanon.com/tools/netflix-metaflow" github_url: "https://github.com/Netflix/metaflow" homepage_url: "https://metaflow.org" stars: 10161 forks: 1305 primary_language: "Python" license: "Apache-2.0" categories: ["developer-tools", "model-training"] tags: ["llmops", "aws", "gcp", "azure", "mlops", "ml-infrastructure", "kubernetes"] updated_at: "2026-07-07T18:34:16.094969+00:00" --- # metaflow > Human-centric framework to build and manage real-life AI/ML systems. Metaflow is a Python-based tool enabling the rapid development, scaling, deployment, and management of AI and ML projects across various cloud environments. It supports teams from initial prototyping through production deployment, offering features like experiment tracking, versioning, and data access optimization for seamless transitions throughout the project lifecycle. ## Facts - Repository: https://github.com/Netflix/metaflow - Homepage: https://metaflow.org - Stars: 10,161 · Forks: 1,305 · Open issues: 467 · Watchers: 286 - Primary language: Python - License: Apache-2.0 - Last pushed: 2026-06-29T22:37:58+00:00 ## Categories - [Developer Tools](/categories/developer-tools.md) - [Model Training](/categories/model-training.md) ## Tags llmops, aws, gcp, azure, mlops, ml-infrastructure, kubernetes ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [JavaGuide](/tools/snailclimb-javaguide.md) - Snailclimb/JavaGuide: 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发 (★ 156,863) - [browser-use](/tools/browser-use-browser-use.md) - 🌐 Make websites accessible for AI agents. Automate tasks online with ease. (★ 103,315) - [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711) - [caveman](/tools/juliusbrussee-caveman.md) - Cuts 65% of tokens in AI coding agent responses. (★ 86,150) ## README (excerpt) ```text # Metaflow [Metaflow](https://metaflow.org) is a human-centric framework designed to help scientists and engineers **build and manage real-life AI and ML systems**. Serving teams of all sizes and scale, Metaflow streamlines the entire development lifecycle—from rapid prototyping in notebooks to reliable, maintainable production deployments—enabling teams to iterate quickly and deliver robust systems efficiently. Originally developed at [Netflix](https://netflixtechblog.com/open-sourcing-metaflow-a-human-centric-framework-for-data-science-fa72e04a5d9) and now supported by [Outerbounds](https://outerbounds.com), Metaflow is designed to boost the productivity for research and engineering teams working on [a wide variety of projects](https://netflixtechblog.com/supporting-diverse-ml-systems-at-netflix-2d2e6b6d205d), from classical statistics to state-of-the-art deep learning and foundation models. By unifying code, data, and compute at every stage, Metaflow ensures seamless, end-to-end management of real-world AI and ML systems. Today, Metaflow powers thousands of AI and ML experiences across a diverse array of companies, large and small, including Amazon, Doordash, Dyson, Goldman Sachs, Ramp, and [many others](ADOPTERS.md). At Netflix alone, Metaflow supports over 3000 AI and ML projects, executes hundreds of millions of data-intensive high-performance compute jobs processing petabytes of data and manages tens of petabytes of models and artifacts for hundreds of users across its AI, ML, data science, and engineering teams. ## From prototype to production (and back) Metaflow provides a simple and friendly pythonic [API](https://docs.metaflow.org) that covers foundational needs of AI and ML systems: 1. [Rapid local prototyping](https://docs.metaflow.org/metaflow/basics), [support for notebooks](https://docs.metaflow.org/metaflow/managing-flows/notebook-runs), and built-in support for [experiment tracking, versioning](https://docs.metaflow.org/metaflow/client) and [visualization](https://docs.metaflow.org/metaflow/visualizing-results). 2. [Effortlessly scale horizontally and vertically in your cloud](https://docs.metaflow.org/scaling/remote-tasks/introduction), utilizing both CPUs and GPUs, with [fast data access](https://docs.metaflow.org/scaling/data) for running [massive embarrassingly parallel](https://docs.metaflow.org/metaflow/basics#foreach) as well as [gang-scheduled](https://docs.metaflow.org/scaling/remote-tasks/distributed-computing) compute workloads [reliably](https://docs.metaflow.org/scaling/failures) and [efficiently](https://docs.metaflow.org/scaling/checkpoint/introduction). 3. [Easily manage dependencies](https://docs.metaflow.org/scaling/dependencies) and [deploy with one-click](https://docs.metaflow.org/production/introduction) to highly available production orchestrators with built in support for [reactive orchestration](https://docs.metaflow.org/production/event-triggering). For full documentation, check out our [API Reference](https://docs.metaflow.org/api) or see our [Release Notes](https://github.com/Netflix/metaflow/releases) for the latest features and improvements. ## Getting started Getting up and running is easy. If you don't know where to start, [Metaflow sandbox](https://outerbounds.com/sandbox) will have you running and exploring in seconds. ### Installing Metaflow To install Metaflow in your Python environment from [PyPI](https://pypi.org/project/metaflow/): ```sh pip install metaflow ``` Alternatively, using [conda-forge](https://anaconda.org/conda-forge/metaflow): ```sh conda install -c conda-forge metaflow ``` Once installed, a great way to get started is by following our [tutorial](https://docs.metaflow.org/getting-started/tutorials). It walks you through creating and running your first Metaflow flow step by step. For more details on Metaflow’s features and best practices, check out: - [How Metaflow works](https://docs.metaf ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/netflix-metaflow`](/api/graphcanon/tools/netflix-metaflow) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_