README

Metaflow

Metaflow 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 and now supported by Outerbounds, Metaflow is designed to boost the productivity for research and engineering teams working on a wide variety of projects, 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. 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 that covers foundational needs of AI and ML systems:

Rapid local prototyping, support for notebooks, and built-in support for experiment tracking, versioning and visualization.
Effortlessly scale horizontally and vertically in your cloud, utilizing both CPUs and GPUs, with fast data access for running massive embarrassingly parallel as well as gang-scheduled compute workloads reliably and efficiently.
Easily manage dependencies and deploy with one-click to highly available production orchestrators with built in support for reactive orchestration.

For full documentation, check out our API Reference or see our Release Notes for the latest features and improvements.

Getting started

Getting up and running is easy. If you don't know where to start, Metaflow sandbox will have you running and exploring in seconds.

Installing Metaflow

To install Metaflow in your Python environment from PyPI:

pip install metaflow

Alternatively, using conda-forge:

conda install -c conda-forge metaflow

Once installed, a great way to get started is by following our tutorial. 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:

If you need help, don’t hesitate to reach out on our Slack community!

Deploying infrastructure for Metaflow in your cloud

While you can get started with Metaflow easily on your laptop, the main benefits of Metaflow lie in its ability to scale out to external compute clusters and to deploy to production-grade workflow orchestrators. To benefit from these features, follow this guide to configure Metaflow and the infrastructure behind it appropriately.