Metaflow

// Overview

Metaflow is an open-source Python framework, originally built at Netflix and now supported by Outerbounds, for building and managing real-world AI and ML systems. It gives data scientists and engineers a single API that covers the full lifecycle, from quick local prototyping in notebooks to maintainable production deployments.

It is aimed at research and engineering teams who want to move fast without rewriting their code as a project grows. You write your workflow once as a normal Python class, and the same code runs on your laptop or scales out to cloud compute on CPUs and GPUs.

As a compute-orchestration tool in the MLOps space, Metaflow unifies code, data, and compute. It handles experiment tracking, versioning, and visualization automatically, and lets you deploy the same flow to production-grade workflow orchestrators.

// What it does

• Pythonic API for defining workflows as classes with @step-decorated methods, runnable locally with no extra setup
• Built-in experiment tracking, versioning, and result visualization, plus support for notebook-based runs
• Scales horizontally and vertically to cloud compute on CPUs and GPUs, including parallel foreach and gang-scheduled distributed jobs
• Fast data access and reliable handling of failures and checkpointing for data-intensive workloads
• Dependency management and one-click deployment to production orchestrators with event-based reactive orchestration
• Same code runs from laptop to cloud, so prototypes move to production without a rewrite

// Getting started

Install Metaflow from PyPI or conda-forge, then write and run a small flow locally to confirm everything works.

pip install metaflow

from metaflow import FlowSpec, step


class HelloFlow(FlowSpec):
    @step
    def start(self):
        print("HelloFlow is starting.")
        self.next(self.hello)

    @step
    def hello(self):
        print("Metaflow says: Hi!")
        self.next(self.end)

    @step
    def end(self):
        print("HelloFlow is all done.")


if __name__ == "__main__":
    HelloFlow()

python helloworld.py run

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Prototype an ML pipeline in a notebook or on your laptop, then scale the same code to cloud compute as the dataset grows
• Run large embarrassingly parallel or distributed training jobs across CPUs and GPUs with automatic versioning of runs and artifacts
• Deploy a tested workflow to a production orchestrator with scheduled or event-triggered runs
• Track experiments and reproduce past results across a team working on many ML projects

// How Metaflow compares

Metaflow alongside other open-source gpu & compute clouds tools AI/TLDR tracks, ranked by GitHub stars.