Netflix/metaflow

Build, Manage and Deploy AI/ML Systems

What it solves

Metaflow helps scientists and engineers overcome the friction of moving AI and ML systems from rapid prototyping in notebooks to reliable, maintainable production deployments. It streamlines the entire development lifecycle by unifying code, data, and compute across different stages of development.

How it works

Metaflow provides a Pythonic API that allows users to build "flows" (workflows) that can be started locally and then scaled to the cloud. It handles the essential infrastructure needs of ML systems, such as experiment tracking, versioning, and dependency management. It allows for horizontal and vertical scaling using CPUs and GPUs, supporting both massive parallel compute workloads and gang-scheduled distributed computing.

Who it’s for

Research and engineering teams of all sizes working on a wide variety of projects, ranging from classical statistics to deep learning and foundation models.

Highlights

  • own-click deployment to production orchestrators with support for reactive orchestration.
  • Built-in experiment tracking, versioning, and visualization.
  • Ability to scale compute workloads (CPUs/GPUs) in the cloud effortlessly.
  • Support for notebooks and rapid local prototyping.
  • Fast data access for high-performance compute jobs.

Related

  • Project
    zenml-io/zenml

    An open-source MLOps platform that orchestrates the full lifecycle of AI pipelines and agents, from development to production deployment.

  • Project
    alikon-art/DeterminFlow

    DeterminFlow is an open‑source, AGPL‑licensed framework for building, running, and monitoring production‑grade AI workflows. It breaks complex pipelines into isolated LLM‑Agent nodes, scripts, API calls and human approvals, providing versioning, checkpoint‑based recovery, per‑node token accounting, and a visual editor. The tool is aimed at content generation, data‑centric AI pipelines, and enterprise automation, and can be installed via desktop binaries, run from source, or deployed with Docker.

  • Project
    microsoft/promptflow

    A suite of development tools for streamlining the end-to-end cycle of LLM-based AI applications, from prototyping and prompt engineering to evaluation and production deployment.

  • Project
    airbnb/chronon

    A data platform for AI/ML that abstracts data computation and serving, ensuring point-in-time consistency between offline training data and online real-time feature serving.

  • Project
    OpenDCAI/DataFlow

    DataFlow is an open‑source Python framework that lets you build, run, and share low‑code pipelines for generating, cleaning, and evaluating LLM training data. It provides a library of reusable operators, a visual WebUI, an AI‑assistant for auto‑creating pipelines, and a Ray‑based distributed execution layer.