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Metaflow

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Open-source framework for building, managing, scaling, and deploying real-life ML, AI, and data science projects and workflows.

Adicionado em:13 de jul. de 2026
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open-sourceMLOpsworkflow orchestrationexperiment trackingPythoncloud scaling

Informações da ferramenta

O que é Metaflow?

Metaflow is an open-source framework that enables ML/AI engineers and data scientists to develop robust Python workflows locally, scale them to the cloud with compute and data management, track experiments automatically, and deploy to production without code changes. Originally developed at Netflix and open-sourced in 2019, it is built for real-life ML, AI, and data science projects rather than just machines.

Recursos principais

  • Modeling with any Python libraries

    Use any Python libraries for models and business logic while Metaflow manages library dependencies locally and in the cloud.

  • Workflow orchestration

    Create robust workflows in plain Python that can be developed and debugged locally then deployed to production without changes.

  • Automatic versioning and experiment tracking

    Metaflow tracks and stores variables inside the flow automatically for easy experiment tracking and debugging.

  • Cloud scaling and compute

    Leverage the cloud to execute functions at scale using GPUs, multiple cores, large memory, and multiple instances in parallel.

  • Data access and versioning

    Access data from data warehouses; Metaflow flows data across steps while versioning everything.

  • Production deployment

    Deploy workflows to production with a single command or click and integrate with other systems through events without changing code.

  • Bring your own cloud

    Deploy the Metaflow stack on your own cloud account or on-premise Kubernetes cluster, integrating with existing infrastructure, security, and data governance.

  • Local development and notebooks

    Explore with notebooks, develop with Metaflow, and test and debug locally with automatic result storage and tracking.

  • Recursive and conditional steps

    Support for recursive and conditional steps in flows.

  • Custom decorators and dependency management

    Develop custom decorators and include any PyPI packages; support for uv and configurable Metaflow.

  • Checkpointing and real-time cards

    Checkpointing progress and real-time dynamic cards for monitoring.

  • Programmatic run and deploy

    Run and deploy flows programmatically with one-click local development stack and spin for quick development.

Casos de uso

  • #1

    Real-life ML and AI project management

    Build and manage production ML, AI, and data science projects including GenAI, computer vision, statistics, and operations research.

  • #2

    Experimentation and model development

    Accelerate experimentation, test more models, and develop safely with automatic tracking and local-to-cloud workflows.

  • #3

    Production ML product development

    Codify and orchestrate complex multi-stage workflows for reliable ML products, as used by teams like 23andMe.

  • #4

    Scaling data science processes

    Improve data science processes, accelerate ML within media and other industries, and enable collaboration at scale.

Melhor para:ML/AI engineersData scientistsDevelopers working on demanding real-life ML, AI, and data projects

Planos de preços

Open-source Metaflow makes it quick and easy to build and manage real-life ML, AI, and data science projects. Originally developed and open-sourced by Netflix in 2019.

Alternativas

Perguntas frequentes

What is Metaflow?
Metaflow is an open-source framework for building, managing, scaling, and deploying real-life ML, AI, and data science projects and workflows. It lets you develop robust Python workflows locally, scale them to the cloud, track experiments automatically, and deploy to production without code changes.
Is Metaflow free?
Yes. Metaflow is open source. It was originally developed and open-sourced by Netflix in 2019, making it quick and easy to build and manage real-life ML, AI, and data science projects.
Who is Metaflow designed for?
Metaflow is built for ML/AI engineers, data scientists, and developers working on demanding real-life ML, AI, and data projects.
What platforms and clouds does Metaflow support?
Metaflow supports local development on a laptop or notebook, a browser-based Metaflow Sandbox, AWS, Azure, Google Cloud, Kubernetes, and on-premise deployments. It integrates with services such as EKS, S3, AWS Batch, AWS Step Functions, AKS, Azure Blob Storage, GKE, Google Cloud Storage, data warehouses, notebooks, any PyPI packages, and uv.
Can I use any Python libraries with Metaflow?
Yes. You can use any Python libraries for models and business logic. Metaflow manages library dependencies both locally and in the cloud, and supports including any PyPI packages as well as custom decorators.
How does Metaflow handle experiment tracking and versioning?
Metaflow automatically tracks and stores variables inside the flow for easy experiment tracking and debugging. It also versions data as it flows across steps.

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Informações da ferramenta

Modelo de preçosOpen Source
CategoriaProdutividade
Tags
open-sourceMLOpsworkflow orchestrationexperiment trackingPythoncloud scaling
IdiomaInglês
Última atualização16 de jul. de 2026