Open-source Metaflow makes it quick and easy to build and manage real-life data science and ML projects.
ModelingUse any Python libraries for models and business logic. Metaflow helps manage library dependencies, locally and in the cloud.
DeploymentDeploy workflows to production with a single command and integrate with other systems through events.
VersioningMetaflow tracks and stores variables inside the flow automatically for easy experiment tracking and debugging.
OrchestrationCreate robust workflows in plain Python. Develop and debug them locally, deploy to production without changes.
ComputeLeverage the cloud to execute functions at scale. Use GPUs, multiple cores, and large amounts of memory as needed.
DataAccess data from data warehouses. Metaflow flows data across steps, versioning everything on the way.
Explore with notebooks, develop with Metaflow, and test and debug locally. Results are stored and tracked automatically for easy analysis.
Break out from the confines of a laptop or a single notebook. Scale out easily to the cloud, utilizing GPUs, multiple cores, and multiple instances in parallel. Metaflow organizes the work for easy collaboration on the way.
Deploy experiments to production with a single click without changing anything in the code. Make flows react to updating data and other events automatically.
Get started easily on a laptop. When you are ready to scale, deploy the Metaflow stack on your cloud account or on-premise Kubernetes cluster. Metaflow integrates seamlessly with your existing infrastructure, security, and data governance policies.
To get a taste of Metaflow in the cloud, try Metaflow Sandbox in the browser.
Metaflow was originally developed at Netflix to address the needs of data scientists who work on demanding real-life data science and ML projects. Netflix open-sourced Metaflow in 2019.
Today, Metaflow is used by hundreds of companies across industries, powering diverse projects from state-of-the-art compute vision and NLP to business-oriented data science, statistics, and operations research.
Our complex, multi-stage workflows are codified and orchestrated using Metaflow.
Our data science team believes they were able to test twice as many models in Q1 2021 as they did in all of 2020.
Metaflow helped us avoid the anti-pattern of needing to push code to find out if something works.
The team has shaved months off the time it takes to build a productionized machine learning model.
Install dependencies from PyPI as well as Conda in your Metaflow steps.
Connect to external services securely using the new @secrets decorator.
Metaflow 2.9 allows you to trigger workflows based on real-time events.
Apache Arrow and Metaflow.S3 make it easy to process data fast.
Learn how to use Metaflow for demanding GPU tasks.
Develop with Metaflow, deploy on your existing Apache Airflow servers.
Deploy and operate Metaflow on GCP and all other major clouds.
Enjoy a clearer learning path, more content.
Deploy and operate Metaflow on Microsoft Azure.
Test Metaflow and the infrastructure behind it in the browser.
Learn the modern ML stack with Metaflow.