Metaflow is a human-friendly library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
For more information, see Metaflow's website and documentation.
Metaflow provides a simple, friendly API that covers foundational needs of ML, AI, and data science projects:
Getting up and running is easy. If you don't know where to start, Metaflow sandbox will have you running and exploring Metaflow in seconds.
To install Metaflow in your local environment, you can install from PyPi:
pip install metaflow
Alternatively, you can also install from conda-forge:
conda install -c conda-forge metaflow
If you are eager to try out Metaflow in practice, you can start with the tutorial. After the tutorial, you can learn more about how Metaflow works here.
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.
An active community of thousands of data scientists and ML engineers discussing the ins-and-outs of applied machine learning.
There are several ways to get in touch with us:
We welcome contributions to Metaflow. Please see our contribution guide for more details.
FAQs
Metaflow: More Data Science, Less Engineering
We found that ob-metaflow demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.