Get hands-on with Metaflow to build and manage real-world ML, AI, and data science projects.
How to use
Start by using notebooks for exploration, then develop with Metaflow and test/debug locally. Scale to the cloud whenever you need. Easily deploy your experiments to production with just one click, no code changes required.
Useful cases
Enhancing the pace of experimentation in MLOps, streamlining data science workflows to foster innovation, and building secure and dependable machine learning products
Core features
Version control, data, compute resources, orchestration, deployment, and modeling.
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