Data and model versioning - Manage large files, datasets, and machine learning models. Track your data and couple its versions to your code versions, while keeping it stored properly outside of your Git repo.
Data pipelines - Use pipelines to describe how models and other data artifacts are built, and provide an efficient way to reproduce them. Think "Makefiles for data and ML projects" done right.
Metrics, parameters, and plots - These are first class citizens in DVC pipelines. Capture, evaluate, and visualize ML projects without leaving Git.
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