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s2_organisation_and_version_control/dvc.md

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@@ -17,9 +17,20 @@ more data that you feed them, we are seeing models today that are being trained
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Because this is a important concept there exist a couple of frameworks that have specialized in versioning data such as
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[dvc](https://dvc.org/), [DAGsHub](https://dagshub.com/), [Hub](https://www.activeloop.ai/),
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[Modelstore](https://modelstore.readthedocs.io/en/latest/) and [ModelDB](https://github.com/VertaAI/modeldb/). We are
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here going to use `dvc` provided by [iterative.ai](https://iterative.ai/) as they also provide tools for automatizing
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machine learning, which we are going to focus on later.
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[Modelstore](https://modelstore.readthedocs.io/en/latest/) and [ModelDB](https://github.com/VertaAI/modeldb/).
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Regardless of what framework, they all implement somewhat the same concept: instead of storing the actual data files
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or in general storing any large *artifacts* files we instead store a pointer to these large flies. We then version
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control the point instead of the artifact.
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<figure markdown>
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![Image](../figures/git_artifact.png){ width="700" }
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<figcaption>
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<a href="https://madewithml.com/courses/mlops/versioning/"> Image credit </a>
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</figcaption>
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</figure>
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We are in this course going to use `dvc` provided by [iterative.ai](https://iterative.ai/) as they also provide tools
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for automatizing machine learning, which we are going to focus on later.
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## DVC: What is it?
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