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Use Cases

We provide short articles on common data science scenarios that DVC can help with or improve. Our use cases are not written to be run end-to-end like tutorials. For more general, hands-on experience with DVC, please see Get Started instead.

Why DVC?

Even with all the success we've seen today in machine learning, especially with deep learning and its applications in business, data scientists still lack best practices for organizing their projects and collaborating effectively. This is a critical challenge: while ML algorithms and methods are no longer tribal knowledge, they are still difficult to implement, reuse, and manage.

Basic uses of DVC

If you store and process data files or datasets to produce other data or machine learning models, and you want to

  • track and save data and machine learning models the same way you capture code;
  • create and switch between versions of data and ML models easily;
  • understand how datasets and ML artifacts were built in the first place;
  • compare model metrics among experiments;
  • adopt engineering tools and best practices in data science projects;

DVC is for you!

We keep reviewing our docs and will include interesting scenarios that surface in the community. Please, contact us if you need help or have suggestions!

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