We provide short articles on common data science scenarios where DVC can help.
Our use cases are not written to be run end-to-end like tutorials. For more hands-on experience with DVC, see Get Started.
Even with all the success we've seen 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 develop, 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!
Choose a page from the navigation sidebar to the left.