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If you find yourself repeating sequence of actions to get or update the results of your project, then you may already have a pipeline. For example, a data science workflow could involve:

  1. Gathering data for training and validation
  2. Extracting useful features from the training dataset
  3. (Re)training an ML model
  4. Evaluating the results against the validation set

DVC helps you define these stages in a standard YAML format (.dvc and dvc.yaml files), making your pipeline more manageable and consistent to reproduce.

See Get Started: Data Pipelines for a hands-on introduction to this topic.

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