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:
- Gathering data for training and validation
- Extracting useful features from the training dataset
- (Re)training an ML model
- Evaluating the results against the validation set
See Get Started: Data Pipelines for a hands-on introduction to this topic.