Just ran thru official tutorial and I'm pretty impressed.
As I understood the idea is to:
- use git branch for each experiment (change of hyperparameters etc.)
- define pipeline stages (preprocessing, train/test split, model training, model validation)
- after this steps you can change any part of pipeline (say data preprocessing or model parameters) and run `dvc repro` to reproduce all stages for which dependancies changed and track metrics for all branches, which os pretty cool and reduce experiment logs in wiki
Exactly. And it is flexible as Git. You can define your own workflow. For example, some data scientists avoid using git-branches for experiments - they use directories.
