Iterative Studio works with the data, metrics
and hyperparameters that you add to your ML project Git repositories. It works
very closely with your Git ecosystem. Using DVC and Git, you will push all your
ML experiments to your GitHub, GitLab or Bitbucket repositories as Git commits.
When you connect to these repositories from Iterative Studio, the project's
dvc.yaml is used to identify all the data, metrics and hyperparameters in your
experiments. If you are not using DVC, you can also add the metrics and
hyperparameters to your Git repositories manually.
Iterative Studio then creates a view, which is an interactive, tabular representation of all the identified values. You can also run new experiments from Iterative Studio using your regular CI/CD setup (e.g. GitHub Actions).
This video illustrates how Iterative Studio works closely with your Git ecosystem.