We have renamed Views to Projects in Iterative Studio.
Iterative Studio creates projects by identifying machine learning models, datasets, metrics and hyperparameters defined in your Git repositories. These values are found in JSON or YAML files in the repository. Additionally, model information may be available as Git tags.
Datasets, metrics, and hyperparameters can be added to a project in two ways:
Set up DVC repositories: You can use DVC and Git to version your code, data and models all within your Git repositories. Data Version Control, or DVC, is a data and ML experiment management tool that takes advantage of the existing engineering toolset that you're already familiar with (Git, CI/CD, etc.). By using DVC, you can be sure not to bloat your repositories with large volumes of data or huge models. These large assets reside in the cloud or other remote storage locations. You will simply track their version info in Git.
DVC also enables you to store and share your data and model files, create data registries, create data pipelines, connect them with CML for CI/CD in machine learning, and so on. Find more about the features and benefits of DVC here.
Refer to the DVC documentation to initialize a DVC repository.
Specify custom files with your metrics and parameters: If you are working with a non-DVC repository, you can add the project provided that the metrics and hyperparameters are stored in JSON or YAML files. For instance, if you have an ML project for which you generate and save metrics either manually or using some ML tracking tools, then you can add this project by specifying the file (within your Git repo) which contains your saved metrics. Refer to the section on project settings to learn how to specify the custom files.