Versions 3.5.0, 3.5.1, 3.5.2, 3.5.3
Watson Studio provides the environment and tools for you to collaborately
work on data to solve your business problems.
You can choose the tools you need to analyze and visualize data,
to cleanse and shape data, to ingest streaming data, or to create
and train machine learning models.
The architecture of Watson Studio is centered around the analytics project.
Data scientists and business analysts use analytics projects to organize
resources and analyze data. This illustration shows the organization and
interactions of an analytics project for Watson Studio.
You can have these types of resources in a project:
- Collaborators are the people on the team who work with the data.
- Data assets point to your data that is either in uploaded files or accessed
through connections to data sources.
- Operational assets are the objects you create, such as scripts and models,
to run code on data.
- Tools are the software you use to derive insights from data. These tools
are included with the Watson Studio service:
- Data Refinery: Prepare and visualize data.
- Jupyter notebook editor: Code Jupyter notebooks.
- JupyterLab IDE: Code Jupyter notebooks and Python scripts with Git
Other project tools require additional services. See the lists of supplemental and
Watson Studio projects fully integrate with the catalogs and deployment spaces:
- Catalogs are provided by the Watson Knowledge Catalog service
- You can easily move assets between projects and catalogs.
- Catalogs and projects support the same types of data assets.
- Data protection rules are enforced on catalog assets that you add to projects.
- Without the Watson Knowledge Catalog service, you can create one catalog without
any governance capabilities to share assets between analytics projects.
- Deployment spaces are provided by the Watson Machine Learning service
- You can easily move assets between analytics projects and deployment spaces.