Watson Studio overview
Watson Studio provides you with the environment and tools to solve your business problems by collaboratively working with data. 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.
This illustration shows how the architecture of Watson Studio is centered around the project. A project is where you organize your resources and work with data.
These are the kind of resources you can have in a project:
- Collaborators are the team who works with the data. Three roles provide different permissions.
- Data assets point to your data. Here’s what you can do to prepare your data:
- Analytical assets and tools are how you derive insights from data.Some tools require additional services. Here’s what you can do to analyze your data:
- Analyze data with Jupyter notebooks or RStudio.
- Build, train, and test, and machine learning and deep learning models.
- Run deep learning model experiments in parallel with neural networks.
- Classify images by training deep learning models to recognize image content.
- Classify text by training a model to classify text according to classes you define.
- Create and share dashboards of data visualizations without coding. You can also bring in data and analytic assets from the IBM Watson Community.
Ready to go? Get started.
A catalog is a repository of data and analytical assets for your organization. Catalogs are provided by the Watson Knowledge Catalog app.
Watson Studio and Watson Knowledge Catalog are fully integrated:
- You log in once to access both apps.
- You can easily move assets between projects and catalogs.
- Catalogs and projects support the same types of data assets.
- Policies are enforced on catalog assets that you add to projects. See Watson Knowledge Catalog overview.
The Watson Community contains resources to help you learn and samples that you can use in your project:
- Read articles from many sources to keep current with data science trends.
- Read tutorials for multiple skill levels to learn how to do specific data science tasks.
- Run sample notebooks to learn new techniques or to use as templates for your own notebooks.
- Add sample data sets to your project to analyze in sample or your own analytical assets.
Watch this short video to see a tour of the Community section.