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, train, and deploy 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 most important resources 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. You customize your project with the tools you need. Here's what you can do to analyze your data:
- Analyze data with Jupyter notebooks or RStudio.
- Build, train, test, and deploy 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.
- Create and share dashboards of data visualizations without coding.
- Classify text by training a model to classify text according to classes you define.
You can also bring in data and analytic assets from the IBM Watson Community.
Ready to go? Get started.
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.
- Data policies are enforced on catalog assets that you add to projects.
The 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 samples, or in your own analytical assets.
Watch this short video to see a tour of the Community section.