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, 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:
- Access data from connections to your cloud or on-premises data sources
- Access assets from your organization’s catalog
- Cleanse and shape data with the Data Refinery tool
- Analytical assets and tools are how you derive insights from data.Some tools require add-ons. 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.
- Promote models to deployment spaces to configure, monitor, and deploy them.
- Run deep learning model experiments in parallel with neural networks.
You can also bring in data and analytic assets from the IBM WatsonCommunity.
This diagram illustrates how assets move between your catalog, projects, and deployment spaces. You can populate the catalog with assets directly, or you can publish assets from projects. You find assets in the catalog and then add them to any project. When you finish developing models in a project, you promote them to the deployment space that’s associated with the project. You configure and deploy models in the deployment space.
Catalogs
A catalog is a repository of data and analytical assets for your organization. The catalog is provided by Watson Knowledge Catalog, which is included with Watson Studio Local. Use the catalog to share assets between projects. You can move assets into the catalog when you finish with your project, or start working on your project by moving in assets from the catalog.
Watson Studio and Watson Knowledge Catalog are fully integrated:
- You can easily move assets between projects and catalogs.
- Catalogs and projects support the same types of data assets.
You can easily find the assets you need in a catalog. Here’s what you can do:
- Search with keywords and filters that are based on subject tags and other asset properties.
- Look the previews of asset contents to make sure you pick the correct assets.
- Read reviews about assets that are provided by catalog collaborators.
- Choose from recommended assets that are automatically compiled based on your usage history, similar assets, and other factors.
- Choose from the most highly rated assets.
See Catalogs.
Deployment spaces
A deployment space is where you configure and deploy your models. After you finish developing your models in your project, you create a deployment space for that project and move your models into it. Deployment spaces are provided by Watson Machine Learning. Watson Studio and Watson Machine Learning are fully integrated. You can easily move assets between projects and deployment spaces. See Deployment spaces.
Watson Community
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.
To download data sets from the Community, you must sign up for a free Watson Studio account on IBM Cloud and sign in to the community. If you download a notebook from the Community, you must edit some of the code to run it on Watson Studio Local.