Table of contents

Watson Studio on Cloud Pak for Data

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 integration. Other project tools require additional services. See the lists of supplemental and related services.

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.

Quick links

Integrated services

Supplemental services
Anaconda Repository for IBM Cloud Pak for Data Control and administer the repository of software packages that data scientists use in Jupyter notebooks.
Decision Optimization Find the most appropriate prescriptive solutions to your business problems by using CPLEX optimization engines to evaluate millions of possibilities.
Jupyter Notebooks with Python 3.7 with GPU Access compute environments for Jupyter notebooks that use GPU-accelerated Python 3.6 libraries.
Execution Engine for Apache Hadoop Integrate the Watson Studio service with your remote Apache Hadoop cluster so you can explore data and build and deploy models on your remote cluster.
Jupyter Notebooks with R 3.6 Access compute environments to create Jupyter Notebooks that use R 3.6 libraries.
RStudio Server with R 3.6 Access the RStudio IDE.
SPSS Modeler Create flows to prepare data, develop and manage models, and visualize data. No coding required.
Watson Machine Learning Build, train, and deploy machine learning models with a full range of tools.
Related services
Cognos Dashboard Identify patterns in your data with sophisticated visualizations. No coding needed.
Data Virtualization Integrate data sources across multiple types and locations into one logical data view.
Analytics Engine Powered by Apache Spark Automatically spin up lightweight, dedicated Apache Spark clusters to run analytical and machine learning jobs.
Streams Develop and run applications that process in-flight data.
Watson Knowledge Catalog Create catalogs of curated assets with this secure enterprise catalog management platform that is supported by a data governance framework.

Compatible data sources

See Supported data sources for a list of data source services that are compatible.