Watson Studio on Cloud Pak for Data as a Service
Watson Studio is one of the core services in Cloud Pak for Data as a Service.
Watson Studio is part of Cloud Pak for Data as a Service and provides the data science capabilities of the data fabric architecture.
Watson Studio provides the environment and tools for you to collaboratively 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 project. A project is a workspace where you organize your resources and work with data.
You can have these types of resources in a project:
- Collaborators are the people on the team who work with the data. Data scientist tasks include analyzing data and building models. Data engineer tasks include preparing and integrating 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.
- Other types of assets that provide components, templates, or other information.
- 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.
- RStudio: Code Jupyter notebooks in R and R Shiny apps.
- SPSS Modeler: Automate the flow of data through a model with SPSS algorithms.
Decision Optimization model builder: Optimize solving business problem scenarios.
Other project tools require additional services. See the lists of supplemental and
- Federated learning: Train models on remote parties without sharing data.
- Pipelines: Automate end-to-end flows of data or models.
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.
- Deployment spaces are provided by the Watson Machine Learning service.
- You can easily move assets between projects and deployment spaces.
|IBM Analytics Engine||Run analytical, machine learning, and Spark API jobs on Apache Spark clusters.|
|Cognos Dashboard Embedded||Use sophisticated visualizations in a project to identify patterns in your data so you can make timely and effective decisions.|
|Watson Knowledge Catalog||Create catalogs of curated assets with this secure enterprise catalog management platform that is supported by a data governance framework.|
|Watson Machine Learning||Build, train, and deploy machine learning models with a full range of tools.|
Compatible data sources
See Connection types for a list of data source services that are compatible.