watsonx.ai Studio
on Cloud Pak for Data as a Service
Last updated: Nov 26, 2024
watsonx.ai Studio on Cloud Pak for Data as a Service
Description
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IBM
watsonx.ai Studio is one of the core services in
Cloud Pak for Data as a Service.
The watsonx.ai Studio service was formerly known as the Watson Studio service.
watsonx.ai Studio is part of Cloud Pak for Data as a
Service and provides the data science capabilities of the data fabric architecture.
watsonx.ai 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 watsonx.ai 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
watsonx.ai 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
related services.
Federated learning: Train models on remote parties without sharing data.
Pipelines: Automate end-to-end flows of data or models.
watsonx.ai 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 to view and manage model and other types of deployments.
You can easily move assets between projects and deployment spaces.
Table 2. Related services. The following related services are often used with this service and
provide complementary features, but they are not required.
Build, train, and deploy machine learning models with a full range of tools.
Compatible data sources
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See Connectors for a list of data source services that are compatible.
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Cloud Pak for Data relationship map
Use this interactive map to learn about the relationships between your tasks, the tools you need, the services that provide the tools, and where you use the tools.
Select any task, tool, service, or workspace
You'll learn what you need, how to get it, and where to use it.
Some tools perform the same tasks but have different features and levels of automation.
Jupyter notebook editor
Prepare data
Visualize data
Build models
Deploy assets
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
AutoAI
Build models
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
SPSS Modeler
Prepare data
Visualize data
Build models
Create a visual flow that uses modeling algorithms to prepare data and build and train a model, using a guided approach to machine learning that doesn’t require coding.
Decision Optimization
Build models
Visualize data
Deploy assets
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Data Refinery
Prepare data
Visualize data
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Orchestration Pipelines
Prepare data
Build models
Deploy assets
Automate the model lifecycle, including preparing data, training models, and creating deployments.
RStudio
Prepare data
Build models
Deploy assets
Work with R notebooks and scripts in an integrated development environment.
Federated learning
Build models
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deployments
Deploy assets
Monitor models
Deploy and run your data science and AI solutions in a test or production environment.
Catalogs
Catalog data
Governance
Find and share your data and other assets.
Metadata import
Prepare data
Catalog data
Governance
Import asset metadata from a connection into a project or a catalog.
Metadata enrichment
Prepare data
Catalog data
Governance
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Data quality rules
Prepare data
Governance
Measure and monitor the quality of your data.
Masking flow
Prepare data
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Governance
Governance
Create your business vocabulary to enrich assets and rules to protect data.
Data lineage
Governance
Track data movement and usage for transparency and determining data accuracy.
AI factsheet
Governance
Monitor models
Track AI models from request to production.
DataStage flow
Prepare data
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Data virtualization
Prepare data
Create a virtual table to segment or combine data from one or more tables.
OpenScale
Monitor models
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Data replication
Prepare data
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Master data
Prepare data
Consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
Services you can use
Services add features and tools to the platform.
watsonx.ai Studio
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
watsonx.ai Runtime
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
IBM Knowledge Catalog
Discover, profile, catalog, and share trusted data in your organization.
DataStage
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
Data Virtualization
View, access, manipulate, and analyze your data without moving it.
Watson OpenScale
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Data Replication
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Match360 with Watson
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Manta Data Lineage
Increase data pipeline transparency so you can determine data accuracy throughout your models and systems.
Where you'll work
Collaborative workspaces contain tools for specific tasks.
Project
Where you work with data.
> Projects > View all projects
Catalog
Where you find and share assets.
> Catalogs > View all catalogs
Space
Where you deploy and run assets that are ready for testing or production.
> Deployments
Categories
Where you manage governance artifacts.
> Governance > Categories
Data virtualization
Where you virtualize data.
> Data > Data virtualization
Master data
Where you consolidate data into a 360 degree view.