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Feature differences between watsonx deployments

Feature differences between watsonx deployments

IBM watsonx as a Service and watsonx on Cloud Pak for Data software have some differences in features and implementation. IBM watsonx as a Service is a set of IBM Cloud services. Watsonx services on Cloud Pak for Data 4.8 are offered as software that you must install and maintain. Services that are available on both deployments also have differences in features on IBM watsonx as a Service compared to watsonx software on Cloud Pak for Data 4.8.

Platform differences

IBM watsonx as a Service and watsonx software on Cloud Pak for Data share a common code base, however, they differ in the following key ways:

Platform differences
Features As a service Software
Software, hardware, and installation IBM watsonx is fully managed by IBM on IBM Cloud. Software updates are automatic. Scaling of compute resources and storage is automatic. You sign up at https://dataplatform.cloud.ibm.com. You provide and maintain hardware. You install, maintain, and upgrade the software. See Software requirements.
Storage You provision a IBM Cloud Object Storage service instance to provide storage. See IBM Cloud Object Storage. You provide persistent storage on a Red Hat OpenShift cluster. See Storage requirements.
Compute resources for running workloads Users choose the appropriate runtime for their jobs. Compute usage is billed based on the rate for the runtime environment and the duration of the job. See Monitor account resource usage. You set up the number of Red Hat OpenShift nodes with the appropriate number of vCPUs. See Hardware requirements and Monitoring the platform.
Cost You buy each service that you need at the appropriate plan level. Many services bill for compute and other resource consumption. See each service page in the IBM Cloud catalog or in the services catalog on IBM watsonx, by selecting Administration > Services > Services catalog from the navigation menu. You buy a software license based on the services that you need. See Cloud Pak for Data.
Security, compliance, and isolation The data security, network security, security standards compliance, and isolation of IBM watsonx are managed by IBM Cloud. You can set up extra security and encryption options. See Security of IBM watsonx. Red Hat OpenShift Container Platform provides basic security features. Cloud Pak for Data is assessed for various Privacy and Compliance regulations and provides features that you can use in preparation for various privacy and compliance assessments. You are responsible for additional security features, encryption, and network isolation. See Security considerations.
Available services Most watsonx services are available in both deployment environments.
See Services for IBM watsonx.
Includes many other services for other components and solutions. See Services for Cloud Pak for Data 4.8.
User management You add users and user groups and manage their account roles and permissions with IBM Cloud Identity and Access Management. See Add users to the account.
You can also set up SAML federation on IBM Cloud. See IBM Cloud docs: How IBM Cloud IAM works.
You can add users and create user groups from the Administration menu. You can use the Identity and Access Management Service or use your existing SAML SSO or LDAP provider for identity and password management. You can create dynamic, attribute-based user groups. See User management.

Common core functionality across services

The following core functionality that is provided with the platform is effectively the same for services on IBM watsonx as a Service and watsonx software on Cloud Pak for Data 4.8:

  • Global search for assets across the platform
  • The Platform assets catalog for sharing connections across the platform
  • Role-based user management within collaborative workspaces across the platform
  • Common infrastructure for assets and workspaces
  • A services catalog for adding services
  • View compute usage from the Administration menu

The following table describes differences in core functionality across services between IBM watsonx as a Service and watsonx software on Cloud Pak for Data 4.8:

Differences in common features across services
Feature As a service Software
Manage all projects Users with the Manage projects permission from the IAM service access Manager role for the IBM Cloud Pak for Data service can join any project with the Admin role and then manage or delete the project. Users with the Manage projects permission can join any project with the Admin role and then manage or delete the project.
Connections to remote data sources Most supported data sources are common to both deployment environments.
See Supported connections.
See Supported data sources.
Connection credentials that are personal or shared Connections in projects and catalogs can require personal credentials or allow shared credentials. Shared credentials can be disabled at the account level. Platform connections can require personal credentials or allow shared credentials. Shared credentials can be disabled at the platform level.
Connection credentials from secrets in a vault Not available Available
Kerberos authentication Not available Available for some services and connections
Sample assets and projects from the Resource hub app Available Not available
Custom JDBC connector Not available Available starting in 4.8.0

Watson Studio

The following Watson Studio features are effectively the same on IBM watsonx as a Service and watsonx software on Cloud Pak for Data 4.8:

  • Collaboration in projects and deployment spaces
  • Accessing project assets programmatically
  • Project import and export by using a project ZIP file
  • Jupyter notebooks
  • Job scheduling
  • Data Refinery
  • Watson Natural Language Processing for Python

This table describes the feature differences between the Watson Studio service on the as-a-service and software deployment environments, differences between offering plans, and whether additional services are required. For more information about feature differences between offering plans on IBM watsonx, see Watson Studio offering plans.

Differences in Watson Studio
Feature As a service Software
Sandbox project Created automatically Not available
Create project Create:
• An empty project
• A project from a sample in the Resource hub
• A project from file
Create:
• An empty project
• A project from file
• A project with Git integration
Git integration • Publish notebooks on GitHub
• Publish notebooks as gist
• Integrate a project with Git
• sync assets to repository in one project and use those assets into another project
Project terminal for advanced Git operations Not available Available in projects with default Git integration
Organize assets in projects with folders Not available Available starting with 4.8.0
Foundation model inferencing Available Requires the watsonx.ai service.
Foundation model tuning Available Not available
Supported foundation models Most foundation models are available on both deployments. See Supported foundation models Requires that the models are installed on the cluster. See Supported foundation models.
AI guardrails for prompting Available Not available
Prompt variables Available Not available
Synthentic data generation Available Requires the Synthetic Data Generator service.
JupyterLab Not available Available in projects with Git integration
Visual Studio Code integration Not available Available
RStudio Cannot integrate with Git Can integrate with Git. Requires an RStudio Server Runtimes service.
Python scripts Not available Work with Python scripts in JupyterLab. Requires a Watson Studio Runtimes service.
Generate code to load data to a notebook by using the Flight service Not available Available
Manage notebook lifecycle Not available Use CPDCTL for notebook lifecycle management
Code package assets (set of dependent files in a folder structure) Not available Use CPDCTL to create code package assets in a deployment space
Promote notebooks to spaces Not available Available manually from the project's Assets page or programmatically by using CPDCTL
Python with GPU Support available for a single GPU type only Support available for multiple Nvidia GPU types. Requires a Watson Studio Runtimes service.
Create and use custom images Not available Create custom images for Python (with and without GPU), R, JupyterLab (with and without GPU), RStudio, and SPSS environments. Requires a Watson Studio Runtimes and other applicable services.
Anaconda Repository Not available Use to create custom environments and custom images
Hadoop integration Not available Build and train models, and run Data Refinery flows on a Hadoop cluster. Requires the Execution Engine for Apache Hadoop service.
Decision Optimization Available Requires the Decision Optimization service.
SPSS Modeler Available Requires the SPSS Modeler service.
Watson Pipelines Available Requires the Watson Pipelines service.

Watson Machine Learning

The following Watson Machine Learning features are effectively the same on IBM watsonx as a Service and watsonx software on Cloud Pak for Data 4.8:

  • Collaboration in projects and deployment spaces
  • Deploy models
  • Deploy functions
  • Watson Machine Learning REST APIs
  • Watson Machine Learning Python client
  • Create online deployments
  • Scale and update deployments
  • Define and use custom components
  • Use Federated Learning to train a common model with separate and secure data sources
  • Monitor deployments across spaces
  • Updated forms for testing online deployment
  • Use nested pipelines
  • AutoAI data imputation
  • AutoAI fairness evaluation
  • AutoAI time series supporting features

This table describes the differences in features between the Watson Machine Learning service on the as-a-service and software deployment environments, differences between offering plans, and whether additional services are required. For details about functionality differences between offering plans on IBM watsonx, see Watson Machine Learning offering plans.

Feature differences between Watson Machine Learning deployments
Feature As a service Software
AutoAI training input Current supported data sources Supported data sources change by release
AutoAI experiment compute configuration 8 CPU and 32 GB Different sizes available
AutoAI limits on data size
and number of prediction targets
Set limits Limits differ by compute configuration
AutoAI incremental learning Not available Available
Deploy using popular frameworks
and software specifications
Check for latest supported versions Supported versions differ by release
Connect to databases for batch deployments Check for support by deployment type Check for support by deployment type
and by version
Deploy and score Python scripts Available via Python client Create scripts in JupyterLab or Python client, then deploy
Deploy and batch score R Scripts Not available Available
Deploy Shiny apps Not available Create and deploy Shiny apps
Deploy from code package
Evaluate jobs for fairness, or drift Requires the Watson OpenScale service Requires the Watson OpenScale service
Evaluate online deployments in a space
for fairness, drift or explainability
Not available Available
Requires the Watson OpenScale service
Evaluate deployed prompt templates in a space Available
Requires the watsonx.governance service
Control space creation No restrictions by role Use permissions to control who can view and create spaces
Import from GIT project to space Not available Available
Code package automatically created when importing
from Git project to space
Not available Available
Update RShiny app from code package Not available Available
Track model details in a model inventory Register models to view factsheets with lifecycle details. Requires the IBM Knowledge Catalog service. Available
Requires the AI Factsheets service.
Create and use custom images Not available Create custom images for Python or SPSS
Notify collaborators about Pipeline events Not available Use Send Mail to notify collaborators
Import project or space file into a nonempty space Not available Available
Deep Learning Experiments Not available Requires the Watson Machine Learning Accelerator service
Provision and manage IBM Cloud service instances Add instances for Watson Machine Learning
or Watson OpenScale
Services are provisioned on the cluster
by the administrator

watsonx.governance

The following governance features are effectively the same on IBM watsonx as a Service and watsonx software on Cloud Pak for Data 4.8:

  • Evaluate deployments for fairness
  • Evaluate the quality of deployments
  • Monitor deployments for drift
  • View and compare model results in an Insights dashboard
  • Add deployments from the machine learning provider of your choice
  • Set alerts to trigger when evaluations fall below a specified threshold
  • Evaluate deployments in a user interface or notebook
  • Custom evaluations and metrics
  • View details about evaluations in model factsheets

This table describes the differences in features between the Watson OpenScale service on the as-a-service and software deployment environments, differences between offering plans, and whether additional services are required.

Differences IBM Watson OpenScale
Feature As a service Software
Upload pre-scored test data Not available Available
IBM SPSS Collaboration and Deployment Services Not available Available
Batch processing Not available Available
Support access control by user groups Not available Available
Free database and Postgres plans Available Postgres available starting in 4.8
Set up multiple instances Not available Available
Integration with OpenPages Not available Available
Evaluation of foundation model assets Available Not available

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Parent topic: Overview of watsonx

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