What's new in IBM Software Hub

See what new features and improvements are available in the latest release of IBM® Software Hub.

What's new in Version 5.1

IBM Software Hub Version 5.1 is the new foundation for IBM Cloud Pak® for Data, IBM Data Product Hub, IBM watsonx™, and IBM watsonx Code Assistant™.

IBM Software Hub Version 5.1 introduces a new experience dedicated to administrators and a new component, named IBM Software Hub Control Center, that helps you manage resources across multiple instances of IBM Software Hub.

This release includes several new services:
  • IBM Manta Data Lineage increases data pipeline transparency so that businesses have insight into data accuracy throughout their models and systems.
  • IBM StreamSets enables you to build streaming data pipelines that help you act on time-sensitive data and enhance real-time decision making.
  • watsonx Code Assistant is a generative AI coding companion that provides contextually aware assistance for programming languages such as Go, C, C++, Java, JavaScript, Python, and TypeScript.

The release also includes updates for numerous services, such as Data Product Hub, DataStage, IBM Knowledge Catalog, Orchestration Pipelines, watsonx.ai™, and watsonx.governance™.

For more information, review the information in the following sections:

In addition, review the following topics:

Platform enhancements

The following table lists the new features that were introduced in IBM Software Hub Version 5.1.

What's new What does it mean for me?
The platform has a new name

IBM Software Hub Version 5.1 is the new foundation for IBM Cloud Pak for Data, IBM Data Product Hub, IBM watsonx, and IBM watsonx Code Assistant.

IBM Software Hub provides a standard framework for:
  • Installing and upgrading
  • Backing up and restoring installations
  • Monitoring resource use and installation health
  • Scaling resources

If you are running IBM Cloud Pak for Data Version 4.8 or Version 5.0, you can upgrade to IBM Software Hub Version 5.1. For more information, see Upgrading IBM Software Hub.

Dedicated experience for administrators

When you install IBM Software Hub, you can access the IBM Software Hub Administration Console, which offers a dedicated and streamlined experience for administrators.

The IBM Software Hub Administration Console includes the:
  • Identity provider configuration
  • Access control
  • Configuration and settings
  • Monitoring
  • Storage volumes
When you install services on top of IBM Software Hub, the services are available in one of the following experiences or through service instances:
  • IBM Cloud Pak for Data
  • IBM Data Product Hub
  • IBM watsonx
  • IBM watsonx Code Assistant
Manage multiple instances with IBM Software Hub Control Center

IBM Software Hub Control Center (Control Center) is an optional component that helps you manage resource use across multiple instances of IBM Software Hub that are deployed on the same cluster.

With Control Center, you can create accounts to allocate resources to different groups so that you can see resource use:
  • Across accounts
  • For individual accounts
  • Across instances
  • For individual instances

For more information on IBM Software Hub Control Center, see Installing IBM Software Hub Control Center.

Streamlined process to back up and restore IBM Software Hub with the OADP utility

It is now easier to back up and restore an IBM Software Hub instance when you use the OADP utility. The improved process includes the following changes:

  • You need to run only a single command to create an online or offline backup. The single command does a backup precheck, runs backup prehooks, backs up volumes and Kubernetes resources, runs backup posthooks, and validates the backup.
  • You need to run only a single command to restore a backup. The single command does a restore precheck, restores volumes and Kubernetes resources, runs restore posthooks, and validates the restore.
  • Some tasks that you previously had to do to prepare IBM Software Hub and some services before creating a backup are now automated.
  • Some tasks that you previously had to do to after a restore to make IBM Software Hub and some services fully functional are now automated.
  • You no longer need to manually download the backup script cpd-operators.sh.
  • A new script was added to clean up a cluster before you restore a backup.
Tip: You can still use the IBM Cloud Pak for Data 5.0 backup and restore commands in this release. However, it is recommended that you use the new single commands. The 5.0 commands will be deprecated in a future release.

Service enhancements

The following table lists the new features that are introduced for existing services in IBM Software Hub Version 5.1:

Software Version What does it mean for me?
Cloud Pak for Data common core services 10.0.0
This release of Common core services includes the following features:
Access more data with new connectors
  • Denodo
  • IBM watsonx.data™ Milvus
  • Microsoft Azure PostgreSQL
Integrated service connections
You can now add new connections using the information from existing service instances. This means that parameter values for the new connection can be automatically filled in from the existing instance.
Multi-node writing from the watsonx.data Presto connector
You can now write data from the watsonx.data Presto connector to DataStage using multiple nodes for parallel writes.
New engine connections for the watsonx.data Presto data source
You can now use the Presto (C++) engine with the watsonx.data Presto data source, giving you more options for querying your data.

To read more about these features, see What's new and changed in Common core services in the IBM Cloud Pak for Data documentation.

Version 10.0.0 of the common core services includes various fixes.

For details, see What's new and changed in the common core services.

If you install or upgrade a service that requires the common core services, the common core services will also be installed or upgraded.

Cloud Pak for Data scheduling service 1.40.0

Version 1.40.0 of the scheduling service includes various fixes.

For details, see What's new and changed in the scheduling service.

Related documentation:
AI Factsheets 5.1.0
This release of AI Factsheets includes the following features:
AI Factsheets on IBM Z and LinuxONE

Now you can use AI Factsheets to track machine learning models from request to production in AI use cases on IBM Z and LinuxONE. Use the detailed factsheets to meet your governance and compliance goals.

For details, see Planning for IBM Software Hub on IBM Z and LinuxONE.

To read more about these features, see:

Version 5.1.0 of the AI Factsheets service includes various fixes.

For details, see What's new and changed in AI Factsheets.

Related documentation:
AI Factsheets
Analytics Engine powered by Apache Spark 5.1.0
This release of Analytics Engine powered by Apache Spark includes the following features:
Automatic daily database snapshot backups
IBM Analytics Engine now automatically backs up the metastore database each day. Administrators can restore the database from the snapshots.
For more information, see Automated Backup and Restore.
Improved flexibility when managing Spark environment variables
When configuring your Spark environment variables, you can now decide whether your changes apply to:
  • All Spark instances and jobs
  • A single instance of the Analytics Engine
  • An individual Spark job
IBM Power (ppc64le) supports Spark with R4.3
Spark with R4.3 is supported on IBM Power (ppc64le) starting in 5.1.
Schedule Spark workloads on remote physical locations
You can now install Analytics Engine powered by Apache Spark on a remote physical location so that you can run Spark workloads on remote clusters. This capability is not enabled by default.
For information about how to enable it, see Setting up a remote physical location for IBM Software Hub and Installing Analytics Engine powered by Apache Spark on a remote physical location.

Version 5.1.0 of the Analytics Engine powered by Apache Spark service includes various fixes.

For details, see What's new and changed in Analytics Engine powered by Apache Spark.

Related documentation:
Analytics Engine powered by Apache Spark
Cognos Analytics 27.0.0
This release of Cognos Analytics includes the following features:
Optimize individual pod memory and ephemeral storage
You can now use a script to fine-tune memory and ephemeral storage in pods to improve service performance. For details, see Fine-tuning memory and ephemeral storage in pods.
Save report outputs to file system
You can now use scripts to configure Cognos Analytics to save report outputs to a file system. Report outputs are saved on a persistent volume in the project where the Cognos Analytics service instance is provisioned. For details, see Saving report outputs to file system.
Updated software version
This release of the service provides Version 12.0.4 of the Cognos Analytics software. For details, see Release 12.0.4 in the Cognos Analytics documentation.

Version 27.0.0 of the Cognos Analytics service includes various fixes.

For details, see What's new and changed in Cognos Analytics.

Related documentation:
Cognos Analytics
Cognos Dashboards 5.1.0
This release of Cognos Dashboards includes the following features:
Updated software version
This release of the service provides Version 12.0.4 of the Cognos Analytics dashboards software. For details, see Release 12.0.4 - Dashboards in the Cognos Analytics documentation.

To read more about these features, see What's new and changed in Cognos Dashboards in the IBM Cloud Pak for Data documentation.

Version 5.1.0 of the Cognos Dashboards service includes various fixes.

For details, see What's new and changed in Cognos Dashboards.

Related documentation:
Cognos Dashboards
Data Gate 7.0.0

Version 7.0.0 of the Data Gate service includes various fixes.

For details, see What's new and changed in Data Gate.

Related documentation:
Data Gate
Data Privacy 5.1.0

Version 5.1.0 of the Data Privacy service includes various fixes.

For details, see What's new and changed in Data Privacy.

Related documentation:
Data Privacy
Data Product Hub 5.1.0
This release of Data Product Hub includes the following features:
Monitor your insights with dashboards
Both administrators and data producers can now use the Insights dashboard to monitor their data products and community activity. The Insights dashboard provides a comprehensive, centralized overview of open tasks, delivered data products, and more. By delivering real-time metrics, the Insights dashboard provides detailed data insights at scale and increases workflow efficiency across the data community.
Create custom business domains to organize your data products
Improve your data community's organization and optimize your data products' searchability by creating custom business domains. With new, custom business domains, you can easily organize your data products into intuitive categories and curate your community for your business needs.
Add custom properties to data products
You can now create and add custom properties to data products to optimize searchability, classification, and organization. By adding custom properties, you can structure information and curate your data product to meet specific business needs.
Enhance and update your published data products
To further enhance and improve data products, you can now create new versions of your published data products. When creating a new version, you can edit data assets, change delivery methods, and manage the access level. By continuously creating new versions of published data products, you can help ensure data accuracy and currency.
Pre-approve data consumers for data products requiring approval
For data consumers who frequently access data products requiring approval, you can now create a list of pre-approved users or user groups. By defining a pre-approved list, you streamline the subscription process for both data consumers and producers and improve efficiency in delivering data products.
Send notifying comments for requests for new data products
To ensure that comments do not go unnoticed, you can now send notifying comments for requests for new data products from your task inbox. The approver for a data product request can enter comments or questions for the requester. The requester receives notifications directly in the user interface or by email. The approver is also notified when the requester responds.
Expedite data product approvals with custom approval workflows
Administrators can now create custom approval workflows for data products that require approval. With custom approvals, multiple levels for approving a data product are controlled by a single workflow. Each user in the workflow receives a task and a notification at the appropriate time to ensure all approval levels are met before the data product is delivered. The approval process is based upon a workflow configuration, which is in turn based upon an imported workflow template file.

To read more about these features, see What's new and changed in Data Product Hub in the IBM Data Product Hub documentation.

Version 5.1.0 of the Data Product Hub service includes various fixes.

For details, see What's new and changed in Data Product Hub.

Related documentation:
Data Product Hub
Data Refinery 10.0.0
This release of Data Refinery includes the following features:
Schedule Data Refinery jobs in Git-based projects
You can now schedule jobs for Data Refinery jobs in Git-based projects. You can set up scheduling when you create the job.

Version 10.0.0 of the Data Refinery service includes various fixes.

For details, see What's new and changed in Data Refinery.

Related documentation:
Data Refinery
Data Replication 5.1.0
This release of Data Replication includes the following features:
Audit logging

Data Replication now integrates with the IBM Software Hub audit logging service. Auditable events for Data Replication are forwarded to the security information and event management (SIEM) solution that you integrate with.

Add multiple licenses to your Data Replication service installation

If you have more than one replication license and want to gain access to all of the capabilities provided by your licenses within the same IBM Software Hub instance, you can now add these licenses to your Data Replication service installation as replication extensions. For details, see Extending Data Replication capabilities.

Use an IBM Data Replication Access Server connection for replicating data between remote source and target data stores in your project
You can now replicate data between remote source and target data stores with the Data Replication service by using an IBM Data Replication Access Server connection to a remote Change Data Capture (CDC) replication engines deployment.

For details, see Setting up remote CDC replication.

Use watsonx.data as a target data store for replicating data in your project
You can now replicate data to watsonx.data with the Data Replication service.
Use IBM Db2 for z/OS as a source data store for replicating data in your project
You can now replicate data from IBM Db2 for z/OS with the Data Replication service.
Use IBM Db2 Database as a source data store for replicating data in your project
You can now replicate data from IBM Db2 Database with the Data Replication service.

To read more about these features, see What's new and changed in Data Replication in the IBM Cloud Pak for Data documentation.

Version 5.1.0 of the Data Replication service includes various fixes.

For details, see What's new and changed in Data Replication.

Related documentation:
Data Replication
DataStage 5.1.0
This release of DataStage includes the following features:
Implement version control in your projects by enabling Git integration

Enable Git integration to sync your project with a Git repository. You can clone changes into your project or commit selected changes to the specified repository and branch.

Test your flows by using MettleCI unit testing

Create a test case component to run tests on a DataStage flow. Generate, upload, or intercept data to run tests and compare the expected and actual results.

Run jobs on remote engines by using DataStage Anywhere

Use DataStage Anywhere to run jobs on a remote engine that is deployed in a location of your choice. You can deploy an engine within your own environment, in an on-premises location, or in any cloud or data center.

New user interface for the Transformer stage
You can now enable the new Transformer stage UI in your flow settings to see the stage's visual enhancements and usability improvements. In the new tile-based interface, you can view all elements on one screen, with mapping links between input and output columns. The new interface includes the following enhancements:
  • Zoom in and out
  • Drag and drop columns into position
  • Undo and redo actions
  • Search all elements with find
  • Prompt to save on exit
Manage workloads for your DataStage instance
You can now specify workload management settings, including job limits and memory usage, in the settings of your DataStage instance.
Run the Watsonx.data connector in ELT run mode
You can now run the Watsonx.data connector in ELT mode with SQL Pushdown. Running in ELT mode transforms data directly on the target system and can increase the efficiency of your flows.
Assign the Super operator role to users
You can now assign the Super operator role to users. Users with this role can create and run jobs, but cannot view or edit DataStage assets.

To read more about these features, see What's new and changed in DataStage in the IBM Cloud Pak for Data documentation.

Version 5.1.0 of the DataStage service includes various fixes.

For details, see What's new and changed in DataStage.

Related documentation:
DataStage
Data Virtualization 3.1.0
This release of Data Virtualization includes the following feature:
Speed up schema listings and table counts by configuring your source setup
You can now improve the performance of operations such as listing schemas in the Explore view and counting tables on the Data sources page. Configure whether the service uses a custom query or an API method to list schemas and count tables at the source type level or the Connection Identifier (CID) level.

To read more about these features, see What's new and changed in Data Virtualization in the IBM Cloud Pak for Data documentation.

Version 3.1.0 of the Data Virtualization service includes various fixes.

For details, see What's new and changed in Data Virtualization.

Related documentation:
Data Virtualization
Db2 5.1.0

Version 5.1.0 of the Db2 service includes various fixes.

For details, see What's new and changed in Db2.

Related documentation:
Db2
Db2 Big SQL 7.8.0

Version 7.8.0 of the Db2 Big SQL service includes various fixes.

For details, see What's new and changed in Db2 Big SQL.

Related documentation:
Db2 Big SQL
Db2 Data Management Console 5.1.0

Version 5.1.0 of the Db2 Data Management Console service includes various fixes.

For details, see What's new and changed in Db2 Data Management Console.

Related documentation:
Db2 Data Management Console
Db2 Warehouse 5.1.0

Version 5.1.0 of the Db2 Warehouse service includes various fixes.

For details, see What's new and changed in Db2 Warehouse.

Related documentation:
Db2 Warehouse
Decision Optimization 10.0.0
This release of Decision Optimization includes the following features:
Compare tables in Decision Optimization experiments to see differences between scenarios
You can now compare tables in a Decision Optimization experiment in both the Prepare data or Explore solution view. This comparison shows data value differences between scenarios displayed next to each other.

Decision Optimization table comparison side-by-side with values highlighted

To read more about these features, see:

Version 10.0.0 of the Decision Optimization service includes various fixes and updates.

For details, see What's new and changed in Decision Optimization.

Related documentation:
Decision Optimization
EDB Postgres 12.20, 13.16, 14.13, 15.8, 16.4

This release of the EDB Postgres service includes various fixes.

For details, see What's new and changed in EDB Postgres.

Related documentation:
EDB Postgres
Execution Engine for Apache Hadoop 5.1.0
This release of Execution Engine for Apache Hadoop includes the following features:
Execution Engine for Apache Hadoop now uses Java 11

The Hadoop Execution Engine RPM is now upgraded to use Java 11 instead of Java 8. All of the edge nodes must also be upgraded to Java 11 before you can install the RPM.

For more information, see Installing Execution Engine for Apache Hadoop on Apache Hadoop clusters.

Version 5.1.0 of the Execution Engine for Apache Hadoop service includes various fixes.

For details, see What's new and changed in Execution Engine for Apache Hadoop.

Related documentation:
Execution Engine for Apache Hadoop
IBM Knowledge Catalog 5.1.0
This release of IBM Knowledge Catalog includes the following features:
Enhanced gen AI based enrichment (IBM Knowledge Catalog Premium and IBM Knowledge Catalog Standard)
  • The granite-8b-code-instruct model replaces the previously used granite 13b model for generating asset and column descriptions. The new model provides more accurate results and needs less memory and storage.
  • Business term abbreviations are now taken into account when display names are generated during metadata enrichment. If a source asset or column name matches any defined business term abbreviation, this abbreviation is used to expand the name.
  • In the metadata enrichment results, you can now remove suggested display names or descriptions in bulk.
Enhanced management and scheduling of metadata enrichment jobs
  • You can now configure execution windows for your metadata enrichment jobs to balance workloads. Jobs then run only within the configured time frames.
  • On the new run metrics dashboard, you can monitor the progress of the individual enrichment tasks for an active metadata enrichment job run. In addition, you can explore run information for completed job runs to identify if and where issues occurred.
Enhanced data quality monitoring (IBM Knowledge Catalog and IBM Knowledge Catalog Premium)
You can now better target the data elements for monitoring of data quality:
  • You can now configure data quality SLA rules without asset-level filters. The rules can be applied to any number of columns that have the same name or the same terms assigned, regardless of the containing data asset.
  • You can now select and run data quality SLA rules as part of metadata enrichment. The rules are no longer enabled in the enrichment settings for the project.
Segment data assets by column values to focus on the information you need
You can now chunk data assets into smaller data assets based on selected column values to help you access only the data that you’re interested in. You can work with connected data assets in your project or directly select a data asset and column from a connection in your project without creating a connected data asset first.
Import, enrich, and assess data quality of data from additional data sources
You can now import metadata from Dremio data lakes, enrich that data, and assess its quality.
Simplify the importing of metadata to better understand your data
You can now import metadata by using a new experience that is integrated with IBM Manta Data Lineage service. The metadata import experience process is simplified and provides more lineage import configuration options, which can help you to understand how data flows in more detail.
IBM Knowledge Catalog, IBM Knowledge Catalog Premium, and IBM Knowledge Catalog Standard now store data in a Neo4j graph database
All editions of IBM Knowledge Catalog now use a Neo4j graph database to store lineage and relationship information. Neo4j provides greater data consistency while improving scaling and performance.

For new installations of IBM Knowledge Catalog, IBM Knowledge Catalog Premium, or IBM Knowledge Catalog Standard, the Neo4j graph database is installed automatically with the service.

Neo4j is the graph database that is used with the IBM Manta Data Lineage service. If you want to use the MANTA Automated Data Lineage service as your lineage service or if you want to enable the relationship explorer feature, you can enable the use of FoundationDB instead of Neo4j during installation or upgrade.

For details, see Determining the optional features to enable.

To read more about these features, see What's new and changed in IBM Knowledge Catalog in the IBM Cloud Pak for Data documentation.

Version 5.1.0 of the IBM Knowledge Catalog service includes various fixes.

For details, see What's new and changed in IBM Knowledge Catalog.

Related documentation:
IBM Knowledge Catalog
IBM Match 360 4.3.49
This release of IBM Match 360 includes the following features:
Navigate the service in a new way

You now use a new navigation menu to move between different tools and capabilities within IBM Match 360. The navigation options are improved and reorganized into a single menu. You can minimize the navigation menu to get more screen space to configure, view, and work with your master data. You can also now switch between different data types more easily while configuring or working with your master data.

Additionally, record and entity profiles are now enhanced to give you a clearer view of their associated attribute and relationship details.

IBM Match 360 now stores data in a Neo4j graph database

IBM Match 360 now uses a Neo4j graph database to store your master data records, entities, and relationships. Neo4j provides greater data consistency while improving scaling and performance.

For new installations of IBM Match 360 on Version 5.1, the Neo4j graph database is installed automatically with the service. For upgrades to Version 5.1 from previous releases, IBM Match 360 will continue to use FoundationDB and OpenSearch by default.

Master data entities can now be stored in the database

Data engineers can now configure IBM Match 360 to store and persist entity composite views in the graph database instead of composing them on demand. When an entity type is configured to persist, the composited attributes of each entity get stored in the database similar to the way that record attributes are stored, meaning that entity data is now more stable and resilient.

When entities are configured to persist, data stewards and business users can search directly on entity data, including supplementary attributes, audit attributes, and system properties such as record count and entity ID. Users can search for persisted entities by using the simple or advanced search mechanisms in the master data explorer interface.

As an additional benefit, searches and exports of persisted entity data are faster than was previously possible.

To read more about these features, see What's new and changed in IBM Match 360 in the IBM Cloud Pak for Data documentation.

Version 4.3.49 of the IBM Match 360 service includes various fixes.

For details, see What's new and changed in IBM Match 360.

Related documentation:
IBM Match 360
Informix 8.2.0
This release of Informix includes the following features:
Informix now uses Informix Dynamic Server 15.0
The Informix service has been upgraded to use Informix Dynamic Server 15.0, which is a major release that includes several enhancements in areas such as administration, ease of use, and storage.

Version 8.2.0 of the Informix service includes various fixes.

For details, see Fix list for Informix Server 14.10.xC6 release.

Related documentation:
Informix
MANTA Automated Data Lineage 42.8.0

Version 42.8.0 of the MANTA Automated Data Lineage service includes various fixes.

For details, see What's new and changed in MANTA Automated Data Lineage.

Related documentation:
MANTA Automated Data Lineage
MongoDB 7.0.14-ent, 8.0.0-ent

This release of the MongoDB service include various fixes.

For details, see What's new and changed in MongoDB.

Related documentation:
MongoDB
OpenPages 9.4.0
This release of OpenPages includes the following features:
Use global search to find OpenPages records

You can now use global search in the search panel on the dashboard to find more records relevant to your search terms, and not only exact text matches.

For example, if you search for “management”, the search now finds records that contain all variations of the root word “manage”, such as “management”, “managements”, “manager”, and so on. Search results are ranked in order from most to least relevant.

Version 9.4.0 of the OpenPages service includes various fixes.

For details, see What's new and changed in OpenPages.

Related documentation:
OpenPages
Orchestration Pipelines 5.1.0
This release of Orchestration Pipelines includes the following features:
Streamline pipeline configuration by specifying settings at the project level

You can now configure pipeline settings at the project level to specify how all assets in the project are created. You can still configure settings, such as autosave, cache, and error policy settings, at the asset level. Any settings that you specify for an individual asset override the project settings.

Simplify job selection by adding multiple jobs to your canvas at the same time

You can now add jobs to pipelines in batch. You can drag the node Run multiple jobs from the node panel onto the canvas, then select one or more job assets, such as the Run DataStage job or the Run Pipelines job. All of the selected assets are added to the canvas with one click.

To read more about these features, see:

Version 5.1.0 of the Orchestration Pipelines service includes various fixes.

For details, see What's new and changed in Orchestration Pipelines.

Related documentation:
Orchestration Pipelines
Planning Analytics 5.1.0
This release of Planning Analytics includes the following features:
Back up the Planning Analytics service online

You can now create online backups of the Planning Analytics service by using the backup and restore utilities on IBM Software Hub. Previously, you could create online backups only by using the Planning Analytics administration console.

For more information about how to create online backups of the IBM Software Hub cluster and how to restore from an online backup, see Backing up and restoring IBM Software Hub.

Updated versions of Planning Analytics software
This release of the service provides the following software versions:
  • Planning Analytics Workspace Version 2.0.99

    For details, see 2.0.99 - What's new in the Planning Analytics Workspace documentation.

  • Planning Analytics Spreadsheet Services Version 2.0.99

    For details, see 2.0.99 - Feature updates in the TM1 Web documentation.

  • Planning Analytics for Microsoft Excel Version 2.0.99

    For details, see 2.0.99 - Feature updates in the Planning Analytics for Microsoft Excel documentation.

  • TM1 Database Version 12.4.2 (formerly Planning Analytics Engine).

    For details, see What's new in TM1 Database Version 12 in the TM1 Database Version 12 documentation.

Version 5.1.0 of the Planning Analytics service includes various fixes.

For details, see What's new and changed in Planning Analytics.

Related documentation:
Planning Analytics
Product Master 7.0.0

Version 7.0.0 of the Product Master service includes various fixes.

For details, see What's new and changed in Product Master.

Related documentation:
Product Master
RStudio® Server Runtimes 10.0.0

Version 10.0.0 of the RStudio Server Runtimes service includes various fixes.

For details, see What's new and changed in RStudio Server Runtimes.

Related documentation:
RStudio Server Runtimes
SPSS Modeler 10.0.0
This release of SPSS Modeler includes the following features:
Promote flows to deployment spaces

You can now directly promote SPSS Modeler flows from projects to deployment spaces without having to export the project and then import it into the deployment space.

Analyze Japanese text data in SPSS Modeler with Text Analytics

You can now use the Text Analytics nodes in SPSS Modeler to analyze text data that is written in Japanese. Text Analytics nodes use advanced linguistic technologies and text mining techniques to analyze text data and extract concepts, patterns, and categories.

Connect to new data sources with SPSS Modeler

You can now connect SPSS Modeler to the following new data sources for read and write access:

  • Microsoft Azure Databricks
  • Microsoft Azure Synapse Analytics
Use Kerberos for Apache Impala

You can now use Kerberos for authentication with an Apache Impala connector. However, when using Kerberos authentication, you cannot use SQL Pushback.

To read more about these features, see:

Version 10.0.0 of the SPSS Modeler service includes various fixes.

For details, see What's new and changed in SPSS Modeler.

Related documentation:
SPSS Modeler
Synthetic Data Generator 10.0.0
This release of Synthetic Data Generator includes the following features:
Connect to new data sources with Synthetic Data Generator

You can now connect Synthetic Data Generator to the following new data sources for read and write access:

  • Microsoft Azure Databricks
  • Microsoft Azure Synapse Analytics
Use Kerberos for Apache Impala

You can now use Kerberos for authentication with an Apache Impala connector. However, when using Kerberos authentication, you cannot use SQL Pushback.

For more information, see Creating Synthetic data in the IBM watsonx documentation.

To read more about these features, see What's new and changed in Synthetic Data Generator in the IBM watsonx documentation.

Version 10.0.0 of the Synthetic Data Generator service includes various fixes.

For details, see What's new and changed in Synthetic Data Generator.

Related documentation:
Synthetic Data Generator
Voice Gateway 1.6.0

Version 1.6.0 of the Voice Gateway service includes various fixes.

For details, see What's new and changed in Voice Gateway.

Related documentation:
Voice Gateway
Watson Discovery 5.1.0

Version 5.1.0 of the Watson Discovery service includes various fixes.

For details, see What's new and changed in Watson Discovery.

Related documentation:
Watson Discovery
Watson Machine Learning 5.1.0
This release of Watson Machine Learning includes the following features:
Deploy multi-source SPSS Modeler flows
You can now create deployments for SPSS Modeler flows that use multiple input streams to provide data to the model.
Create deep learning experiments with Watson Machine Learning
You no longer need Watson Machine Learning Accelerator to train deep learning experiments. If you have the IBM Software Hubscheduling service and Watson Machine Learning installed, you can now train a neural network using the Deep learning experiment builder in Watson Studio. Train deeper neural networks and explore more complicated hyperparameter spaces.
New runtime available to deploy AI asset on s390x hardware
You can now use Runtime 24.1 to deploy AI assets on s390x hardware.
To read more about these features, see:

Version 5.1.0 of the Watson Machine Learning service includes various fixes.

For details, see What's new and changed in Watson Machine Learning.

Related documentation:
Watson Machine Learning
Watson OpenScale 5.1.0
This release of Watson OpenScale includes the following features:
Import model deployment configuration settings
When you’re adding deployments to configure evaluations for production models, you can now import the settings from your preproduction model deployment to provide model details.
Configure global explanations with LIME
You can now use the LIME (Local Interpretable Model-Agnostic explanations) algorithm to configure global explanations. To use LIME to configure global explanations, you must enable the global explanation parameter when you configure explainability.
Run quality evaluations with historical data
You can now use an API to evaluate historical feedback data for online deployments and prompt templates. By running quality evaluations with historical data, you can analyze your model performance over time with a wider scope.
To read more about these features, see:

Version 5.1.0 of the Watson OpenScale service includes various fixes.

For details, see What's new and changed in Watson OpenScale.

Related documentation:
Watson OpenScale
Watson Speech services 5.1.0

Version 5.1.0 of the Watson Speech to Text service includes various fixes.

For details, see What's new and changed in Watson Speech to Text.

Related documentation:
Watson Speech services
Watson Studio 10.0.0
This release of Watson Studio includes the following features:
Schedule jobs in Git-based projects

You can now schedule jobs within Git-based projects. You can set up scheduling when you create the job.

Use your IBM watsonx.data Spark engine with Jupyter notebooks in Watson Studio

If you have IBM watsonx.data and Watson Studio provisioned, you can now create new Python environment templates that are based on the Spark engine that runs on your IBM watsonx.data instance. Then you can run your code in a Jupyter notebook directly from the Watson Studio user interface. For more information, see watsonx.data documentation.

To read more about these features, see:

Version 10.0.0 of the Watson Studio service includes various fixes.

For details, see What's new and changed in Watson Studio.

Related documentation:
Watson Studio
Watson Studio Runtimes 10.0.0

Version 10.0.0 of the Watson Studio Runtimes service includes various fixes.

For details, see What's new and changed in Watson Studio Runtimes.

Related documentation:
Watson Studio Runtimes
watsonx.ai 10.0.0
This release of watsonx.ai includes the following features:
Install watsonx.ai on a single node OpenShift® (SNO) cluster
You can now install watsonx.ai in the full service or lightweight engine mode on a single node OpenShift (SNO) cluster if high availability and scalability is not a requirement. For details, see IBM Software Hub platform hardware requirements and Choosing an installation mode in IBM watsonx.ai.
New software specification for deploying custom foundation models
You can now deploy custom foundation models by using the new watsonx-cfm-caikit-1.1 software specification. This software specification is not available with every model architecture.
New model architectures for deploying custom foundation models
You can now deploy custom foundation models from the following model architectures with the vLLM runtime:
  • Bloom
  • Databricks
  • exaone
  • Falcon
  • GPTJ
  • Gemma
  • Gemma2
  • GPT_BigCode
  • GPT_Neox
  • GPTJ
  • GPT2
  • Granite
  • Jais
  • Llama
  • Marlin
  • Mistral
  • Mixtral
  • MPT
  • Nemotron
  • Olmo
  • Persimmon
  • Phi
  • Phi3
  • Qwen2
Deploy custom foundation models on MIG-enabled clusters

You can now deploy custom foundation models on a cluster with Multi-Instance GPU (MIG) enablement. MIG is useful when you want to deploy an application that does not require the full power of an entire GPU.

Review hardware requirements and configure MIG support to deploy your custom foundation models. For more information, see Requirements for deploying custom foundation models on MIG-enabled clusters.

Deploy custom foundation models on specific GPU nodes
You can now deploy custom foundation models on specific GPU nodes when you have multiple GPU nodes available for deployment. Review the process of creating a customized hardware specification to use a specific GPU node for deploying your custom foundation model.
Automate the building of RAG patterns with AutoAI
Use AutoAI to automate the retrieval-augmented generation (RAG) process for a generative AI solution. Upload a collection of documents and transform them into vectors that can be used to improve the output from a large language model. Compare optimized pipelines to select the best RAG pattern for your application.
Screen capture of AutoAI RAG experiment summary
Simplify complex documents by using the text extraction API
Simplify your complex documents so that they can be processed by foundation models as part of a generative AI workflow. The text extraction API uses document understanding processing to extract text from document structures such as images, diagrams, and tables that foundation models often cannot interpret correctly.
Chat with multimodal foundation models about images
Add an image to your prompt and chat about the content of the image with a multimodal foundation model that supports image-to-text tasks. You can chat about images from the Prompt Lab in chat mode or by using the Chat API.
Build conversational workflows with the watsonx.ai chat API
Use the watsonx.ai chat API to add generative AI capabilities, including agent-driven calls to third-party tools and services, into your applications.
Add contextual information to foundation model prompts in Prompt Lab
Help a foundation model generate factual and up-to-date answers in RAG use cases by adding relevant contextual information to your prompt as grounding data. You can upload relevant documents or connect to a third-party vector store that has relevant data. When a new question is submitted, the question is used to query the grounding data for relevant facts. The top search results plus the original question are submitted as model input to help the foundation model incorporate relevant facts in its output.
Work with new foundation models in Prompt Lab
You can now use the following foundation models for inferencing from the API and from the Prompt Lab in watsonx.ai:
  • Granite Guardian 3.0 models in 2 billion and 8 billion parameter sizes
  • Granite Instruct 3.0 models in 2 billion and 8 billion parameter sizes
  • granite-20b-code-base-sql-gen
  • granite-20b-code-base-schema-linking
  • codestral-22b
  • Llama 3.2 Instruct models in 1 billion and 3 billion parameter sizes
  • Llama 3.2 Vision Instruct models in 11 billion and 90 billion parameter sizes
  • llama-guard-3-11b-vision
  • mistral-small
  • ministral-8b
  • pixtral-12b

For details, see Foundation models in watsonx.ai.

Work with new embedding models for text matching and retrieval tasks
You can now use the following embedding models to vectorize text in watsonx.ai:
  • all-minilm-l12-v2

For details, see Foundation models in watsonx.ai.

Enhance search and retrieval tasks with the text rerank API
Use the text rerank method in the watsonx.ai REST API together with the ms-marco-minilm-l-12-v2 reranker model to reorder a set of document passages based on their similarity to a specified query. Reranking adds precision to your answer retrieval workflows.

For details about the ms-marco-minilm-l-12-v2 model, see Foundation models in watsonx.ai.

Review benchmarks that show how foundation models perform on common tasks

Review foundation model benchmarks to learn about the capabilities of foundation models deployed in watsonx.ai before you try them out. Compare how various foundation models perform on the tasks that matter most for your use case.

Configure AI guardrails for user input and foundation model output separately in Prompt Lab
Adjust the sensitivity of the AI guardrails that find and remove harmful content when you experiment with foundation model prompts in Prompt Lab. You can set different filter sensitivity levels for user input and model output text, and you can save effective AI guardrails settings in prompt templates.

To read more about these features, see What's new and changed in watsonx.ai in the IBM watsonx documentation.

Related documentation:
watsonx.ai
watsonx Assistant 5.1.0
This release of watsonx Assistant includes the following features:
Conversational search analytics
You can now analyze the performance of your conversational search by using conversational search analytics. For more information, see Conversational search analytics.
Debug conversational search with Inspector
You can now debug conversational search issues using the Inspector in your watsonx Assistant. For more information, see Debugging failures for Conversational search or skill based actions.
Respond using a table format
The watsonx Assistant web chat can now provide responses in table format. The table response type is only available in the web chat. For more information, see Response types reference.
Pass values to skill-based actions
watsonx Assistant users can now pass values to skill-based actions within the conversation flow. To enable this feature, external skill providers must implement the new endpoint for skill-based actions. For more information, see Passing values to a subaction.

Version 5.1.0 of the watsonx Assistant service includes various security fixes.

For details, see What's new and changed in watsonx Assistant.

Related documentation:
watsonx Assistant
watsonx Code Assistant for Red Hat® Ansible® Lightspeed 5.1.0
Related documentation:
watsonx Code Assistant for Red Hat Ansible Lightspeed
watsonx Code Assistant for Z 5.1.0
Related documentation:
watsonx Code Assistant for Z
watsonx.data 2.1.0
This release of watsonx.data includes the following features:
Data sources, catalogs, and storage enhancements
This release includes the following data sources, catalogs, and storage enhancements:
  • Now, you can connect to Apache Phoenix data sources. For more information, see Apache Phoenix.
  • You can create or add a new data source to the engine without attaching a catalog to it. You can attach a catalog to the data source at a later stage.

Version 2.1.0 of the watsonx.data service includes various fixes.

For details, see What's new and changed in watsonx.data.

Related documentation:
watsonx.data
watsonx.governance 2.1.0
This release of watsonx.governance includes the following features:
Enhancements for governance management

These features extend the way you can extend and manage governance activity.

Distribute governance activities across multiple clusters

You can now distribute governance activities across multiple servers and sync data between the remote servers to a primary governance cluster. Use this capability to isolate production assets on one server and control access to them for greater security.

For details, see Managing multiple clusters for watsonx.governance.

Track governance activity for custom models and tuned models

You can now extend governance to include tracking prompt templates for custom foundation models and tuned models. You can capture the data for the prompt templates, including evaluation results, in a factsheet as part of an AI use case.

Enhancements for asset evaluation

Use these features to improve the quality of your asset evaluations and drive decision-making.

Run quality evaluations with historical data

You can now use an API to evaluate historical feedback data records for online deployments and prompt templates to analyze your model performance over time with a wider scope.

Configure generative AI quality evaluations with LLM-as-a-judge

When you configure generative AI quality evaluations, you can now configure settings to calculate metrics with LLM-as-a-judge models. LLM-as-a-judge models are LLM models that you can use to evaluate the performance of other models.

Import model deployment configuration settings

When you’re adding deployments to configure evaluations for machine learning models in production, you can import settings from your pre-production model deployment to provide model details for evaluations.

Configure global explanations with LIME

You can now use the LIME (Local Interpretable Model-Agnostic explanations) algorithm to configure global explanations for machine learning models. To use LIME to configure global explanations, you must enable the global explanation parameter when you configure explainability.

Enable GPU metrics computation

You can use detectors on GPUs to increase the speed of data safety, answer quality, and retrieval quality metric computations.

For details, see Enabling GPU metrics computation.

Enhancements for the governance console

Use these features to better manage your use cases in the Governance console.

Add custom tabs to the governance console dashboard

You can now add up to three custom tabs to the dashboard. For example, you might want to use custom tabs to group related panels and widgets in separate places on the Home page.

Create stacked bar charts in the governance console

You can now configure a stacked bar chart on the dashboard and in the View Designer panel. Use a stacked bar chart to compare the proportional contributions of each item to the total within a category. For example, for the Model object type, you might create a stacked bar chart that shows risks grouped by Model Category and stacked by Computed Tier.

Use expressions to set field values based on a respondent's questionnaire answers

When you create response actions in governance console, you can now enter an expression for the value of a field. For example, you can enter the following values:

  • [$TODAY$] for the current date
  • [$END_USER$] for the name of the signed on user
  • [$System Fields:Description$] to set the field to the value of the Description field of the object
Enhancements to the watsonx.governance Model Risk Governance solution

This release includes the following enhancements:

New Model Group object type

Groups similar models together. For example, versions of a model that use a similar approach to solve a business problem might be grouped under a Model Group object.

New Use Case Risk Scoring calculation

Aggregates metrics by breach status into risk scores to give an overall view into how the underlying models of a use case are performing.

New Discovered AI library business entity

Provides a default place to store any AI deployments that are not following sanctioned governance practices within an organization (also known as “shadow AI”).

To read more about these features, see What's new and changed in watsonx.governance in the IBM watsonx documentation.

Version 2.1.0 of the watsonx.governance service includes various fixes.

For details, see What's new and changed in watsonx.governance.

Related documentation:
watsonx.governance
watsonx Orchestrate 5.1.0
This release of watsonx Orchestrate includes the following features:
Create projects in skill studio to automate complex tasks and processes
As a builder, you can create projects in Skill studio and publish them as skills to the Skills and apps page, where you enhance the skills and make them available in the skill catalog. You can use the skill by entering a phrase in the watsonx Orchestrate chat or by adding these skill as actions on AI assistants. You can create skills from types like workflows, decision models, and generative AI responses. For details, see https://www.ibm.com/docs/en/watsonx/watson-orchestrate/current?topic=building-projects.
Use formatting for skill descriptions
As a builder, you can create skills that have skill input descriptions in plain text. You can format any of the descriptions by using bold, italics, underline, and you can add hyperlinks.
Use slot logic for validations
As a builder, when you enhance a conversational skill, you can specify validations on the inputs (slot filling) that are obtained from the nonlinear multi-turn conversation interaction.

Version 5.1.0 of the watsonx Orchestrate service includes various fixes.

For details, see What's new and changed in watsonx Orchestrate.

Related documentation:
watsonx Orchestrate

New services

Service Category What does it mean for me?
IBM Manta Data Lineage Data governance

IBM Manta Data Lineage is a new service that is available with IBM Software Hub 5.1.0.

With IBM Manta Data Lineage, you can determine data accuracy throughout your business models and systems. You can more clearly see data dependencies in complex systems, quickly find compliance issues, or identify potential risks.

You can access your imported lineages in the Data lineage workspace, or you can view the lineage for a specific asset on the Catalogs or Projects pages.

Related documentation:
IBM Manta Data Lineage
IBM StreamSets Analytics

IBM StreamSets is a new service available with IBM Software Hub 5.1.0.

With the IBM StreamSets service, you can build streaming data pipelines to act on time-sensitive data and enhance real-time decision making.

Data engineers use IBM StreamSets to build, run, and monitor streaming data pipelines that access and connect data across various types of data sources.

With IBM StreamSets data engineers can:
  • Collaboratively build pipelines.
  • Access multiple types of external systems.
  • Detect and correct unexpected data drift.
Related documentation:
IBM StreamSets
watsonx Code Assistant AI

IBM watsonx Code Assistant is a new service available with IBM watsonx Code Assistant 5.1.0.

watsonx Code Assistant is a generative AI coding companion that provides contextually aware assistance for programming languages such as Go, C, C++, Java, JavaScript, Python, and TypeScript. You integrate watsonx Code Assistant into your IDE, and it helps simplify your coding tasks.

Use watsonx Code Assistant to:

Generate code suggestions
Enter a prompt that describes the code you need, and watsonx Code Assistant generates something you can choose to use.
Modernize Java applications
You can modernize your WebSphere Application Server traditional application to Liberty.
Document code
Generate comment lines that document what your code does.
Generate unit tests
Generate a test for a class, file, function, or method.
Explain code
Analyze and summarize your code to understand what the code does.

To read more about these features, see What's new and changed in watsonx Code Assistant in the IBM watsonx documentation.

Related documentation:
watsonx Code Assistant

Installation enhancements

What's new What does it mean for me?
Install on newer versions of Red Hat OpenShift
You can install IBM Software Hub Version 5.1 on the following versions of Red Hat OpenShift Container Platform:
  • Version 4.12 or later fixes
  • Version 4.14 or later fixes
  • Version 4.15 or later fixes
  • Version 4.16 or later fixes
  • Version 4.17 or later fixes
Integration with IBM Fusion Version 2.9
You can now install IBM Software Hub Version 5.1 with IBM Fusion Version 2.9, which offers performance and scalability improvements. If you use IBM Fusion, it is strongly recommended that you upgrade to IBM Fusion Version 2.9.

For more information, see:

Integration with Nutanix storage
You can now install IBM Software Hub Version 5.1 on Nutanix storage. Support is limited to self-managed, on-premises, Red Hat OpenShift Container Platform clusters running on hyper-converged infrastructure.

For more information, see:

Streamlined installation commands

Previously, you had to run multiple commands to set up an instance of IBM Cloud Pak for Data. Starting in IBM Software Hub Version 5.1.0, you can use the new cpd-cli manage setup-instance command to install:

  • IBM Cloud Pak foundational services operators and custom resources
  • IBM Software Hub operators
  • IBM Software Hub custom resources

For more information, see Installing the required components for an instance of IBM Software Hub.

Streamlined upgrade commands
You no longer need to specify storage information when you upgrade the IBM Software Hub control plane or services. The cpd-cli uses the information that is stored in the relevant custom resources to determine what storage to use.
Integrated storage performance validation

The new cpd-cli manage setup-instance command includes an option named --run_storage_tests. You can use the --run_storage_tests option when you install or upgrade IBM Software Hub.

Storage performance is critical to the functionality of and performance of IBM Software Hub. For more information, see:

Additional guidance on running health checks during installation and upgrade
If your cluster is unhealthy, it can negatively impact the performance of IBM Software Hub. It is strongly recommended that you run the following commands before you upgrade your IBM Software Hub installation:
  • cpd-cli health cluster
  • cpd-cli health nodes
  • cpd-cli health network-performance

The installation and upgrade documentation include guidance on running these commands.

After you install or upgrade IBM Software Hub, you can run the following commands to confirm that the instance is healthy:
  • cpd-cli health operators
  • cpd-cli health operands

The installation and upgrade documentation include guidance on running these commands.

Unified routes
Previously, the IBM Cloud Pak for Data created the cpd route and the Identity Management Service service created the cp-console route, which meant that you needed to manage certificates for two different routes.

Starting in IBM Software Hub Version 5.1, new installations use only the cpd route, which means you only need to manage certificates for a single route. All calls that were previously passed to the cp-route are now directed to the cpd route.

If you upgrade to IBM Software Hub Version 5.1, you can optionally unify the cpd and cp-console routes. For more information, see Unifying the cpd and cp-console routes.

Removals and deprecations

What's changed What does it mean for me?
The IBM Certificate manager is deprecated

If you are upgrading to IBM Software Hub Version 5.1, you can continue to use the IBM Certificate manager, provided by IBM Cloud Pak foundational services.

If you are installing IBM Software Hub Version 5.1, use the Red Hat OpenShift Container Platform cert-manager Operator. For more information, see:
The setup-instance-topology is deprecated
The cpd-cli manage setup-instance-topology command is deprecated and replaced by the cpd-cli manage setup-instance command.

Use the new cpd-cli manage setup-instance command to install and upgrade an instance of IBM Software Hub.

Watson Machine Learning Accelerator is not available on IBM Software Hub Version 5.1

Watson Machine Learning Accelerator is not available on IBM Software Hub Version 5.1. To run deep learning experiments, use Watson Machine Learning.

If you previously installed Watson Machine Learning Accelerator, you can recreate any assets before moving to Watson Machine Learning. If you were using the Watson Machine Learning API or CLI, you will now need to use the Watson Machine Learning API for deep learning training. For more information, see: Preparing to upgrade Watson Machine Learning.

Previous releases

Looking for information about previous releases? See the following topics in IBM Documentation: