What's new and changed in Watson OpenScale

Watson OpenScale updates can include new features and fixes. Releases are listed in reverse chronological order so that the latest release is at the beginning of the topic.

You can see a list of the new features for the platform and all of the services at What's new in IBM Software Hub.

IBM Cloud Pak for Data Version 5.1.2

A new version of Watson OpenScale was released in March 2025.

This release includes the following changes:

Customer-reported issues fixed in this release
For a list of customer-reported issues that were fixed in this release, see the Fix List for IBM Cloud Pak® for Data on the IBM Support website.

IBM Cloud Pak for Data Version 5.1.1

A new version of Watson OpenScale was released in February 2025.

This release includes the following changes:

Customer-reported issues fixed in this release
For a list of customer-reported issues that were fixed in this release, see the Fix List for IBM Cloud Pak for Data on the IBM Support website.

IBM Cloud Pak for Data Version 5.1.0

A new version of Watson OpenScale was released in December 2024.

This release includes the following changes:

New features
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.

For details, see Providing 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.

For details, see 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.

For details, see Quality evaluations.

Updates
The following updates were introduced in this release:
  • Batch configurations for machine learning model evaluations now support EDB Postgres databases.
  • You can now view a progress indicator to understand the status of evaluations that you run.
  • Use the new confusion matrix to analyze quality evaluation results. The confusion matrix now displays the percentage of positive and negative transactions that are analyzed correctly and the level of correctness for categories of transactions.

    For details, see Reviewing evaluation results.

  • Use the new data visualizations to analyze global explanations. You can gain insights about feature influence, feature distribution, and differences with baseline global explanations.

    For details, see Explaining model transactions.

Customer-reported issues fixed in this release
For a list of customer-reported issues that were fixed in this release, see the Fix List for IBM Cloud Pak for Data on the IBM Support website.