Watson OpenScale

Version: 5.1.2

Experience: Cloud Pak for Data watsonx™

Description

Use Watson OpenScale to analyze your AI with trust and transparency and understand how your AI models make decisions. Detect and mitigate bias and drift. Increase the quality and accuracy of your predictions. Explain transactions and perform what-if analysis.

Watson OpenScale is an enterprise-grade environment for AI applications that provides your enterprise visibility into how your AI is built, is used, and delivers return on investment. Its open platform enables businesses to operate and automate AI at scale with transparent, explainable outcomes that are free from harmful bias and drift. Watson OpenScale supports external models that are developed in third-party providers, including Amazon Web Services or Microsoft Azure.

With the Watson OpenScale service, you can scale adoption of trusted AI across enterprise applications on hosted on-premises environments or in a private cloud environment.

Choosing a governance solution

Watson OpenScale can be installed as part of the integrated watsonx.governance™ service or as a separate service.

  • The watsonx.governance service includes governance features for generative AI assets as well as machine learning assets.

  • As a separate service, Watson OpenScale can evaluate machine learning models and generate insights about performance. It can be used with other services to build a comprehensive AI governance solution for machine learning models.

Licensing information

This service is included in the following licenses:

  • IBM Cloud Pak® for Data Enterprise Edition
  • IBM Cloud Pak for Data Standard Edition
  • IBM® watsonx.governance Model Management

For more information, see Licenses and entitlements.

Quick links

Integrated services

Table 1. Related services. The following related services are often used with this service and provide complementary features, but they are not required.
Service Capability
Runtime 24.1 on Python 3.11 for GPU Access compute environments for Jupyter Notebooks that use GPU-accelerated Python 3.11 libraries.
Runtime 24.1 on R 4.3 Access compute environments to create Jupyter Notebooks that use R 4.3 libraries.
Watson Machine Learning Build, train, and deploy machine learning models with a full range of tools.
Watson Studio Prepare, analyze, and model data in a collaborative environment with tools for data scientists, developers, and domain experts.
AI Factsheets Use AI Factsheets to organize and track lineage events, facts, and details for each of your machine learning models' lifecycle, and increase transparency for model governance needs.