New: How to correct AI model drift
Track performance of production AI and its impact on business goals, with actionable metrics in a single console.
Apply business results to create a continuous feedback loop that improves and sustains AI outcomes.
Maintain regulatory compliance by tracing and explaining AI decisions across workflows, and intelligently detect and correct bias to improve outcomes.
Credit risk modeling
Credit lenders can monitor risk models for performance, bias, and explainability to limit risk exposure from regulations, and create more fair and explainable outcomes for customers.
Explainable claims processing
Insurance underwriters can use machine learning to make more consistent and accurate risk assessments around claims, ensure fair outcomes for customers and explain AI recommendations for regulatory and business intelligence purposes.
Predict telco asset failure
Data scientists can build machine-learning models and work with their IT operations teams to confidently recommend proactive asset maintenance for telecommunications providers.
Dive into OpenScale's capabilities.
Documentation and resources
Explore reference material to get started and see what you can do in Watson OpenScale.
Learn how to configure Watson OpenScale and set up bias detection and explainability.
Understand how Watson OpenScale can help manage trusted AI at scale and increase confidence in business outcomes.
IBM offers end-to-end model management and helps companies operationalize AI, according to Bloor Research.