Model risk management and model governance in Watson OpenScale
Last updated: Oct 13, 2023
Model risk management and model governance in Watson OpenScale
Use Watson OpenScale to manage risk from machine learning models and to remain in compliance with governance standards.
Planning for model risk management and governance
Copy link to section
Plan your risk management and governance strategy by considering what machine learning models you are deploying in your organization and considering your governance requirements.
Governance with Watson OpenScale
Copy link to section
Use Watson OpenScale as a stand-alone solution. Enable the model risk management features to create pre-production and production repositories and compare models. Monitor your deployed models for the following considerations:
Drift: Any change in input data also known as Drift can cause the model to make inaccurate decisions, impacting business KPIs
Bias: Training data can be cleaned to be free from bias but runtime data might induce biased behavior of model
Explainability: Traditional statistical models are simpler to interpret and explain
Missing Validation or Test Data: Model training data sets might not capture the range of data or combinations that are encountered in runtime. Validation and monitoring of AI models is necessary in addition to govern and manage risk.
About cookies on this siteOur websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising.For more information, please review your cookie preferences options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.