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

Plan your risk management and governance strategy by considering what machine learning models you are deploying in your organization and considering your governance requirements.

You can choose to rely on Watson OpenScale for your risk management needs, or you can extend your governance plan using IBM OpenPages.

Governance with Watson OpenScale

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.

Extending governance with IBM OpenPages

Use Watson OpenScale as part of an overall model risk governance solution by integrating with IBM OpenPages Model Risk Governance. IBM OpenPages Model Risk Governance is a scalable governance, risk and compliance (GRC) solution that runs on Cloud Pak for Data. It allows you to centralize risk management functions within a single environment to help you identify, manage, monitor and report on risk and regulatory compliance for your enterprise.

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Parent topic: Evaluating AI models with Watson OpenScale