A company’s generative AI model for customer segmentation has been running in production for a long time and recently started producing inconsistent outputs. The company wants to assess model bias and detect drift. Which AWS service or feature should they use?

Amazon SageMaker Model Monitor is the correct choice because it continuously monitors machine learning models in production for data drift, model quality, and bias. In this scenario, inconsistent outputs and the need to detect drift and assess bias directly align with Model Monitor's capabilities. Amazon SageMaker Clarify is used for detecting and mitigating bias and explaining predictions before or during model development, not primarily for continuous production monitoring of drift. Amazon SageMaker Model Cards provide documentation for models, which is important for governance but doesn't actively monitor performance or drift. Amazon SageMaker Feature Store is for creating, storing, and sharing features for ML models, not for monitoring deployed models.