A business running multiple ML models wants to detect changes in model performance so it can address problems quickly. Which AWS feature satisfies this requirement?

Amazon SageMaker Model Monitor is the correct choice because it continuously monitors the quality of ML models in production. It automatically detects data drift, model drift, and other performance issues, alerting you to potential problems so you can take corrective action. This directly addresses the requirement of detecting changes in model performance to address problems quickly. Amazon SageMaker JumpStart provides pre-built solutions and models, not monitoring capabilities. Amazon SageMaker HyperPod is designed for distributed training of large models, not ongoing performance monitoring. Amazon SageMaker Data Wrangler helps prepare data for ML, which is a pre-modeling step, not a monitoring solution for deployed models.