A financial services company uses Amazon SageMaker to develop and register machine learning (ML) models for various business needs, such as fraud detection, risk assessment, and customer segmentation. These models are stored in model groups within the SageMaker Model Registry. The data science team is categorized into three specialized groups based on their areas of focus: fraud detection models, risk assessment models, and customer segmentation models. An ML engineer needs to implement a solution to organize the existing models into these three business categories to improve discoverability at scale, while ensuring the integrity of the model artifacts and their existing groupings remains unaffected. Which solution will meet these requirements?