A company is building a web-based AI application with Amazon SageMaker that provides ML experimentation, training, a central model registry, deployment, and monitoring. Training data resides in Amazon S3 and must remain secure and isolated. To manage multiple model versions centrally with the least operational overhead, which approach should they use?

The SageMaker Model Registry is designed for central management of ML models, offering versioning, approval workflows, and deployment capabilities, directly addressing the need for managing multiple model versions with least operational overhead. Model groups within the registry further organize models based on projects or applications. Using Amazon ECR repositories for every model would increase operational overhead significantly due to managing numerous repositories. Assigning unique image tags in ECR for each model version is a valid practice for container images but doesn't provide the higher-level model management features like approval workflows, metadata tracking, and direct deployment that the SageMaker Model Registry offers. Relying solely on unique tags within the SageMaker Model Registry without leveraging model groups would make organization and management of a growing number of models less efficient.