Question
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?
- A. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
- B. Create a model group for each category. Move the existing models into these category model groups.
- C. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
- D. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections. Correct
Explanation
Option D is correct because creating a Model Registry collection for each category allows organizing the existing models without affecting their integrity or existing groupings, thus improving discoverability at scale as required.
