Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 1 discussion

https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-models.html "Each model package in a Model Group corresponds to a trained model. The version of each model package is a numerical value that starts at 1 and is incremented with each new model package added to a Model Group. For example, if 5 model packages are added to a Model Group, the model package versions will be 1, 2, 3, 4, and 5."

The correct answer is C. Use the SageMaker Model Registry and model groups to catalog the models. SageMaker Model Registry is specifically designed for managing ML models within the SageMaker ecosystem. It provides built-in versioning and cataloging capabilities for ML models. Model groups in the Model Registry allow for logical organization of related models. It integrates seamlessly with other SageMaker components like training, deployment, and monitoring. This solution offers the least operational overhead as it's a native SageMaker feature designed for this exact purpose. It provides features like model approval workflows, which are crucial for managing models in production environments.

Amazon SageMaker Model Registry creates Catalog models for production and Manage model versions.

Amazon SageMaker Model Registry is specifically designed to manage and catalog models in a centralized way, including versioning, approval workflows, and deployment history. It simplifies the process of managing different versions of models, which aligns with the company's requirement to use a central model registry.

A. No, ECR is used to store container images B. No, ECR is used to store container images C. Yes D. No, Each model package in a Model Group corresponds to a trained model. The version of each model package is a numerical value that starts at 1 and is incremented with each new model package added to a Model Group - https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-models.html

C: The SageMaker Model Registry organizes models into Model Package Groups, tracks versions as Model Packages, and optionally aggregates groups into Collections. This structure ensures robust versioning and manageability for trained models. https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-models.html

https://aws.amazon.com/blogs/machine-learning/centralize-model-governance-with-sagemaker-model-registry-resource-access-manager-sharing/