Which of the following strategies is the MOST LIKELY to ensure model versioning, repeatability, and auditability?

You are a data scientist at a pharmaceutical company that builds predictive models to analyze clinical trial data. Due to regulatory requirements, the company must maintain strict version control of all models used in decision-making processes. This includes tracking which data, hyperparameters, and code were

used to train each model, as well as ensuring that models can be easily reproduced and audited in the future. You decide to implement a system to manage model versions and track their lifecycle effectively.

Which of the following strategies is the MOST LIKELY to ensure model versioning, repeatability, and auditability?
A . Leverage the SageMaker Model Registry to register, track, and manage different versions of models, capturing all relevant metadata, including data sources, hyperparameters, and training code
B . Create a version control system in Git for the model’s training code and configuration files, while storing the trained models in a separate S3 bucket for easy retrieval
C . Use Amazon S3 to store each version of the model manually, tagging the stored files with metadata about the training data, hyperparameters, and code used for training
D . Use SageMaker Model Monitor to track the performance of models in production, ensuring that any
changes in model behavior are documented for future audits

View Answer

Answer: A

Explanation:

Correct option:

Leverage the SageMaker Model Registry to register, track, and manage different versions of models, capturing all relevant metadata, including data sources, hyperparameters, and training code

The SageMaker Model Registry is specifically designed for managing model versions in a systematic and organized manner. It allows you to register different versions of a model, track metadata such as data sources, hyperparameters, and training code, and ensure that each version is easily reproducible. This approach is ideal for regulatory environments where audit trails and model governance are critical.

With the Amazon SageMaker Model Registry you can do the following:

Catalog models for production.

Manage model versions.

Associate metadata, such as training metrics, with a model.

View information from Amazon SageMaker Model Cards in your registered models.

Manage the approval status of a model.

Deploy models to production.

Automate model deployment with CI/CD.

Share models with other users.

Incorrect options:

Use Amazon S3 to store each version of the model manually, tagging the stored files with metadata about the training data, hyperparameters, and code used for training – While using Amazon S3 to store model versions with metadata is possible, it requires a lot of manual effort and lacks the automated tracking and management capabilities needed for comprehensive version control, repeatability, and auditability.

Create a version control system in Git for the model’s training code and configuration files, while storing the trained models in a separate S3 bucket for easy retrieval – Using Git for version control of the training code and configurations is a good practice, but it does not address the need to manage the actual trained models and their associated metadata systematically. The SageMaker Model Registry offers a more comprehensive solution that integrates both code and model versioning.

Use SageMaker Model Monitor to track the performance of models in production, ensuring that any changes in model behavior are documented for future audits – SageMaker Model Monitor is useful for monitoring model performance in production, but it does not handle version control or track the metadata necessary for repeatability and audits. It is complementary to, but not a substitute for, the SageMaker Model Registry.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html

https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html