Answer: Create Amazon SageMaker Model Cards with intended uses and training and inference details.

## Detailed Explanation ### Question Analysis The ML team develops custom ML models and shares model artifacts with other teams while retaining training code and data. They need a mechanism specifically for the **ML team to audit models** when publishing them. The key requirement is establishing documentation that enables effective auditing of custom ML models. ### Evaluation of Options **A: Create documents with relevant information. Store the documents in Amazon S3.** - While storing documentation in S3 provides accessibility, this approach lacks standardization and structure. - Creating ad-hoc documents doesn't ensure comprehensive coverage of all necessary audit information. - This solution doesn't provide a systematic framework for tracking model details, performance metrics, or ethical considerations. - Less suitable because it relies on manual processes without AWS-native tooling designed specifically for model documentation. **B: Use AWS AI Service Cards for transparency and understanding models.** - AWS AI Service Cards are designed for **pre-built AWS AI services** (like Amazon Rekognition, Amazon Comprehend, etc.), not for custom ML models developed by teams. - These cards provide transparency about AWS-managed services but cannot be customized for documenting team-specific model development processes. - Completely unsuitable for custom ML models as they don't support documenting custom training data, algorithms, or team-specific evaluation metrics. **C: Create Amazon SageMaker Model Cards with intended uses and training and inference details.** - **Optimal solution** because Amazon SageMaker Model Cards are specifically designed for documenting custom ML models. - They provide a standardized, structured format that includes: - Model purpose and intended use cases - Training methodology and data details - Performance metrics and evaluation results - Ethical considerations and limitations - Bias analysis and risk assessments - Version history and lineage tracking - This comprehensive documentation enables effective auditing by providing a clear, organized record of model development, evaluation, and intended usage. - Model Cards promote transparency, accountability, and reproducibility—all essential for auditing custom ML models. - As an AWS-native solution, it integrates seamlessly with the SageMaker ecosystem that the ML team likely uses for model development. **D: Create model training scripts. Commit the model training scripts to a Git repository.** - While version control for training scripts is a good practice, it addresses only one aspect of model documentation. - This solution doesn't capture model performance metrics, intended uses, ethical considerations, or other critical audit information. - Git repositories track code changes but don't provide a structured format for documenting the complete model lifecycle. - Less suitable because it's insufficient for comprehensive model auditing requirements. ### Why Option C is the Best Choice 1. **Purpose-Built Solution**: SageMaker Model Cards are specifically designed for documenting ML models, unlike generic documentation approaches. 2. **Comprehensive Coverage**: They capture all essential information needed for auditing—from training details to ethical considerations. 3. **Standardization**: Provides a consistent format that ensures all necessary audit information is captured systematically. 4. **AWS Integration**: Seamlessly works with the SageMaker platform that ML teams typically use for model development and deployment. 5. **Audit-Ready**: The structured format makes it easy for the ML team to review, validate, and audit models before and after publication. ### Key Distinction While options A and D represent good general practices, they don't provide the specialized, comprehensive documentation framework needed specifically for model auditing. Option B is completely misaligned with the requirement for custom models. Only option C delivers a purpose-built solution that addresses all aspects of the ML team's auditing needs.