AWS Certified Cloud Practitioner

Explanation

Amazon SageMaker Model Cards are specifically designed for documenting and publishing custom ML models. Here's why option C is correct:

Amazon SageMaker Model Cards provide a standardized way to document machine learning models with:

• Intended uses - How the model should be used and for what purposes
• Training details - Information about the training data, algorithms, and hyperparameters
• Inference details - How the model performs inference and what inputs/outputs it expects
• Performance metrics - Model evaluation results
• Risk assessments - Potential limitations and biases

Why other options are incorrect:

A. Create documents with the relevant information. Store the documents in Amazon S3.

• While S3 is good for storage, this approach lacks standardization and doesn't provide the structured documentation format specifically designed for ML models.

B. Use AWS AI Service Cards for transparency and understanding models.

• AWS AI Service Cards are designed for AWS's pre-built AI services (like Amazon Rekognition, Amazon Comprehend), not for custom ML models built by customers.

D. Create model training scripts. Commit the model training scripts to a Git repository.

• While version control for scripts is important, this doesn't provide the comprehensive documentation needed for publishing and sharing ML models with stakeholders.

Best Practice: Amazon SageMaker Model Cards help ensure transparency, reproducibility, and responsible AI practices by providing a consistent way to document ML models throughout their lifecycle.