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

## 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.

Author: Ritesh Yadav