Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 91 discussion

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

Amazon SageMaker Model Cards are designed to provide transparency and detailed information about a model's intended uses, training process, data, and performance. They are specifically designed for auditing models and making them more interpretable, which is exactly what the ML team needs. Model cards help document key details about the model, including how it was trained, its intended use cases, and any potential risks associated with its use. This solution supports effective auditing and provides a comprehensive, structured format for tracking important model information.

Amazon SageMaker Model Cards: These provide a standardized way to document important information about ML models, including: Model purpose and intended use cases Training data and methodology Evaluation metrics and results Ethical considerations and limitations Bias analysis Version history This comprehensive documentation facilitates auditing by providing a clear record of how the model was developed, evaluated, and intended to be used. It also promotes transparency and accountability. B. Use AWS AI Service Cards for transparency and understanding models: AWS AI Service Cards are designed for pre-built AI services provided by AWS, not for custom ML models developed by a team. They are not applicable for this use case.

The correct answer is C. SageMaker Model Cards are designed specifically for documenting model details and usage.

The correct answer is: C. Create Amazon SageMaker Model Cards with intended uses and training and inference details. Explanation: Amazon SageMaker Model Cards provide a standardized and centralized way to document key details about a machine learning model. This includes intended use, training and inference details, performance metrics, and ethical considerations. These cards enable the ML team to maintain transparency, track audit details, and share relevant information with other teams when publishing models.