一个机器学习研究团队开发了自定义的机器学习模型。模型工件会共享给其他团队，以便集成到产品和服务中。该机器学习团队保留了模型训练代码和数据。该团队希望构建一种机制，以便对模型进行审计。 该机器学习团队在发布自定义机器学习模型时应采用哪种解决方案？
A. 创建包含相关信息的文档，将文档存储在 Amazon S3 中。
B. 使用 AWS 人工智能服务卡片来提高模型的透明度和可理解性。
C. 创建包含预期用途、训练和推理细节的 Amazon SageMaker 模型卡片。
D. 创建模型训练脚本，并将其提交到 Git 仓库。
An ML research team develops custom ML models. The model artifactsare shared with other teams for integration into products and services.The ML team retains the model training code and data. The ML team wants to build a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom IML models?
A. Create documents with the relevant information. Store the documents in Amazon S3.
B. Use AWS Al Service Cards for transparency and understanding mordels
C. Create Amazon SageMaker Model Cards with intended uses and training and inference details
D. Create model training scripts. Commit the model training scriptsto a Git repository.
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