AIF-C01 · Question #177
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML
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Question
An ML research team develops custom ML models. The model artifacts are 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 builk a mechanism that the ML team can use to audit models. Which solution should the ML team use when publishing the custom ML models?
Options
- ACreate documents with the relevant information. Store the documents in Amazon S3.
- BUse AWS A] Service Cards for transparency and understanding models.
- CCreate Amazon SageMaker Model Cards with Intended uses and training and inference details.
- DCreate model training scripts. Commit the model training scripts to a Git repository.
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