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A data engineer is implementing model governance for machine learning (ML) workflows on AWS.

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A data engineer is implementing model governance for machine learning (ML) workflows on AWS. The data engineer needs a solution that can track the complete lifecycle of the ML models, including data preparation, model training, and deployment stages. The solution must ensure reproducibility and audit compliance.

A.

Use Amazon SageMaker Debugger to capture metrics. Create associations between datasets and training jobs by monitoring training jobs.

B.

Use Amazon SageMaker ML Lineage Tracking to create associations between artifacts, training jobs, and datasets by recording metadata.

C.

Use Amazon SageMaker Model Monitor to create associations between artifacts and training jobs by tracking model performance.

D.

Use Amazon SageMaker Experiments to create associations between datasets and artifacts by tracking hyperparameters and metrics.

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