This article discusses key questions related to Experiments, Model Training, and Evaluation in the context of AWS SageMaker. It explores how SageMaker can address these questions and provides insights on utilizing its features for data partitioning, model training, evaluation, and tracking. The article also introduces the Fraud Detection use-case and demonstrates how to use SageMaker Wrangler and AutoPilot to automate data partitioning and model training. Additionally, it highlights tools like SageMaker Debugger and SageMaker Clarify for monitoring training progress and tracking model boundaries and bias detection.

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