ShopScale runs dozens of SageMaker training jobs (script mode with built-in XGBoost) under an Experiment. Each training job logs a metric 'validation:accuracy' at each epoch into the TrialComponent. The ML team needs an automated programmatic report that compares validation:accuracy at epoch 10 across all trial components in the Experiment to pick the best run for deployment. Which approach will give the most direct, programmatic way to aggregate that metric across runs?

The SageMaker Experiments Python SDK, specifically sagemaker.analytics.TrialComponentAnalytics, is designed for this exact purpose. It allows you to programmatically query and analyze metrics logged by trial components within an experiment, making it straightforward to filter for a specific metric (validation:accuracy) at a particular epoch (epoch 10) across all runs. This provides a direct and efficient way to compare and identify the best run. Querying CloudWatch Metrics directly would be overly complex. While metrics are logged there, linking them back to specific trial components and epochs for aggregation would require significant custom parsing and mapping. Exporting logs to S3 and using Glue ETL is an indirect, resource-intensive approach for data that is already structured and accessible via the Experiments SDK. SageMaker Model Monitor is for monitoring model performance in production, not for comparing training run metrics within an experiment.