AWS Demonstrates a SageMaker HyperPod Workflow for Capturing Data and Fine-Tuning Models

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   AWS Machine Learning BlogPicks

   How to Build a Model Improvement Flywheel with SageMaker HyperPod Data Capture

   AWS demonstrates how to use SageMaker HyperPod Data Capture to record inference traffic, analyze it with Athena, create a SQL labeling policy, and fine-tune a support router on the same cluster. In the synthetic walkthrough, a 60-second LoRA fine-tune raised held-out accuracy from 83.9% to 100% across 280 tickets; a separate set of 24 hand-written probes scored 20/24. The post includes scripts, manifests, SQL, datasets, and expected outputs in a GitHub repository.

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