Build a model improvement flywheel with Amazon SageMaker HyperPod Data Capture

This article demonstrates how to build a model improvement flywheel using Amazon SageMaker HyperPod Data Capture, which records live inference inputs and outputs to S3 with minimal latency impact.

• Three-tier data capture architecture: SageMaker endpoint (compliance), load balancer (traffic monitoring), and model pod (debugging and training data)
• Deploy foundation models with capture enabled to record production traffic that reveals model blind spots and drift
• Use Amazon Athena to query captured data and surface issues like new product categories the model doesn't recognize
• Create SQL labeling policies to automatically correct and label captured records for retraining
• Fine-tune models on production data using HyperPod training operator on the same cluster, measuring accuracy improvements
• Deploy improved models side-by-side with capture still enabled for continuous improvement cycles
• Capture adds negligible latency (200ms with or without proxy) and costs scale with S3 storage and Athena queries

The walkthrough shows a support-ticket router that missed a new air-conditioner product category, detected through captured traffic analysis, relabeled via SQL policy, and improved from 83.9% to 100% accuracy on held-out evaluation data through LoRA fine-tuning.