Build a model improvement flywheel with Amazon SageMaker HyperPod Data Capture | Amazon Web Services
Foundation models deployed on Amazon SageMaker HyperPod meet inputs that no pre-curated dataset covers: domain-specific jargon, unexpected formats, new products, and changing user behavior. Without a record of production inference data, teams can’t detect drift, find failure patterns, or use real interactions to improve the model.
Amazon SageMaker HyperPod now provides Data Capture, a built-in capability that records live inference inputs and outputs to Amazon Simple Storage Service (Amazon S3) at three independent tiers, with minimal latency impact and zero impact on model availability.
In this post, we walk through one complete example on a single SageMaker HyperPod cluster. A home-appliance support router captures its production traffic, Amazon Athena queries surface a new-product blind spot, a SQL policy labels the captured records, and a fine-tune on that data teaches the router the new product. We measure accuracy before and after, on data that could not have existed before the model...
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