The new Data Capture capability within Amazon SageMaker HyperPod streams live model inputs and outputs across three independent architectural tiers straight to Amazon S3. In an appliance routing demonstration, captured logs were analyzed using Amazon Athena to detect coverage gaps, labeled via a SQL policy, and used to fine-tune a model on an adjacent GPU. The architecture enables simultaneous inference serving and retraining on a single cluster using two NVIDIA GPUs. Teams must secure captured payloads containing sensitive information with mechanisms like Amazon Comprehend redaction, AWS Key Management Service encryption, and S3 lifecycle rules.
Key points
Continuous inference logging into S3 powers an end-to-end model improvement flywheel with…: Continuous inference logging into S3 powers an end-to-end model improvement flywheel without impacting production availability.
Organizations can retrain and deploy updated models side by side with active workloads on…: Organizations can retrain and deploy updated models side by side with active workloads on a single HyperPod cluster.
Teams must implement encryption, lifecycle policies, and redaction safeguards to protect…: Teams must implement encryption, lifecycle policies, and redaction safeguards to protect sensitive customer data captured in full model payloads.
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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 was deployed. The inference operator serves the model on one node, and the training operator runs the fine-tuning on another.
We use synthetic data for the walkthrough. The scripts, manifests,…
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