Build a model improvement flywheel with Amazon SageMaker HyperPod Data Capture
Amazon SageMaker HyperPod Data Capture records live inference inputs and outputs to Amazon S3. This post walks through one end-to-end example: capturing a support router's production traffic, surfacing a new-product blind spot with Amazon Athena, fine-tuning on the captured data with the HyperPod training operator, and measuring the before-and-after lift.
The post demonstrates a five-step flywheel on a single SageMaker HyperPod cluster to improve a model by capturing production inference data. It explains how to use Amazon SageMaker HyperPod Data Capture, which records live inference inputs and outputs to Amazon S3 at three tiers with minimal latency impact and zero impact on model availability.
The stages include setting variables, staging the base model in S3, setting up IAM permissions for four identities, capturing the inference data in Tier 3, analyzing the data with Athena, labeling the records with a SQL policy, fine-tuning the model using the training operator on a second GPU, and redeploying the improved model side by side with capture enabled.
The source material provides specific commands, code snippets, and configuration details to implement this flywheel process for improving a model on Amazon SageMaker HyperPod.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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