AWS Machine Learning Blog· Shantanu Tripathi·· 2 d ago
Build a model improvement flywheel with Amazon SageMaker HyperPod Data Capture
Build a model improvement flywheel with Amazon SageMaker HyperPod Data Capture
AI summary
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.
Selection record
Threshold 60Official, first-handFirst 42Second 58
Not admittedSum of both 100 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：SageMaker数据捕获与模型微调教程
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: AWS Machine Learning Blog · aws.amazon.com