AWS Machine Learning Blog· Purna Sanyal·· 7 h ago
Exowatt beats frontier models on accuracy with SageMaker serverless model customization
Exowatt beats frontier models on accuracy with SageMaker serverless model customization
AI summary
In this post, we show how Exowatt used Amazon SageMaker AI serverless model customization to build a repeatable AI pipeline that produces task-specific fine-tuned models for two deployment targets, edge and cloud, achieving higher accuracy than frontier models at up to 49x lower cost.
Selection record
Threshold 60Official, first-handFirst 42Second 34
Not admittedSum of both 76 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：SageMaker微调模型及AI Agent部署实践
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