Exowatt uses SageMaker serverless fine-tuning to beat frontier models on alarm triage and energy analysis
AIExowatt used Amazon SageMaker AI serverless model customization to fine-tune Qwen3-1.7B for edge alarm triage and Qwen3-14B for cloud energy-market analysis. The fine-tuned Qwen3-1.7B scored 0.911 macro-F1 on alarm triage, above Claude Sonnet 4.6's 0.698, and the fine-tuned Qwen3-14B scored 0.975 on energy-market analysis, above Claude Opus 4.7's 0.625.
AIWhy it matters.
The post walks through a full fine-tune, evaluate, and deploy pipeline with measured accuracy and cost against frontier models, useful for teams weighing small task-specific models at the edge.
Source details
AWS Machine Learning Blog · Purna Sanyal · aws.amazon.comPublished · added here
Original title: Exowatt beats frontier models on accuracy with SageMaker serverless model customization