Exowatt uses Amazon SageMaker serverless model customization to build AI pipeline for solar energy systems
Exowatt developed a repeatable AI pipeline using Amazon SageMaker serverless model customization to fine-tune Qwen3-1.7B and Qwen3-14B models for edge and cloud deployment. The pipeline achieved higher accuracy than frontier models in alarm triage and energy market analysis tasks, with macro-F1 scores of 0.911 and 0.975 respectively. The approach reduced model customization costs by up to 49 times and enabled deployment from raw operational data to evaluated artifacts in days rather than months.
This case demonstrates how serverless model customization can effectively adapt large AI models for specialized industrial applications, significantly reducing costs and deployment time while maintaining high accuracy—relevant for AI practitioners and energy infrastructure operators seeking efficient, domain-specific AI solutions.
Source: AWS Machine Learning Blog · aws.amazon.com