用 SageMaker 无服务器定制构建 AI 产品打标系统

AI 摘要

AWS 博客介绍如何用 Amazon SageMaker 无服务器模型定制构建 AI 产品打标系统。方案先对 Qwen3-8B 做监督微调（SFT），再用带可验证奖励的强化学习（RLVR）和 GRPO 优化，训练由 SageMaker 无服务器定制管理容量，最终模型部署到 SageMaker 异步推理做批量目录富化。文章对比了无服务器与 SageMaker Training Jobs 的差异，并给出数据准备、权限和部署等前置条件。

正文节选

Build an AI-powered product tagging system with Amazon SageMaker serverless model customization Retail catalogs rarely arrive as clean, structured attributes. Product names, descriptions, and category paths come from many sources and change continuously. Search, recommendations, and catalog navigation depend on consistent tags, but manually applying those tags across thousands of stock keeping units (SKUs) is slow and difficult to keep consistent. A general-purpose frontier model can generate ta

发布时间：2026-09-16 00:11

抓取时间：2026-09-16 00:21

来源机构：AWS