SageMaker Serverless AI Automates Retail Product Tagging

Retail catalogs suffer from inconsistent tagging across thousands of SKUs, making search and navigation unreliable. This walkthrough shows how to customize Qwen3-8B using supervised fine-tuning and reinforcement learning with verifiable rewards via Amazon SageMaker serverless model customization, eliminating the need to manage training infrastructure. The workflow covers three stages: data preparation using the Amazon Sales Dataset, serverless model training with SFTTrainer and RLVRTrainer, and deployment via SageMaker Asynchronous Inference on a provisioned ml.g6.2xlarge instance. The approach teaches the model a specific tagging schema, optimizing precision without paying for unnecessary broad model capabilities.