Build an AI-powered product tagging system with Amazon SageMaker serverless model customization
Amazon Web Services Linpo Guo
Amazon SageMaker serverless model customization walkthrough customizes Qwen3-8B to generate product tags in a fixed nine-category schema using supervised fine-tuning (SFT) followed by reinforcement learning with verifiable rewards (RLVR).
Why it matters
Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system.