Build an AI-powered product tagging system with Amazon SageMaker serverless model customization
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This article outlines a method for building an AI-powered product tagging system using Amazon SageMaker serverless model customization. It addresses the challenge of manually tagging retail catalogs by customizing the Qwen3-8B open-weight model instead of relying on general-purpose frontier models. The workflow begins with data preparation, where source catalog information is transformed into supervised fine-tuning (SFT) datasets. Next, the model undergoes SFT to learn the specific tagging schema, followed by reinforcement learning with verifiable rewards (RLVR) using Group Relative Policy Optimization (GRPO). This optimization phase employs a deterministic reward function to balance recall and precision, ensuring consistent attribute generation. Finally, the optimized model is deployed via Amazon SageMaker Asynchronous Inference on provisioned instances for batch-oriented catalog enrichment. The approach leverages serverless training capacity to manage compute resources efficiently while maintaining high-quality output.
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