Amazon Marketplace scores product page quality with SageMaker AI
How Amazon Marketplace evaluates content quality with Amazon SageMaker AI
The non-obvious part is the fusion design: instead of relying on a vision-language model alone, OCR readability features feed a CatBoost classifier alongside it, with SHAP explanations attached to every decision. Teams building content-moderation or quality-scoring pipelines can copy this rendering-plus-ML pattern on SageMaker. It also shows how Amazon turns model outputs into seller-facing guidance rather than just internal metrics.
Amazon Marketplace built a pipeline on Amazon SageMaker AI to evaluate A+ Content quality across its product catalogue at scale. Each product page is rendered as a single image snapshot, then scored by a vision-language model whose output is fused with OCR-based readability signals in a CatBoost classifier. The team uses SHAP to explain each scoring decision. The explanations are converted into prioritised, actionable recommendations for selling partners to improve their pages.
How Amazon Marketplace evaluates content quality with Amazon SageMaker AI
Learn how Amazon Marketplace evaluates A+ Content quality at scale on Amazon SageMaker AI by rendering each product page as a single snapshot, scoring it with a vision-language model fused with OCR readability signals in a CatBoost classifier, and using SHAP to turn each decision into prioritized, actionable recommendations for selling partners.