---
format: "aidr-story-markdown/v1"
id: "42c742abce32e0f986c0ba33cb7e577796af859dd0dc053d955a27c43a6b9bca"
canonical_url: "https://aidr.today/42c742ab?lang=en"
title: "Build an AI-powered product tagging system with Amazon SageMaker serverless model customization"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-09-15T16:11:36.000Z"
category: "Products"
topics: []
source_urls: ["https://aws.amazon.com/blogs/machine-learning/build-an-ai-powered-product-tagging-system-with-amazon-sagemaker-serverless-model-customization/"]
summary: "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."
---

# Build an AI\-powered product tagging system with Amazon SageMaker serverless model customization

> [Open the canonical story](<https://aidr.today/42c742ab?lang=en>)

**Published:** 2026-09-15T16:11:36.000Z
**Category:** Products

## Summary

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\.

## Sources

- [Story source](<https://aws.amazon.com/blogs/machine-learning/build-an-ai-powered-product-tagging-system-with-amazon-sagemaker-serverless-model-customization/>)