How uniopen customized Amazon Nova to their retail moderation policies for production deployment
How uniopen customized Amazon Nova to their retail moderation policies for production deployment How uniopen customized Amazon Nova to their retail moderation policies for production deployment uniopen is a digital communication and membership platform launched by Taiwan’s Uni-President Enterprises Group, connecting customers to ecommerce, membership benefits, and other retail experiences across web, tablet, and mobile channels. Across those channels, uniopen applies a moderation policy that classifies each interaction along two axes.
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- How uniopen customized Amazon Nova to their retail moderation policies for production deployment uniopen is a digital communication and membership platform launched by Taiwan’s Uni-President Enterprises Group, connecting customers to ecommerce, membership benefits, and other retail experiences across web, tablet, and mobile channels.
- Across those channels, uniopen applies a moderation policy that classifies each interaction along two axes.
- The first is what behavior occurred (nine categories), and the second is what subject the behavior refers to (brand, other, or forbidden).
- Both must be correct for a moderation decision to be useful, and both are specific to uniopen’s business rather than something a general-purpose model can be expected to learn out of the box.
- In this post, we show how the team adapted Amazon Nova 2 Lite to these business-specific moderation policies through supervised fine-tuning in Amazon SageMaker AI and a final prompt-level output optimization.
- The AWS approach kept correction data, managed training, evaluation, and deployment controls in one repeatable workflow.