● Covered by 1 source · 1 reportLow impact1 neutral
uniopen customized Amazon Nova 2 Lite for retail moderation policies
Key points
- uniopen customized Amazon Nova 2 Lite.
- Fine-tuning done in Amazon SageMaker AI.
- Moderation policies classify behavior and subject.
- Workflow uses AWS services for data, training, deployment.
Customizing Amazon Nova for Retail Moderation
uniopen, a digital communication and membership platform from Taiwan’s Uni-President Enterprises Group, has customized Amazon Nova 2 Lite to align with its specific retail moderation policies. The platform connects customers to e-commerce, membership benefits, and other retail experiences across web, tablet, and mobile channels.
Specific Moderation Policies
uniopen's moderation policy classifies each interaction along two axes: behavior (nine categories) and subject (brand, other, or forbidden). These classifications are specific to uniopen’s business and require a customized model rather than a general-purpose solution. Both axes must be correctly identified for effective moderation decisions.
Technical Implementation
The customization involved supervised fine-tuning of Amazon Nova 2 Lite within Amazon SageMaker AI, followed by prompt-level output optimization. This AWS-based approach manages correction data, training, evaluation, and deployment controls within a single, repeatable workflow. Amazon Nova 2 Pro supports candidate correction generation, which human reviewers verify before it enters the training set.
Architectural Separation
The architecture separates the production moderation path from the correction, training, evaluation, and deployment processes. Amazon S3 stores verified correction sets and training data, while Amazon DynamoDB tracks model configurations. Argo Workflows on Amazon EKS orchestrates prompt optimization, evaluation, and deployment, with Argo CD applying approved configurations to production. Amazon SNS and Amazon CloudWatch provide operational notifications.
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Reporting from
uniopen, a digital communication platform, adapted Amazon Nova 2 Lite using supervised fine-tuning in Amazon SageMaker AI to meet its specific retail moderation policies. This customization allows uniopen to classify user interactions based on nine behavior categories and three subject types, which are unique to its business operations. The approach separates production moderation from correction, training, and deployment, using AWS services for a repeatable workflow.