Fix evaluation bias to unlock better models on Amazon Bedrock
In brief
- 96.8% of vision AI evaluation labels came from the existing model.
- Unbiased ground truth was built to properly assess model performance.
- Costs dropped by 86% across 11 image-quality attributes after selecting a new model.
Property Finder discovered that 96.8% of their vision AI evaluation labels came from the incumbent model, masking its true accuracy. The company explains how it built unbiased ground truth, applied evolutionary prompt optimization, and selected a more accurate, cost-effective model on Amazon Bedrock, cutting costs by 86% across 11 image-quality attributes according to AWS.
What it changes for you
- Retail & E-commerceRetailers using vision AI tools can improve model accuracy by fixing evaluation biases, thereby reducing operational costs.
This summary gives you the essentials. The full article, its figures and quotes are on Artificial Intelligence.
Read the original articleAI-generated summary of the source, checked by rules. How we write · Report an error