Why we run two scoring tracks (LLM + Mediapipe) for our AI face-rating tool

1. 1Run multiple scoring tracks in parallel to reduce variability in results
2. 2Use LLM and Mediapipe as separate scoring tracks to leverage their unique strengths
3. 3Configure the tracks to output scores that can be compared and averaged
4. 4Test the tool with multiple inputs to ensure consistency and reliability
5. 5Apply the averaged scores to improve the overall accuracy of the face-rating tool

Data scientists and AI engineers can benefit from this approach to increase the accuracy of their models, while product managers can use it to improve user trust in AI-powered tools

💡 Using multiple scoring tracks, such as LLM and Mediapipe, can significantly improve the reliability and accuracy of AI face-rating tools

Improve AI face-rating tool reliability with multiple scoring tracks! 🤖💡