Your eval criteria are code. Version them like code.

1. 1Define evaluation criteria as code
2. 2Version control the criteria using tools like Git
3. 3Track changes to the criteria over time
4. 4Test and validate the criteria using automated tests
5. 5Continuously monitor and update the criteria as needed

Data scientists and AI engineers benefit from versioning evaluation criteria to track changes and ensure reproducibility, while product managers can use this approach to monitor model performance and make data-driven decisions

💡 Treating evaluation criteria as code enables version control, reproducibility, and accountability in AI model development

🚨 Version your eval criteria like code to ensure transparency and accountability in AI model development 💡