A/B Testing
AI Infrastructure
Controlled experimentation comparing two or more versions of an ML model in production.
Detailed Explanation
A/B Testing is a controlled experimentation process in AI infrastructure where multiple versions of a machine learning model are deployed simultaneously to different user groups or traffic segments. By comparing their performance metrics, organizations can determine which model performs better, optimize predictions, improve user experience, and make data-driven decisions for deploying the most effective model in production.
Use Cases
- •Optimize model deployment by testing different versions on user groups to identify the most effective model for improved performance and user experience.
Related Terms
Other terms in the AI Infrastructure category