AI model testing helps you confirm that an AI system performs accurately and consistently in real business conditions. This blog explains how to validate accuracy and performance using the right metrics, cross-validation and holdout methods, robustness and fairness testing, and production monitoring for drift and unsafe outputs. You gain clarity on what to test, when to test it, and how to act on the results.
The post Don’t Trust AI Blindly: A Leader’s Approach to AI Model Validation for Accuracy and Performance first appeared on TestingXperts.
