Evaluating the true success of an Artificial Intelligence model extends far beyond simply building and deploying it. A robust framework for measuring AI success requires a comprehensive understanding of various metrics, each offering unique insights into a model’s performance, reliability, and real-world impact. Effective model evaluation ensures that AI systems are not only accurate but also fair, efficient, and aligned with business objectives. Selecting the right metrics is paramount,
