How to Monitor Ranking Models

Monitoring ranking models is crucial for ensuring high-quality recommendations and maintaining customer satisfaction. Poorly performing ranking models can lead to decreased revenue, increased churn, and reduced user engagement. To monitor these models effectively, it's essential to use rank-aware evaluation metrics such as Mean Reciprocal Rank (MRR), Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (nDCG). These metrics help gauge the relevancy of predictions and their order. By leveraging machine learning observability, companies can proactively identify performance degradation, uncover the worst-performing features and slices, and quickly root cause model issues to improve overall ranking model performance.