Design an ML monitoring system for a fantasy sports app, focusing on drift, performance, outliers, and quality.

A robust ML monitoring system for a fantasy sports app should track several key areas:

• Data Drift: Monitor shifts in input data distributions using metrics like Population Stability Index (PSI) or Kullback-Leibler (KL) divergence to detect changes that could impact model accuracy.
• Model Performance: Track both operational metrics (e.g., prediction latency, uptime) and predictive performance (e.g., accuracy, MAE, RMSE). Crucially, also monitor business-level KPIs that the model influences, such as user engagement (DAU, watch time) and revenue.
• Outlier Detection: Implement mechanisms to identify anomalous data points or prediction errors that deviate significantly from expected patterns, which could indicate data quality issues or model failures.
• Data Quality: Continuously validate input data for completeness, accuracy, and consistency to ensure it meets the model's requirements.