Monitor Model Performance

You monitor model performance in production by tracking both functional and operational metrics.

Functional monitoring checks the health of the data and the model:

• Data quality: Monitor for missing values, duplicates, and syntax errors.
• Data/feature drift: Compare current input data to training data using stats like KL divergence, PSI, chi-squared, etc.
• Model drift: Check if model accuracy drops over time due to changing patterns in the data.

Operational monitoring keeps the system running smoothly:

• System health: Tracks latency, errors, and memory usage.
• Input data health: Watch for type mismatches, nulls, and out-of-range values.
• Model performance: Use precision/recall, RMSE, or top-k accuracy depending on the use case.
• Business KPIs: Tie model performance to actual business outcomes (e.g., conversions, revenue impact).