Model Monitoring
Model Monitoring is the continuous process of tracking a machine learning model's performance, behavior, and operating conditions after deployment to identify issues and changes that may affect its reliability.
What is Model Monitoring?
Model monitoring involves tracking metrics such as prediction accuracy, latency, error rates, data quality, and changes in input or output distributions. It can also detect issues such as data drift, model degradation, unexpected behavior, and changes in the environment in which the model operates.
Why is Model Monitoring Important?
A model that performs well during testing can become less effective as real-world conditions change. Continuous monitoring helps teams identify performance problems early, investigate their causes, and determine when a model needs recalibration, retraining, or replacement.
Common use cases
Model monitoring is commonly used for fraud detection, recommendation systems, forecasting, predictive maintenance, credit scoring, computer vision, and production AI applications.