Monitoring Model Performance

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Monitoring model performance is crucial after deploying a machine learning model to ensure it continues to meet the expected standards. Initially, a model may perform well, but over time, its effectiveness can decline due to various factors such as data drift, changes in user behavior, or overfitting. Regular monitoring helps identify these issues early, allowing for timely interventions.

Key performance indicators (KPIs) such as accuracy, F1-score, and ROC AUC are essential for evaluating model performance. However, in cases where true labels are not available immediately, alternative methods like analyzing the distribution of predictions and prediction probabilities can be employed. Significant deviations from the training data distribution may indicate performance degradation, while low prediction probabilities can signal that the model is struggling in deployment ²³.

Incorporating human oversight can also be beneficial, especially when predictions are significantly off, even if the model’s confidence is high. Techniques like shadow mode can be used to test new models in a production environment without affecting users until they are validated ²⁴.

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