Model Governance, Drift Monitoring, and Automated Retraining for Production Machine Learning
Abstract
The deployment of machine learning (ML) models in production environments requires continuous oversight to ensure reliability, accuracy, transparency, and compliance with evolving business and operational requirements. Model governance, drift monitoring, and automated retraining have emerged as essential components of the machine learning lifecycle, enabling organizations to maintain model performance and support responsible AI adoption. Model governance establishes structured policies, procedures, and accountability mechanisms for model development, validation, deployment, monitoring, and retirement. It promotes transparency, reproducibility, risk management, data privacy, and regulatory compliance throughout the model lifecycle. Drift monitoring complements governance by identifying changes in input data distributions, relationships between features and target variables, and predictive performance over time. Such changes, commonly categorized as data drift, concept drift, and model performance degradation, can significantly affect the reliability of production models. Automated retraining addresses these challenges by initiating model updates when predefined performance thresholds, drift indicators, or operational conditions are triggered.
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