Model Monitoring

Model monitoring focuses on maintaining the accuracy and reliability of deployed machine learning models by continuously assessing their performance and detecting anomalies. Current research emphasizes developing methods to explain performance degradation (e.g., using explainable AI techniques), accurately estimating performance even with delayed or absent ground truth labels (e.g., leveraging model confidence scores), and detecting various forms of data drift using statistical measures and deep learning approaches. This field is crucial for ensuring the trustworthiness and safety of AI systems across diverse applications, from healthcare and finance to manufacturing and supply chain management, by providing actionable insights for timely intervention and improved model robustness.