Monitoring and Maintaining Machine Learning Models Against Concept Drift in the Context of AIOps Systems

The adoption of AI systems across various sectors has increased considerably in recent years. This is a consequence of the remarkable capability of AI to extract insights from large-scale datasets, improve personalization, automate tasks and complex processes within organizations, and support more informed decision-making. Notable examples include the financial sector, where AI is applied to monitor transactions and accelerate credit decision processes; healthcare, where AI contributes to drug discovery and assists clinicians in the early diagnosing; manufacturing, where predictive maintenance using AI systems help reduce costs and mitigate the risks associated with unexpected failures; and software engineering, where AI supports anomaly detection, fault prediction, and resource demand forecasting in large-scale, complex systems.
Despite the widespread adoption and potential of AI systems, most research has been focused on model development, while investigations into their lifecycle and evolution in production environments remain at an early stage. This research path is particularly relevant for AI practitioners, who are responsible for ensuring the reliability, functionality, and predictive accuracy of deployed systems. To bridge the gap between scientific research and the practical needs of industry practitioners, this thesis focuses on two key aspects of the AI lifecycle: techniques for monitoring and maintaining AI systems over time.....