[2412.11308] From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection

Statistics > Machine Learning arXiv:2412.11308 (stat) [Submitted on 15 Dec 2024 (v1), last revised 6 Apr 2026 (this version, v2)] Title:From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection Authors:Ugur Dar, Mustafa Cavus View a PDF of the paper titled From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection, by Ugur Dar and 1 other authors View PDF HTML (experimental) Abstract:Predictive models often degrade in performance due to evolving data distributions, a phenomenon known as data drift. Among its forms, concept drift, where the relationship between explanatory variables and the response variable changes, is particularly challenging to detect and adapt to. Traditional drift detection methods often rely on metrics such as accuracy or marginal variable distributions, which may fail to capture subtle but important conceptual changes. This paper proposes a novel method, Profile Drift Detection (PDD), which enables both the detection of concept drift and an enhanced understanding of its underlying causes by leveraging an explainable AI tool: Partial Dependence Profiles (PDPs). PDD quantifies changes in PDPs through new drift metrics that are sensitive to shifts in the data stream while remaining computationally efficient. This approach is aligned with MLOps practices, emphasizing continuous model monitoring and adaptive retraining in dynamic environments. Experiments on synthetic and real-world datasets demonstrate t...