From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors

Abstract

The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system's life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model's performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model's performance in the

Forward Learning of Large Language Models by Consumer Devices

“…However, enabling only model's inference on the device is not enough. The performance of the AI models, in fact, deteriorates as time passes since the last training cycle; phenomenon known as concept drift [16], hence mandates model's parameter updates from time to time. The prominent field of on-device learning (ODL) [17] allows for machine learning (ML) models deployed on edge devices to adapt to the continuously changing data statistics, which are collected by the sensors, and performing model's parameter training.…”