Self-Adaptive Machine Learning Models for Robust Performance Under Data Drift and Concept Shift

Abstract

The efficacy of machine learning (ML) systems in real-world environments is often compromised due to the dynamic nature of data, where distributions evolve over time, manifesting as data drift and concept shift. This paper explores the design and implementation of self-adaptive machine learning models that retain robust predictive performance despite these challenges. We outline a unified framework that integrates online learning, drift detection, and continual adaptation without requiring constant human intervention. Through extensive review and a conceptualized architecture, we provide insights into the trade-offs between adaptation speed, model complexity, and stability. A series of structured modules — including monitoring, detection, adaptation, and evaluation — are proposed to automate the response to evolving data characteristics. This self-adaptive paradigm holds promise for industrial, healthcare, and financial systems where operational data distributions cannot be assumed static.