Publications

Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques

Authors: Lorena Poenaru-Olaru, June Sallou, Luís Cruz, Jan S. Rellermeyer, Arie van Deursen

Published in: GREENS.

Abstract: Deployed machine learning systems often suffer from accuracy degradation over time generated by constant data shifts, also known as concept drift. Therefore, these systems require regular maintenance, in which the machine learning model needs to be adapted to concept drift. The literature presents plenty of model adaptation techniques. The most common technique is periodically executing the whole training pipeline with all the data gathered until a particular point in time, yielding a massive energy footprint. In this paper, we propose a research path that uses concept drift detection and adaptation to enable sustainable AI systems.

@INPROCEEDINGS{poenaru2023retrain,
author={Lorena Poenaru-Olaru and June Sallou and Luis Cruz and Jan S. Rellermeyer and Arie van Deursen},
booktitle={GREENS}, 
title={Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques}, 
year={2023}}

Read me: Preprint.