Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques

verfasst von

Lorena Poenaru-Olaru, June Sallou, Luis Miranda da Cruz, Jan S. Rellermeyer, Arie van Deursen

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.

Organisationseinheit(en)

Institut für Systems Engineering
Fachgebiet Verlässliche und skalierbare Softwaresysteme

Externe Organisation(en)

Delft University of Technology

Typ

Aufsatz in Konferenzband

Seiten

17-18

Anzahl der Seiten

2

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Erneuerbare Energien, Nachhaltigkeit und Umwelt

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.1109/greens59328.2023.00009 (Zugang: Geschlossen)
http://resolver.tudelft.nl/uuid:d115c5ba-f671-4713-9513-a0c7c5abf7f1 (Zugang: Offen)