Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques

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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.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/ACM 7th International Workshop on Green And Sustainable Software, GREENS 2023
PublisherIEEE
Pages17-18
Number of pages2
ISBN (Electronic)9798350312386
DOIs
Publication statusPublished - 2023
Event7th IEEE/ACM International Workshop on Green And Sustainable Software, GREENS 2023 - Melbourne, Australia
Duration: 14 May 2023 → …

Publication series

NameProceedings - 2023 IEEE/ACM 7th International Workshop on Green And Sustainable Software, GREENS 2023

Conference

Conference7th IEEE/ACM International Workshop on Green And Sustainable Software, GREENS 2023
Country/TerritoryAustralia
CityMelbourne
Period14/05/23 → …

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • concept drift adaptation
  • sustainable model maintenance
  • sustainable model retraining

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