AIOps solutions enable faster discovery of failures in operational large-scale systems through machine learning models trained on operation data. These models become outdated during the occurrence of concept drift, a term used to describe shifts in data distributions. In operation data concept drift is inevitable and it impacts the performance of AIOps solutions over time. Therefore, concept drift should be closely monitored and immediate maintenance to prevent erroneous predictions is required. In this work, we propose an automated maintenance pipeline for AIOps models that monitors the occurrence of concept drift and chooses the most appropriate model retraining technique according to the drift type.
|Title of host publication||Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||2|
|Publication status||Published - 2023|
|Event||2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023 - Melbourne, Australia|
Duration: 15 May 2023 → 16 May 2023
|Name||Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023|
|Conference||2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023|
|Period||15/05/23 → 16/05/23|
Bibliographical noteGreen 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.
- concept drift adaptation
- concept drift detection
- machine learning model lifecycle