Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN) |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 98-99 |
| Seitenumfang | 2 |
| ISBN (elektronisch) | 9798350301137 |
| ISBN (Print) | 979-8-3503-0114-4 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023 - Melbourne, Australien Dauer: 15 Mai 2023 → 16 Mai 2023 |
Abstract
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 These models become outdated during the occurrence of concept drift, a term used to describe shifts in data distributions.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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Software
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
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- BibTex
- RIS
2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN). Institute of Electrical and Electronics Engineers Inc., 2023. S. 98-99.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Maintaining and Monitoring AIOps Models Against Concept Drift
AU - Poenaru-Olaru, Lorena
AU - Miranda da Cruz, Luis
AU - Rellermeyer, Jan S.
AU - Van Deursen, Arie
PY - 2023
Y1 - 2023
N2 - 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 These models become outdated during the occurrence of concept drift, a term used to describe shifts in data distributions.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.
AB - 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.
KW - AIOps
KW - concept drift adaptation
KW - concept drift detection
KW - machine learning model lifecycle
UR - http://www.scopus.com/inward/record.url?scp=85165126759&partnerID=8YFLogxK
U2 - 10.1109/CAIN58948.2023.00024
DO - 10.1109/CAIN58948.2023.00024
M3 - Conference contribution
AN - SCOPUS:85165126759
SN - 979-8-3503-0114-4
SP - 98
EP - 99
BT - 2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023
Y2 - 15 May 2023 through 16 May 2023
ER -