Maintaining and Monitoring AIOps Models Against Concept Drift
Author:
Affiliation:
1. TU Delft,Software Engineering,Netherlands
2. Leibniz University,Dependable and Scalable Software Systems,Hannover,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10164724/10164725/10164758.pdf?arnumber=10164758
Reference7 articles.
1. Are Concept Drift Detectors Reliable Alarming Systems? - A Comparative Study
2. Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform
3. A survey on concept drift adaptation
4. Towards a Consistent Interpretation of AIOps Models
5. An Empirical Study of the Impact of Data Splitting Decisions on the Performance of AIOps Solutions
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2. Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World;Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI;2024-04-14
3. Test & Evaluation Best Practices for Machine Learning-Enabled Systems;2024
4. A Joint Study of the Challenges, Opportunities, and Roadmap of MLOps and AIOps: A Systematic Survey;ACM Computing Surveys;2023-10-21
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