Designing Self-Healing Data Pipelines for Autonomous and Continuous AI  Operations

Sunil Kumar Mudusu

Designing Self-Healing Data Pipelines for Autonomous and Continuous AI Operations

Authors

Sunil Kumar Mudusu

Keywords:

Self-healing pipelines, Bayesian networks, anomaly detection, AI operations, automated remediation, scalability and performance evaluation.

Abstract

This study design and analyzes a selfhealing data pipeline to perform ongoing AIprocesses, with special interest in anomaly detection,root-cause detection and automated remediation. The pipeline includes the methods used, whichinclude Z-score anomaly detection

References

Steenwinckel, B., De Paepe, D., Vanden Hautte, S., Heyvaert, P., Bentefrit, M., Moens, P., Dimou, A., Van Den Bossche, B., De Turck, F., Van Hoecke, S. and Ongenae, F., 2021. FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning. Future Generation Computer Systems, 116, pp.30-48.

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Published

2024-03-20

How to Cite

Sunil Kumar Mudusu. (2024). Designing Self-Healing Data Pipelines for Autonomous and Continuous AI Operations . Journal of Computational Analysis and Applications (JoCAAA), 33(2), 1238–1247. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5386