The Role of AI/ML in Modern DevOps: From Anomaly Detection to Predictive Operations
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© 2025 by IJCTT Journal
Volume-73 Issue-1
Year of Publication : 2025
Authors : Ravindra Agrawal
DOI :  10.14445/22312803/IJCTT-V73I1P102

How to Cite?

Ravindra Agrawal, "The Role of AI/ML in Modern DevOps: From Anomaly Detection to Predictive Operations," International Journal of Computer Trends and Technology, vol. 73, no. 1, pp. 19-25, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I1P102

Abstract
Incorporating Artificial Intelligence and Machine Learning into DevOps practices is a fundamental shift in organizations' software delivery and operations. A 2024 DORA survey found that more than 81% of organizations are considering incorporating AI into their applications [1]. This deep analysis examines how AI/ML technologies revolutionize operational efficiency, incident response, and resource optimization within DevOps workflows. This study, by carefully analyzing the current applications and developing trends, proves that entities adopting AI-driven DevOps methodologies see considerable improvement in system dependability, cost efficiency, and team output while lowering operational expenditures and the rate of human error. The paper will also address some practical implications of this convergence, including barriers to implementation, performance indicators, and future pathways in the fast-developing sphere of AI-augmented DevOps. DORA metrics continue to be the key measurement criteria for measuring the success of AI/ML within DevOps.

Keywords
DevOps, Artificial Intelligence, Continuous integration, Continuous delivery, SRE, Anomaly detection.

Reference

[1] Get the DORA Accelerate State of DevOps Report, Google Cloud. [Online]. Available: https://cloud.google.com/devops/state-of-devops
[2] Langston Nashold, and Rayan Krishnan, “Using LSTM and SARIMA Models to Forecast Cluster CPU Usage,” arXiv, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] M.U. Stsepanenka, “The Impact of Artificial Intelligence and Machine Learning Technologies on DevOps Evolution,” Belarusian State University of Informatics and Radioelectronics, 2024.
[Publisher Link]
[4] Oyekunle Claudius Oyeniran et al., “AI-driven Devops: Leveraging Machine Learning for Automated Software Deployment and Maintenance,” Engineering Science & Technology Journal, vol. 4, no. 6, pp. 728-740, 2023.
[Google Scholar]
[5] Deepika Goyal, “AI-Driven DevOps for Agile Excellence with Machine Learning,” Insights2Techinfo, 2024.
[Google Scholar] [Publisher Link]
[6] Bharath Chandra Vadde, and Vamshi Bharath Munagandla, “Integrating AI-Driven Continuous Testing in DevOps for Enhanced Software Quality,” Journal of Artificial Intelligence in Medicine, vol. 14, no. 1, pp. 1-9, 2023.
[Google Scholar] [Publisher Link]