End-to-End Intelligent Automation Using AI and Machine Learning in DevOps Pipelines for Real-Time Analytics
DOI:
https://doi.org/10.15680/IJMRSETM.2025.0101008Keywords:
Intelligent Automation, DevOps Pipelines, Artificial Intelligence, Machine Learning, Real-Time Analytics, Continuous Integration, Continuous Deployment, Autonomous Decision Systems, MLOps, Cloud-Native SystemsAbstract
The growing complexity of modern software systems and the demand for rapid, data-driven decision-making have accelerated the adoption of intelligent automation within DevOps pipelines. This paper presents an end-to-end intelligent automation framework that integrates artificial intelligence (AI) and machine learning (ML) to enable real-time analytics and autonomous decision support across the software delivery lifecycle. The proposed approach embeds ML-driven monitoring, anomaly detection, and predictive analytics into continuous integration and continuous deployment (CI/CD) pipelines, allowing systems to adapt dynamically to changing workloads and operational conditions. By leveraging automated feedback loops, the framework improves deployment efficiency, system reliability, and operational visibility while reducing manual intervention. Experimental observations demonstrate enhanced pipeline performance, faster incident detection, and improved decision-making accuracy. The results highlight the effectiveness of AI- and ML-enabled DevOps automation in supporting scalable, resilient, and data-driven enterprise systems.
References
1. Humble, J., & Farley, D. (2010). Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley.
2. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
3. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
4. Sugumar, R. (2025, March). Diabetes Insights: Gene Expression Profiling with Machine Learning and NCBI Datasets. In 2025 7th International Conference on Intelligent Sustainable Systems (ICISS) (pp. 712-718). IEEE.
5. Chandramohan, A. (2017). Exploring and overcoming major challenges faced by IT organizations in business process improvement of IT infrastructure in Chennai, Tamil Nadu. International Journal of Mechanical Engineering and Technology, 8(12), 254.
6. Paul, D., Poovaiah, S. A. D., Nurullayeva, B., Kishore, A., Tankani, V. S. K., & Meylikulov, S. (2025, July). SHO-Xception: An Optimized Deep Learning Framework for Intelligent Intrusion Detection in Network Environments. In 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3) (pp. 1-6). IEEE.
7. Nagarajan, G. (2022). Advanced AI–Cloud Neural Network Systems with Intelligent Caching for Predictive Analytics and Risk Mitigation in Project Management. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7774-7781.
8. Vimal Raja, G. (2024). Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
9. Kumar, S. N. P. (2022). Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 67–79. https://ijhit.info/index.php/ijhit/article/view/140/136
10. Bass, L., Weber, I., & Zhu, L. (2015). DevOps: A Software Architect’s Perspective. Addison-Wesley.
11. Navandar, P. (2023). Guarding Networks: Understanding the Intrusion Detection System (IDS). Journal of biosensors and bioelectronics research. https://d1wqtxts1xzle7.cloudfront.net/125806939/20231119-libre.pdf?1766259308=&response-content-disposition=inline%3B+filename%3DGuarding_Networks_Understanding_the_Intr.pdf&Expires=1767147182&Signature=H9aJ73csgfALZ~2B89oBRyYgz57iuooJUU0zKPdjpmQjunvziuvJjd~r8gYT52Ah6RozX-LUpFB14VO8yjXrVD73j1HN9DAMi1PSGKaRbcI8gBbrnFQQGOhTO7VYkGcz3ylDLZJatGabbl5ASNiqe0kINjsw6op5mJzXUoWLZkmret8YBzR1b6Ai8j4SCuZ2kc75dAfryQSZDKuv9ISFi9oHyMxEwWKkyNDnnDP~0EW3dBp7qmwPJVbnm7wSQFFU9AUx5o3T742k80q8ZxvS8M-63TZkyb5I3oq6zBUOCVgK471hm2K9gYtYPrwePdoeEP5P4WmIBxeygrqYViN9nw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
12. Kumar, R. K. (2023). AI‑integrated cloud‑native management model for security‑focused banking and network transformation projects. International Journal of Research Publications in Engineering, Technology and Management, 6(5), 9321–9329. https://doi.org/10.15662/IJRPETM.2023.0605006
13. Kavuru, L. T. (2025). Invisible Hands: The Rise of Unseen AI Partners in Remote Project Decision Loops. International Journal of Research and Applied Innovations, 8(5), 13006-13014.
14. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.
15. Bussu, V. R. R. (2024). End-to-End Architecture and Implementation of a Unified Lakehouse Platform for Multi-ERP Data Integration using Azure Data Lake and the Databricks Lakehouse Governance Framework. International Journal of Computer Technology and Electronics Communication, 7(4), 9128-9136.
16. Islam, M. M., Hasan, S., Rahman, K. A., Zerine, I., Hossain, A., & Doha, Z. (2024). Machine Learning model for Enhancing Small Business Credit Risk Assessment and Economic Inclusion in the United State. Journal of Business and Management Studies, 6(6), 377-385.
17. Parameshwarappa, N. (2025). Building Bridges: The Architecture of Digital Inclusion in Public Services. Journal Of Multidisciplinary, 5(8), 96-103.
18. Kumar, S. S. (2023). AI-Based Data Analytics for Financial Risk Governance and Integrity-Assured Cybersecurity in Cloud-Based Healthcare. International Journal of Humanities and Information Technology, 5(04), 96-102.
19. Kagalkar, A., Sharma, A., Chaudhri, B., & Kabade, S. (2024). AI-Powered Pension Ecosystems: Transforming Claims, Payments, and Member Services. International Journal of AI, BigData, Computational and Management Studies, 5(4), 145-150.
20. Meka, S. (2025). Redefining Data Access: A Decentralized SDK for Unified and Secure Data Retrieval. Journal Code, 1325, 7624.
21. Rahman, M. R., Tohfa, N. A., Arif, M. H., Zareen, S., Alim, M. A., Hossen, M. S., ... & Bhuiyan, T. (2025). Enhancing android mobile security through machine learning-based malware detection using behavioral system features. https://www.researchgate.net/profile/Nasrin-Tohfa/publication/397379591_Enhancing_android_mobile_security_through_machine_learning-based_malware_detection_using_behavioral_system_features/links/6912b141c900be105cc0b8b6/Enhancing-android-mobile-security-through-machine-learning-based-malware-detection-using-behavioral-system-features.pdf
22. Kasaram, C. R. (2023). Structuring Reusable API Testing Frameworks with Cucumber-BDD and REST Assured. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(1), 7626-7632.
23. Moustafa, N., & Slay, J. (2016). The Significant Features of the CICIDS2017 Dataset for Network Intrusion Detection Systems. IEEE Access, 6, 41241–41258.
24. Vasugi, T. (2022). AI-Optimized Multi-Cloud Resource Management Architecture for Secure Banking and Network Environments. International Journal of Research and Applied Innovations, 5(4), 7368-7376.
25. Achari, A. P. S. K., & Sugumar, R. (2025, March). Performance analysis and determination of accuracy using machine learning techniques for decision tree and RNN. In AIP Conference Proceedings (Vol. 3252, No. 1, p. 020008). AIP Publishing LLC.
26. Binu, C. T., Kumar, S. S., Rubini, P., & Sudhakar, K. (2024). Enhancing Cloud Security through Machine Learning-Based Threat Prevention and Monitoring: The Development and Evaluation of the PBPM Framework. https://www.researchgate.net/profile/Binu-C-T/publication/383037713_Enhancing_Cloud_Security_through_Machine_Learning-Based_Threat_Prevention_and_Monitoring_The_Development_and_Evaluation_of_the_PBPM_Framework/links/66b99cfb299c327096c1774a/Enhancing-Cloud-Security-through-Machine-Learning-Based-Threat-Prevention-and-Monitoring-The-Development-and-Evaluation-of-the-PBPM-Framework.pdf
27. Sakinala, K. (2025). Advancements in Devops: The Role of Gitops in Modern Infrastructure Management. International Journal of Information Technology and Management Information Systems, 16(1), 632-646.
