This article explores methods for reducing deployment time in large-scale cloud systems through the implementation of automated DevOps pipelines. The focus lies on integrating the principles of Continuous Integration (CI) and Continuous Delivery (CD), adopting Infrastructure as Code (IaC), leveraging containerization and orchestration tools, and incorporating AI-driven solutions to optimize deployment workflows. The theoretical foundations of DevOps and CI/CD are examined alongside empirical data derived from comparative analyses of manual and automated deployment processes. The study also offers practical recommendations for improving the efficiency of cloud infrastructure. Findings confirm that the holistic application of these methods leads to reduced deployment times, lower operational costs, and enhanced system resilience. The insights presented in this paper will be relevant to both researchers and practitioners working on distributed cloud system development, where automated DevOps pipelines serve as a critical tool for minimizing deployment time and streamlining CI/CD processes. The study's outcomes and methodologies hold potential value for academia as well as industry professionals seeking to enhance the scalability, efficiency, and resilience of modern IT infrastructures.

DevOps, CI/CD, automation, cloud systems, Infrastructure as Code, containerization, deployment optimization, artificial intelligence, scalability, FinOps.

Gangu K., Mishra R. DevOps and continuous delivery in cloud-based CDN architectures //International Journal of Research in All Subjects in Multi Languages (IJRSML). – 2025. – Vol. 13 (1). – pp. 69-90.

Ugwueze V. U., Chukwunweike J. N. Continuous integration and deployment strategies for streamlined DevOps in software engineering and application delivery //International Journal of Computer Applications Technology and Research. – 2024. – Vol. 14 (1). – pp. 1-24.

Gaur I. et al. Optimizing Cloud Applications with DevOps // 2024 International Conference on Computational Intelligence and Computing Applications (ICCICA). – IEEE. - 2024. – Vol. 1. – pp. 68-74.

Suraj P. Edge Computing vs. Traditional Cloud: Performance & Security Considerations //Spanish Journal of Innovation and Integrity. – 2022. – Vol. 12. – pp. 312-320.

Patchamatla P. S., Owolabi I. O. Integrating serverless computing and kubernetes in OpenStack for dynamic AI workflow optimization //International Journal of Multidisciplinary Research in Science, Engineering and Technology. – 2020. – Vol. 3 (12). – pp. 1359-1375.

Suraj P. An Overview of Cloud Computing Impact on Smart City Development and Management //International Journal of Trend in Scientific Research and Development. – 2024. – Vol. 8 (6). – pp. 715-722.

Vangala V. DevOps for Legacy Systems: Strategies for Successful Integration. – 2025. – pp.1-10.

Aminu M. et al. Enhancing cyber threat detection through real-time threat intelligence and adaptive defense mechanisms //International Journal of Computer Applications Technology and Research. – 2024. – Vol. 13 (8). – pp. 11-27.

Vemuri N., Thaneeru N., Tatikonda V. M. AI-Optimized DevOps for Streamlined Cloud CI/CD //International Journal of Innovative Science and Research Technology. – 2024. – Vol. 9 (2). – pp. 504-510.

Krishnamurthy S. et al. Application of Docker and Kubernetes in Large-Scale Cloud Environments //International Research Journal of Modernization in Engineering, Technology and Science. – 2020. – Vol. 2 (12). – pp. 1022-1030.

Dave S. A. et al. Designing Resilient Multi-Tenant Architectures in Cloud Environments //International Journal for Research Publication and Seminar. – 2020. – Vol. 11 (4). – pp. 356-373.