Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article looks at the planning patterns and operational habits necessary to ensure stability, performance, and reliability in the large-scale platforms of DevOps. It centers on mission-critical schemes of source code management like GitLab, GitHub, and Gerrit to list how multinational organizations with multilateral engineering foundations have redesigned their system architecture and operation frameworks to gain global development. Four major dimensions covered in the discussion are the difficulty of scaling distributed development systems, how to make platforms resilient, zero-downtime upgrades, and the AI-powered predictive analytics of platform management. The use of artificial intelligence in predictive resource administration, observing anomalies, and capacity planning is paid special attention to. Multi-primary configurations, distributed replications on a world-scale, blue-green deployment, and assistance of AI in making predictions are considered through the lens of how they affect the system stability, developer productivity, efficiency in operations, and compliance requirements. Based on the industry metrics and general experience in a particular operational field, the study represents quantifiable positive outcomes, such as a massive decline in the number of cases of incidents, a massive drop in the expenses of the infrastructure, and a substantial rise in the rates of developer satisfaction. Such a combination of resilience engineering and AI-based predictive management targets a gap in the existing DevOps literature, in which it is rare to find such hybrid approaches reflected in a single operational scheme. According to the obtained results, the implementation of the broad-based strategy, which is integrated with resilient architecture and AI-amplified predictive analytics, would contribute to enhancing the overall stability of operation significantly, optimising resource consumption, and ensuring sustainable global development practices. In the future, as organizations move forward with engineering entities spread across various geographical locations, such strategies would transform so as to become a necessary part of modern evolution, DevOps platforms.