Design and Implementation of Automated CI/CD Pipelines in DevOps

Abstract

In modern software engineering, fast and quality software delivery cannot be overemphasized in terms of maintaining a competitive advantage. Traditional development procedures tend to create delays and errors and uneven roll-outs, especially on complex systems. The article provides a description of the architecture and deployment of an automated CI/CD pipeline within a DevOps system that incorporates intelligent risk prediction that is intelligent, security validation, and resilient deployment. It is based on containerization, Docker usage, and Kubernetes orchestration, along with AI-assisted predictive analytics to estimate the risk of deployment as a Random Forest model trained on synthetic deployment data. Important improvements revealed by performance assessment include a reduction of deployment time by 72.46% and increase of the success rate by 16.67%, and a 3.63× increase in throughput compared to the manual processes. The pipeline also has a reduced Mean Time to Detect (MTTD) of 15 minutes to 1.2 minutes and Mean Time to Recovery (MTTR) of 45 minutes to 52 seconds, and 99.9% system availability is achieved. The results indicate that automated CI/CD operations may assist in the efficiency, reliability, and stability of the functioning of a contemporary DevOps system.