Devops And Ai: Automating Software Delivery Pipelines For Continuous Integration And Deployment

Over the past few years, AI has proliferated across multiple industries, including software development. The integration of AI in processes such as DevOps has proven to be particularly useful in improving the efficiency and scope of Continuous Integration and Continuous Deployment (CI/CD) pipelines. This paper assesses the impact of AI automating software delivery processes by focusing on optimizing performance, minimizing errors, and decreasing time-to-deployment cycles. It examines the application of several AI methods, like reinforcement learning, multi-agent systems, and anomaly detection, in real-time DevOps workflows. It proposes a personalized AI architecture aimed at automating decision making, predicting system failures, and supporting intelligent resource allocation. Through a case study, this research captures the improvements in deployment speed, build time, and error detection with the application of these models. In addition, the ethical concerns and risks of applying AI in DevOps, specifically issues of transparency, explainability, and security, is examined. The results state that AI stands to substantially improve the workflow of practitioners within the DevOps paradigm by solving software delivery problems, all while optimizing on reliability and speed while addressing the scalability and rigidity issues of traditional CI/CD pipelines. The paper suggests directions for further research, calling for the development of responsible AI models suited for deployment in complex and large-scale software ecosystems.