AI-Driven DevOps: Leveraging Machine Learning for Automated Software Delivery Pipelines

Abstract

The convergence between artificial intelligence and DevOps is transforming the software development and delivery. This paper discusses the implementation of machine learning processes into automated deployment pipelines both in terms of theoretical foundations as well as practical results. It analyses how predictive modeling; anomaly detection and adaptive automation can help in improving the efficiency of continuous integration and deployment systems (CI/CD). The research based on the analysis of the operational experience of the environments of enterprises, as well as evaluation of various methods of using AI in optimization, indicates that the process can reduce the deployment errors by approximately 3447 percent, decrease the pipeline time by 2841 percent, and increase the efficiency of resources utilization by a fifth. The theoretical framework connects factors of statistical learning, reliability engineering, and process maturity of DevOps. Its barriers to implementation, including the data consistency, the transparency of the models and the adaptation to the organizational aspects are also mentioned. In practice experimental results have shown that supervised learning models have failure-prediction F1-scores of between 0.82 to 0.91, and reinforcement learning programs have another 23 to 38 percent better the performance of traditional rule-based systems. Altogether, the discussion highlights the necessity to stay even-handed regarding the benefits of automation and the increase in the complications of handling learning systems in manufacturing pipelines, which can be valuable information in the development of intelligent and trustworthy ways of working the DeoPs