AI/ML-Driven DevOps Automation: Transforming Software Delivery Through Intelligent Automation

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) is transforming DevOps, enabling it to become a predictive, intelligent, and adaptive process across the software delivery lifecycle. This study aims to create an AI/ML based DevOps automation framework for deployment risk prediction, deployment optimization, rollback management, and continuous monitoring in a single CI/CD workflow. The framework’s data components are the build logs, deployment history, infrastructure monitoring, configuration repositories, incident records, and user feedback, which are then combined with DevOps data into a machine learning pipeline. The data is then preprocessed, combined, and processed by feature engineering to be split into training, validation, and testing sets. A Random Forest model is employed for deployment risk prediction, while an AI-driven decision engine uses predicted risk and operational conditions to support deployment optimization, resource allocation, automated rollback, and continuous monitoring. Experimental evaluation demonstrates a 50% reduction in average build time, a 60% reduction in deployment failure rate, a 50% reduction in rollback frequency, a 34% improvement in deployment risk prediction accuracy, and a 68% reduction in false positive rate. The findings demonstrate improved software delivery efficiency, reliability, operational stability, and decision-making.