AI-Driven DevOps: Enhancing Automation, Efficiency, and Reliability in Software Development

Abstract

We are seeing that software development is changing very fast, and it demands only faster and more secure delivery methods. Traditional DevOps practices automate workflows effectively, but they have limitations in predictive analytics and anomaly detection itself. Further, these practices cannot make intelligent decisions automatically. This thesis studies how to add AI into DevOps work processes, focusing on CI/CD pipelines, as per the need to improve automation and efficiency. The research examines AI integration, which means better workflow management and system strength. Usually, the research visualizes a complete method for creating and testing AIpowered CI/CD systems that helps to cover the some steps like data collection, model training, and monitoring. The methodology includes all essential processes from preprocessing to evaluation and explainability. We use real CI/CD log data to create AI-powered workflows with machine learning models. These models definitely help with automatic testing, better deployments, predicting failures, and finding security problems. The dataset is further divided into training, validation, and testing subsets for proper model evaluation. The model itself achieved 100% accuracy, precision, and recall in simulated experiments. As per comparative analysis, AI-driven DevOps shows better results than traditional DevOps regarding build success rates, deployment speed, recovery time, and security detection. When we do AI integration is used in DevOps processes, it helps to reveal significant improvements. Case studies in the real world show that AI methods work well in large software systems. Moreover, these approaches help reduce system failures, fix problems faster, and make security better. This research surely helps connect traditional automation with smart decision-making in DevOps. Moreover, the findings bridge an important gap between these two approaches. Basically, when organizations add AI models to their CI/CD pipelines, they get the same result - software delivery that can fix itself, predict problems, and adapt automatically. The study surely concludes that AI-driven DevOps is not just an improvement of existing practices. Moreover, it represents a complete transformation toward fully independent software engineering systems.