Modern software engineering practices increasingly rely on DevOps pipelines to enable continuous integration, continuous delivery (CI/CD), and rapid deployment cycles. However, as systems scale in complexity and distributed architectures become more prevalent, traditional DevOps pipelines face challenges such as deployment failures, inefficient resource utilization, rollback overheads, and unpredictable performance degradation
This research proposes an autonomous DevOps pipeline framework enhanced with machine learning-based deployment optimization and failure prediction techniques. The framework leverages historical deployment data, system logs, performance metrics, and infrastructure telemetry to predict deployment risks and optimize release strategies in real time
Machine learning models such as gradient boosting, recurrent neural networks, and anomaly detection algorithms are integrated into the CI/CD pipeline to forecast deployment success probabilities and detect early failure signals. Additionally, reinforcement learning techniques are used to dynamically optimize deployment strategies such as canary releases, blue-green deployments, and rolling updates
The system enables self-adaptive pipeline behavior, where deployment decisions are automatically adjusted based on predictive insights. This reduces downtime, improves system reliability, and enhances deployment efficiency. The proposed approach demonstrates how AI-driven automation can transform traditional DevOps pipelines into intelligent, self-healing, and autonomous systems capable of continuous optimization