<scp>ML</scp> ‐Driven <scp>DevOps</scp> : An Empirical Framework for Predictive Optimization and Intelligent Automation in <scp>CI</scp> / <scp>CD</scp> Pipelines

ABSTRACT Traditional DevOps pipelines often struggle with scalability, adaptability, and intelligence, particularly in distributed microservices and hybrid cloud environments, where reactive monitoring, static resource allocation, and manual interventions contribute to frequent failures, longer recovery times, and inefficient resource utilization. This study proposes an adaptive ML‐Driven DevOps (ML‐DevOps) framework designed to transform reactive CI/CD pipelines into proactive, self‐optimizing systems through predictive analytics, anomaly detection, reinforcement learning, and intelligent resource optimization. The framework integrates five core components: a predictive analytics engine, hybrid anomaly detection system, reinforcement learning agent, resource optimizer, and orchestration layer for compatibility with mainstream DevOps tools. A 21‐month evaluation was conducted across 25 organizations representing five industries, with over 78,000 deployment events analyzed. The framework demonstrated substantial improvements in deployment reliability, recovery efficiency, and resource management, consistently reducing failures, accelerating recovery, and optimizing infrastructure use across diverse industries and organizational scales. By embedding machine learning intelligence throughout the software delivery lifecycle, the ML‐DevOps framework advances DevOps from reactive automation to intelligent, autonomous operation. Its modular, plug‐and‐play design ensures practical integration into existing toolchains, making it a scalable and domain‐agnostic solution. Future work will explore explainability, federated learning, and lightweight edge deployment to enhance transparency and adaptability.