A Cloud-Driven DevOps Approach for End-to-End Machine Learning Workflows

Abstract: The fusion of Generative AI and DevOps is reshaping the landscape of cloud computing by introducing dynamic intelligence into automated software delivery pipelines. Traditionally, DevOps has relied on deterministic scripts and manual configurations to manage infrastructure, CI/CD workflows, and system operations. However, as applications scale across hybrid and multi-cloud environments, these static approaches face limitations in flexibility, responsiveness, and resilience. Generative AI addresses these challenges by leveraging large language models (LLMs) and agentic architectures to understand context, generate code, interpret telemetry, and take proactive actions.Our research demonstrates that generative AI is not just a tool for automation but a catalyst for building self- optimizing, context-aware, and resilient DevOps systems. As organizations adopt these technologies, they will transition from reactive incident handling to predictive and autonomous operations, setting the stage for the next era of intelligent cloud engineering.

Keywords: Generative AI, DevOps Automation, Cloud Workflow Optimization, Large Language Models (LLMs)