Serverless Kubernetes with AI-Augmented DevOps: Optimizing Cloud Infrastructure for Scalable and Cost-Efficient Deployments

Abstract

The emergence of serverless Kubernetes, combined with AI-augmented DevOps, is transforming cloud infrastructure by enabling scalable, cost-efficient deployments. This research explores the role of AI in optimizing serverless Kubernetes environments by integrating predictive resource management, automated failure detection, and intelligent workload scheduling. The study investigates how AI-driven automation enhances scalability, reduces operational costs, and minimizes human intervention in managing Kubernetes clusters. A comparative analysis of AI-based workload scheduling against traditional Kubernetes scheduling mechanisms is presented, demonstrating improvements in response time, resource utilization, and cost efficiency. Furthermore, the paper evaluates self-healing capabilities and AI-powered observability tools that contribute to proactive issue resolution and improved system resilience. Experimental results validate the efficacy of AI-driven serverless Kubernetes in optimizing cloud-native application deployments. The findings contribute to the evolution of DevOps methodologies by integrating AI and serverless computing for enhanced efficiency and reliability.