INTEGRATING MACHINE LEARNING MODELS WITH INFRASTRUCTURE AUTOMATION TOOLS FOR ENHANCED DECISION-MAKING IN INFRASTRUCTURE MANAGEMENT

Abstract

This research paper explores the transformative potential of integrating machine learning (ML) models with core infrastructure automation tools Ansible, Terraform, and Kubernetes to revolutionize decision-making in IT infrastructure management. The paper aims to demonstrate how ML can automate and optimize decisions related to scaling and adapting infrastructure, thereby enhancing operational efficiency, reducing costs, and improving system resilience. Through a comprehensive examination, we detail the process of developing and deploying ML models that predictively manage system demands and proactively address system maintenance and configuration challenges. We discuss both the technological frameworks and the practical implementations of ML within these tools, highlighting the significant benefits of predictive analytics in dynamic resource allocation and infrastructure scaling. Additionally, we outline the key challenges such as integration complexity, data security, and the need for continuous model training and evaluation to adapt to evolving system environments. The goal of this study is to provide a blueprint for organizations looking to leverage advanced analytics to foster a more proactive, resilient, and cost-effective infrastructure management strategy. This approach not only promises substantial improvements in operational capabilities but also aligns with strategic business outcomes by ensuring IT infrastructures are robust, scalable, and aligned with future technological advancements