From on-premise to cloud: Evolving IT infrastructure for the AI age

Abstract

The transition from traditional on-premise IT infrastructure to cloud-based systems represents a fundamental shift necessary for the advancement and implementation of artificial intelligence (AI) technologies. This paper explores the critical role of cloud computing in supporting AI, emphasizing its benefits in terms of scalability, flexibility, and cost-efficiency. It examines the limitations of on-premise systems, the advantages of various cloud models (IaaS, PaaS, SaaS), and key considerations for successful cloud migration, such as data security, compliance, and cost analysis. Through a detailed case study of a financial institution's cloud migration for AI implementation, this study highlights the challenges, solutions, and lessons learned in the process. The case study provides practical insights into the phased approach to cloud migration, the use of hybrid cloud solutions as transitional strategies, and the importance of training and skill development for IT teams to ensure business continuity during the transition. Furthermore, the paper discusses future trends in cloud and AI integration, including the impact of emerging cloud technologies like server less and edge computing, and the pivotal role of 5G in enhancing cloud-based AI applications. It predicts the future landscape of cloud and AI integration, emphasizing the increasing adoption of hybrid and multi-cloud strategies, investment in data privacy and security, and the integration of AI with other emerging technologies such as IoT and block-chain. The paper concludes by underscoring the importance of cloud infrastructure in the AI age and calls for businesses to evaluate their cloud readiness to stay competitive in a rapidly evolving digital landscape. By embracing cloud-based solutions, businesses can unlock new opportunities for innovation, efficiency, and growth, positioning themselves at the forefront of the AI revolution.