The Integration of Machine Learning and Cloud Computing for Scalable AI Systems

Abstract

The integration of Machine Learning (ML) and Cloud Computing has emerged as a powerful paradigm for building scalable AI systems. Cloud computing offers flexible, on-demand resources that enhance the efficiency and scalability of ML models. This paper explores how cloud infrastructure, combined with ML algorithms, can be leveraged to solve complex, data-intensive tasks in various industries, including healthcare, finance, and autonomous systems. The study emphasizes the benefits of using cloud platforms for large-scale data storage, model training, and real-time AI inference. Additionally, it outlines challenges related to privacy, security, and the optimization of cloud-based AI systems.