Cost-effective Cloud Architectures for Large-scale Machine Learning Workloads

Abstract

The optimization of cloud infrastructure for real-time AI processing presents a critical challenge and opportunity for organizations seeking to leverage machine learning (ML) at scale. This paper explores the strategies, case studies, and ethical considerations associated with achieving cost-effective cloud architectures for large-scale ML workloads. By examining real-world examples from leading cloud providers and international perspectives, we identify best practices and future directions for organizations navigating the complexities of cloud-based ML deployments.