Year: 2025
Type: article
Abstract: This comprehensive article explores the evolution and implementation of cloud infrastructure for large-scale machine learning systems. The article examines critical aspects of distributed training architectures, resource optimization strategies, and performance enhancement techniques in cloud environments. It addresses the challenges of scaling artificial intelligence workloads while maintaining efficiency and cost-effectiveness. The article analyzes various approaches to infrastructure design, including parameter server and ringallreduce architectures, along with methods for optimizing data pipelines and model training processes. The article also covers monitoring systems, security considerations, and emerging trends in cloud-based machine learning deployments, providing insights into best practices for implementing scalable AI solutions.
Author: Abdul Muqtadir Mohammed
Institution: University at Buffalo, State University of New York
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4407719187