Cloud as a Platform for AI/ML: Democratization, Services and Architectures

Abstract

The rapid development of Artificial Intelligence (AI) and Machine Learning (ML) systems necessitates substantial computational resources, specialized tools, and scalable storage solutions. This research examines how cloud computing systems function as an essential infrastructure that supports the current AI/ML revolution. The paper discusses three crucial elements of cloud infrastructure that support AI/ML operations through GPU and TPU acceleration, data lake scalability, AWS SageMaker, Azure Machine Learning, and Google Vertex AI managed services. The paper examines current architectural designs and MLOps life cycles that support automated model development and deployment at scale while ensuring reproducibility. Cloud-based AI systems create a democratizing effect, which enables organizations of every size to access AI technology through cost-effective solutions that eliminate hardware purchase requirements. The paper examines essential problems, including managing costs, protecting data security, and minimizing vendor dependence. The paper delivers a complete assessment of cloud services and their advantages and disadvantages to demonstrate how cloud technology drives AI innovation and accessibility.