CLOUD-BASED AI/ML MODEL DEPLOYMENT: A COMPARATIVE ANALYSIS OF MANAGED AND SELF-MANAGED PLATFORMS

Abstract

The widespread adoption of artificial intelligence and machine learning (AI/ML) technologies has created an urgent need for efficient and scalable deployment solutions across industries. This article presents a comprehensive analysis of cloud-based AI/ML model deployment strategies, examining both managed platforms offered by major cloud providers (AWS SageMaker, Google Vertex AI, and Microsoft Azure Machine Learning) and self-managed infrastructure solutions. Through systematic evaluation of platform capabilities, infrastructure requirements, and organizational considerations, the article develops a decision framework to guide enterprises in selecting appropriate deployment architectures. The article analysis reveals that while managed platforms offer significant advantages in terms of reduced complexity, automated infrastructure management, and faster time-to-market, self-managed solutions provide superior customization capabilities and potential cost benefits at scale for organizations with sufficient technical expertise. The article synthesizes implementation data from multiple enterprise case studies to identify critical success factors in AI/ML deployment, including infrastructure scalability, monitoring capabilities, and resource optimization. Furthermore, the article proposes a novel evaluation matrix for assessing the total cost of ownership across different deployment scenarios, incorporating both direct infrastructure costs and indirect expenses related to expertise and maintenance. These findings contribute to the growing body of knowledge on enterprise AI/ML operations while providing practical guidance for organizations navigating the complex landscape of cloud-based model deployment strategies.