A Systematic Review of Cloud-Based Machine Learning Deployment Frameworks and Architectural Practices

Abstract

This study addresses the persistent problem of fragmented and insufficiently consolidated knowledge on cloud-based machine learning deployment frameworks and the architectural practices required for successful production implementation, a gap that continues to widen as organizations move from model experimentation to real-world operationalization. The purpose of the study was to develop a structured understanding of the major deployment frameworks, dominant architectural practices, operational benefits, and recurring risks associated with cloud-based machine learning deployment across real-world cases. To achieve this, the study adopted a cross-sectional, case-based systematic review design with quantitative descriptive support, synthesizing evidence from 60 reviewed studies, including 46 empirical or case-based studies (76.7%), with cases drawn from healthcare (25.0%), manufacturing and industry (23.3%), enterprise and business analytics (20.0%), smart systems and IoT (18.3%), and multi-sector contexts (13.4%). The key variables examined were deployment framework capability, architectural maturity, lifecycle governance, operational trust, scalability, maintainability, interoperability, monitoring readiness, and deployment risks. Data were analyzed through structured screening, eligibility assessment, thematic coding, frequency analysis, percentage distribution, and five-point Likert evidence scoring. The findings show that 48 studies (80.0%) identified cloud deployment frameworks as central to operational success, 51 studies (85.0%) emphasized architecture-related practices as critical to production readiness, and 44 studies (73.3%) linked deployment quality to lifecycle governance, monitoring, and maintainability. Container-orchestration ecosystems emerged as the most prominent framework category, appearing in 42 studies (70.0%) with a mean capability score of 4.45/5.00, while managed cloud ML platforms appeared in 39 studies (65.0%) with a score of 4.33/5.00. Among architectural practices, containerization was reported in 46 studies (76.7%) with a mean of 4.52/5.00, monitoring and observability in 45 studies (75.0%) with 4.49/5.00, and orchestration in 43 studies (71.7%) with 4.46/5.00. Overall deployment effectiveness scored 4.24/5.00, while major challenges remained monitoring and model drift (65.0%), security and privacy concerns (60.0%), and interoperability complexity (58.3%). The study implies that machine learning deployment success in cloud environments depends not only on framework adoption, but also on mature architecture, disciplined lifecycle governance, and context-sensitive operational design that supports repeatability, portability, trust, and long-term sustainability.