A COMPARATIVE STUDY OF MONOLITHIC AND MICROSERVICES ARCHITECTURES FOR ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPLICATIONS
Keywords:
Microservices, Monolithic Architecture, Machine Learning, Artificial Intelligence, Software Architecture, Scalability, DeploymentSynopsis
Purpose – This paper compares monolithic and microservices architectures for deploying AI/ML applications in real-world systems.
Design/methodology/approach – A qualitative comparative analysis of monolithic vs. microservices approaches is conducted, using literature from published works, focusing on their performance, scalability, and development ease.
Findings – Microservices offer better scalability, modularity, and CI/CD integration, but add architectural complexity. Monolithic architectures are easier to debug and deploy for smaller applications but are not suitable for scaling dynamic AI/ML workloads.
Practical implications – Developers and architects can select optimal architectural paradigms based on system complexity, data flow, and operational goals.
Originality/value – This study uniquely focuses on AI/ML-specific applications and provides comparative insight through analysis of academic and industrial practices.
References
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