Designing Scalable Artificial Intelligence Systems Using Microservices-Based Machine Learning Architectures
Keywords:
Microservices, Machine Learning, AI Scalability, System Architecture, Cloud AI, ContainerizationAbstract
Purpose: This paper investigates the design of scalable artificial intelligence (AI) systems using microservices-based machine learning (ML) architectures.
Design/methodology/approach: A structured literature review was conducted on studies published before 2020 to assess architectural patterns, scalability mechanisms, and performance implications of microservices in ML deployments.
Findings: Microservices-based ML enhances scalability, flexibility, and continuous deployment in AI systems. However, issues such as orchestration complexity, service dependencies, and latency challenges remain.
Practical implications: This architectural approach facilitates modular deployment of AI components, promoting rapid innovation and fault isolation in large-scale enterprises.
Originality/value: This paper synthesizes key architectural paradigms and proposes an integrated framework combining ML workflows and containerized microservices for real-world scalability.
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