Comparative Analysis of Monolithic and Microservices Architectures for Large-Scale Machine Learning Systems
Keywords:
Monolithic Architecture, Microservices, Machine Learning, Scalability, Distributed Systems, ETL, Fault Tolerance, DevOpsSynopsis
Purpose: This paper investigates the comparative efficiency of monolithic versus microservices architectures for large-scale machine learning (ML) systems, focusing on scalability, maintainability, and performance.
Design/methodology/approach: The study analyzes deployment models in ML contexts and evaluates them based on modularity, system reliability, and integration capability.
Findings: Microservices demonstrate higher scalability and maintainability for ML systems, enabling rapid deployment, fault isolation, and easier model versioning. Monolithic systems, while simpler to deploy initially, hinder real-time performance and continuous ML lifecycle integration.
Practical implications: ML practitioners designing scalable infrastructures should prioritize microservices, especially for dynamic environments with evolving models and data pipelines.
Originality/value: This paper uniquely synthesizes architectural impacts on ML lifecycle performance, supported by empirical findings from a decade of system engineering literature.
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