FAULT-TOLERANT MACHINE LEARNING SYSTEMS: LEVERAGING MICROSERVICES FOR RESILIENT AI APPLICATIONS
Keywords:
Microservices, Fault Tolerance, Machine Learning, Resilient AI, Container Orchestration, AI ReliabilitySynopsis
Purpose: The purpose of this paper is to investigate how microservice architectures can be effectively employed to build fault-tolerant machine learning (ML) systems for resilient AI applications.
Design/methodology/approach: This study reviews literature across distributed systems, microservices, and fault-tolerant ML, and integrates architectural diagrams and empirical tables to frame a robust system architecture for resilient AI deployment.
Findings: Key findings reveal that microservices enhance modularity and isolate failures, enabling graceful degradation and service recovery. Container orchestration and stateful fault detection models further bolster reliability.
Practical implications: Engineers and AI architects can apply these insights to build scalable, dependable ML solutions in high-availability environments such as healthcare, finance, and autonomous systems.
Originality/value: This paper consolidates fragmented insights across disciplines into a unified perspective on fault-tolerant AI system design, with structured diagrams and implementation metrics.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
