INTEGRATING ARTIFICIAL INTELLIGENCE PIPELINES WITH CLOUD-NATIVE MICROSERVICES FOR REAL-TIME DECISION MAKING
Keywords:
Cloud-Native, Microservices, Real-Time Decision Making, Artificial Intelligence, AI Pipelines, DevOps, Kubernetes, Service MeshSynopsis
Purpose: The integration of Artificial Intelligence (AI) with cloud-native microservices represents a paradigm shift in real-time decision-making capabilities.
Design/methodology/approach: This paper investigates the synergy between microservice-based architectures and AI pipelines, emphasizing scalability, latency reduction, and autonomous operation.
Findings: AI models embedded within containerized microservices offer modular and scalable real-time processing for diverse domains such as retail, healthcare, and financial services. Kubernetes and service meshes enable orchestration and monitoring of complex decision workflows.
Practical implications: Enterprises can accelerate AI deployment using CI/CD for ML, gain real-time insights, and adapt dynamically through cloud-native DevOps strategies.
Originality/value: This paper synthesizes findings from pre-2020 literature and presents a unified architecture for scalable real-time AI-powered decision systems using cloud-native practices.
References
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
