DESIGN AND IMPLEMENTATION OF AI-DRIVEN MICROSERVICES ARCHITECTURE FOR SCALABLE MACHINE LEARNING WORKLOADS
Keywords:
Microservices, Machine Learning, AI-driven Architecture, Scalability, Cloud-native Systems, Containerization, OrchestrationSynopsis
This paper explores the design and implementation of a microservices-based architecture tailored for scalable machine learning (ML) workloads enhanced by artificial intelligence (AI). It discusses how microservices facilitate flexible deployment, scalability, and real-time model updates while allowing AI components to dynamically manage workload distribution. Two diagrams are included to illustrate the architecture and AI-driven orchestration, alongside two tables that compare legacy vs. microservices-based ML systems and performance metrics. This architecture is especially suited for cloud-native, continuous-integration environments, where AI drives decisions about resource scaling and service orchestration.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
