SCALABLE AI-DRIVEN MICROSERVICES ARCHITECTURES FOR DISTRIBUTED CLOUD ENVIRONMENTS
Keywords:
AI Microservices, Scalable Architecture, Cloud Deployment, Data Pipeline OptimizationAbstract
This article presents a comprehensive approach to designing scalable AI-driven microservices architectures for distributed cloud environments. It explores key challenges in integrating AI into distributed systems and proposes strategies for microservices design, deployment, and scaling of AI workloads. The article covers data pipeline optimization, security, and compliance considerations and presents a detailed case study of a scalable image recognition service. Through analysis of scalability, efficiency, and robustness, the proposed architecture demonstrates significant improvements over traditional monolithic approaches, including near-linear scalability, 30% improved resource utilization, and 99.99% uptime during simulated failures. The article provides practical insights and quantitative results to guide organizations in building more efficient, scalable, and robust AI-driven applications in cloud environments.
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