Scalable Distributed Learning Architectures for Cloud-Native Artificial Intelligence Systems
Keywords:
Distributed Learning, Cloud-Native AI, Scalability, Kubernetes, Federated Learning, Deep Learning, Microservices, Orchestration, Model ParallelismAbstract
The rapid expansion of artificial intelligence (AI) applications has necessitated the evolution of distributed learning systems that are both scalable and cloud-native. With increasing demands on computational resources and model complexity, traditional centralized AI systems are insufficient. This paper explores the architecture of scalable, distributed AI frameworks that are optimized for cloud-native environments. We analyze current methodologies, propose architectural strategies for scalability and resilience, and highlight challenges associated with data distribution, orchestration, and model convergence in multi-node environments. Through comparative evaluations, we identify best practices in containerized AI workloads, distributed training paradigms, and efficient resource scheduling.
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