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Abstract
Efficiently managing the AI model lifecycle in cloud-native ecosystems has become increasingly complex, especially in multi-zone cloud platforms. To maintain performance and reliability across geographies, intelligent orchestration strategies are necessary to automate deployment, predict resource needs, and ensure service continuity. This paper presents a scalable framework that utilizes AI-driven resource prediction and continuous deployment pipelines to manage the end-to-end model lifecycle, from training to retirement. The framework emphasizes cross-zone synchronization, proactive scaling, and minimal human intervention. Through comparative studies and architectural analysis, the proposed approach demonstrates improved latency, cost-efficiency, and fault tolerance.
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