Adaptive AI Infrastructure: A Containerized Approach for Scalable Model Deployment

The exponential growth of artificial intelligence applications has necessitated significant advancements in AI infrastructure to manage the computational, storage, and energy demands of large-scale machine learning models.This paper introduces a unified framework for optimizing resource allocation across model training and deployment phases, leveraging dynamic resource scaling, energy-efficient hardware configurations, and containerization techniques.Experimental results demonstrate up to 45% reduction in infrastructure costs and 32% improvement in energy efficiency while maintaining model performance.Case studies in e-commerce and healthcare illustrate the framework's adaptability, providing a robust pathway for sustainable AI system deployment.