Leveraging Cloud Computing for Real-Time AI and ML Model Adaptation

Abstract

The integration of Cloud Computing with Real-Time Artificial Intelligence (AI) and Machine Learning (ML) is rapidly transforming various industries by enabling scalable, adaptive, and efficient model deployment. This paper explores the leveraging of cloud computing to enhance the adaptability of AI and ML models in real-time environments. We propose a novel framework that utilizes cloud infrastructure to dynamically update and deploy models based on real-time data streams. The framework incorporates adaptive algorithms that optimize model performance and resource utilization, addressing challenges such as latency, scalability, and model drift. By integrating real-time data processing with cloud-based model management, our approach ensures continuous model adaptation and improved decision-making capabilities. The efficacy of the proposed framework is demonstrated through a series of experiments in diverse domains, including financial forecasting, healthcare diagnostics, and IoT systems. The results indicate that leveraging cloud computing for real-time AI and ML model adaptation significantly enhances performance metrics such as prediction accuracy, response time, and operational efficiency. This study provides a comprehensive analysis of the benefits and limitations of the framework, offering valuable insights for practitioners and researchers seeking to implement adaptive AI solutions in dynamic environments.