Optimizing Data Infrastructure and Analytics Engineering for Real-Time Predictive Models in Dynamic Environments Using Deep Learning Architectures and Cloud-Native Solutions

Abstract

The rapid evolution of big data and machine learning has necessitated the optimization of data infrastructure and analytics engineering to support real-time predictive models in dynamic environments. This paper explores the integration of deep learning architectures and cloud-native solutions to enhance data processing, model accuracy, and decision-making speed. We analyze the latest advancements in scalable data pipelines, distributed computing, and real-time analytics to provide a comprehensive framework for modern predictive modeling. Our study highlights key strategies, such as data lakehouse adoption, serverless computing, and edge AI, to optimize data flow and reduce latency in predictive analytics. We also examine the challenges of real-time data processing, model retraining, and security in cloud-native environments. The findings suggest that leveraging deep learning in conjunction with advanced cloud infrastructure significantly improves efficiency and adaptability in dynamic business and industrial applications.