CLOUD-NATIVE ARCHITECTURES FOR LARGE-SCALE AI-BASED PREDICTIVE MODELING

Abstract

The demand of adapted, expandable, efficient deployment techniques has become more acknowledged because of the accelerated growth of artificial intelligence (AI) initiatives and high intricacity of big forms of predictive modeling. Cloud-native architectures which are founded on concepts such as serverless computing, microservices, orchestration and containerization create a solid foundation in satisfying these needs. Dividing its emphasis between distributed model training, real-time inference, and automated lifecycle management, this paper explores how cloud-native technology acts to enable large-scale AI-based predictive modeling. By integrating MLOps practices with elastic cloud infrastructure, organizations will be able to realize better fault tolerance, faster deployment schedules, and the most efficient use of resources. The proposed methodology demonstrates that cloud-native ideas can help AI systems work with a vast amount of data, dynamically adapt to changing loads, and maintain high performance levels in the actual environment.

Keywords: Cloud-native architecture, predictive modeling, containerization, MLOps, microservices, real-time inference, serverless computing.