Journal of Emerging Technology and Digital
Transformation
Online ISSN
3006-9726
Volume . 4 Issue . 2 (2025)
Print ISSN
3006-9718
Cloud-Native Architectures for Large-Scale AI-Based Predictive
Modeling
Muhammad Talha Tahir Bajwa
University of Agriculture Faisalabad Department of Computer Science
talhabajwa6p@gmail.com
Saman Wattoo
University of Agriculture Faisalabad, Department of Computer Science
Corresponding Author samanwattoo251@gmail.com
Irum Mehmood
University of Okara, Department: Computer Science
irummehmood75@gmail.com
Muhammad Talha
Bahria University E-8 , Islamabad Campus Department of Computer Science
Muhammadtalha7893@yahoo.com
Muhammad Junaid Anwar
University of Agriculture Faisalabad, Department of Computer Science
junaidanwar365@gmail.com
Muhammad Sana Ullah
University of Agriculture Faisalabad Department of Computer Science
msanaullah133@gmail.com
RECEIVED
02 July 2025
ACCEPTED
15 July 2025
ABSTRACTAbstractAbstct
PUBLISHED
22 Aug 2025
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.
Corresponding Author*Saman Wattoo