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AI Driven Microservice and Serverless Architectures for Real Time Big Data and Enterprise DevOps Platforms

Dr.Anusha Kannan

Abstract

AI-driven microservice and serverless architectures are reshaping real-time big data processing and enterprise DevOps platforms by enabling scalable, event-driven, and intelligent systems. By combining containerized microservices, serverless computing, and distributed streaming frameworks, organizations can process high-velocity data streams with low latency while maintaining elasticity and operational efficiency. AI and machine learning models embedded within these architectures enhance real-time analytics, predictive insights, anomaly detection, and automated decision-making across enterprise workloads. 

Cloud-native technologies such as Kubernetes, service meshes, and API gateways support resilient orchestration, fault isolation, and seamless service communication. Serverless platforms further optimize resource utilization through dynamic scaling and pay-per-use models, accelerating deployment cycles and reducing operational overhead. Integrated DevOps and MLOps pipelines enable continuous integration, automated testing, model deployment, and observability, ensuring governance, security, and compliance at scale. Together, AI-driven microservices and serverless paradigms establish intelligent, adaptive enterprise platforms capable of supporting real-time big data ecosystems and accelerating digital transformation

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2026.0901002

Pages

10-19

Published

January 10, 2026
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dr.Anusha Kannan (%2026). AI Driven Microservice and Serverless Architectures for Real Time Big Data and Enterprise DevOps Platforms. International Journal of Science, Research and Technology , Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 10-19. https://doi.org/10.15662/IJSRAT.2026.0901002

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