Vol. 5 No. 1 (2025): January−June 2025
Short Articles

Deploying AI Models in .NET Web Applications Using Azure Kubernetes Service (AKS)

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  • Aditya S Shethiya
Aditya S Shethiya
University of Bridgeport, Connecticut, USA

Published 2025-03-14

Keywords

  • AI model deployment,
  • .NET web applications,
  • Azure Kubernetes Service (AKS),
  • scalable AI inference,
  • cloud-based machine learning

How to Cite

Shethiya, A. S. (2025). Deploying AI Models in .NET Web Applications Using Azure Kubernetes Service (AKS). Spectrum of Research, 5(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/13

Copyright (c) 2025 Spectrum of Research

Creative Commons License

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

Abstract

The integration of artificial intelligence (AI) into .NET web applications is revolutionizing industries by enabling intelligent automation, predictive analytics, and real-time decision-making. However, deploying AI models efficiently in production environments presents challenges related to scalability, management, and performance optimization. Azure Kubernetes Service (AKS) provides a robust solution by offering container orchestration, automated scaling, and seamless integration with .NET applications. This paper explores the deployment of AI models in .NET web applications using AKS, highlighting the benefits of containerization, model serving, and continuous integration/continuous deployment (CI/CD) pipelines. It also discusses key considerations such as optimizing AI inference performance, managing resources effectively, and ensuring high availability. By leveraging AKS, developers can deploy AI-powered .NET applications at scale, improving efficiency, reliability, and maintainability in cloud-based environments.

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