MACHINE LEARNING AND AI WITH .NET CORE: A COMPREHENSIVE GUIDE
Keywords:
ASP.NET Core Integration, Machine Learning Implementation, Enterprise AI Solutions, Performance Optimization, ML.NET ArchitectureAbstract
This comprehensive article explores the integration of Machine Learning and Artificial Intelligence capabilities within the ASP.NET Core framework, examining its transformative impact across various industries. The article investigates performance metrics, implementation strategies, and best practices for deploying ML-powered applications. It details how organizations across healthcare, finance, retail, and transportation sectors have leveraged these technologies to achieve significant operational improvements. The article covers the framework's foundational architecture, ML.NET implementation patterns, and crucial considerations for security and scalability. Through detailed examination of real-world implementations, this article demonstrates how ASP.NET Core's integration with ML and AI has revolutionized application development while establishing new benchmarks for performance and efficiency in enterprise-scale deployments.
References
Manish Patel, "Why Enterprise Use Asp.Net Core For Cutting-Edge Software Development?," Concetto Labs, February 13, 2023. Available: https://www.concettolabs.com/blog/asp-net-core-for-cutting-edge-software-development/
Yuvraj Raulji, "Integrating AI and ML into .NET Applications: A Comprehensive Guide," Prakash Infotech, February 12, 2024. Available: https://prakashinfotech.com/integrating-ai-and-ml-into-net-applications
Addweb Solutions Pvt. Ltd, "Integrating AI & ML with .NET Applications: A Complete Guide," LinkedIn, May 29, 2024. Available: https://www.linkedin.com/pulse/integrating-ai-ml-net-applications-complete-guide-addwebsolution-zag9f
Sven Peldszus et al., "Towards ML-Integration and Training Patterns for AI-Enabled Systems," Bridging the Gap Between AI and Reality, 31 October 2024. Available: https://link.springer.com/chapter/10.1007/978-3-031-73741-1_26
Axel Dalbard and Jesper Isacson, "Comparative study on performance between ASP.NET and Node.js Express for web-based calculation tools," JÖNKÖPING, 2021/06. Available: https://www.diva-portal.org/smash/get/diva2:1572101/FULLTEXT01.pdf
GeekforGeeks, "Top 10 Machine Learning Frameworks in 2025," 08 Nov, 2024. Available: https://www.geeksforgeeks.org/machine-learning-frameworks/
Aspire Software Consultancy, "AI and Machine Learning Integration in .NET Applications," October 11, 2023. Available: https://aspiresoftwareconsultancy.com/ai-machine-learning-integration-net-applications/
Audacia, "How to use ML.NET to build machine learning models in .NET applications," Medium, May 1, 2023. Available: https://medium.com/codex/how-to-use-ml-net-to-build-machine-learning-models-in-net-applications-6632c6b1663
Praxie, "Optimizing Performance: AI Applications in Operations," LinkedIn, November 26, 2024. Available: https://www.linkedin.com/pulse/optimizing-performance-ai-applications-operations-get-praxie-byjrc
Harrison Clarke, "Mastering MLOps: Best Practices for Secure Machine Learning Systems," February 9, 2024. Available: https://www.harrisonclarke.com/blog/mastering-mlops-best-practices-for-secure-machine-learning-systems
