Integrating Machine Learning with Fullstack Development Using ML.NET - Chettier, Thiyagarajan Mani; Boyina, Venkata Ashok Kumar (International Journal of Computer Sciences and Engineering, 2025)
Improved user experiences and data-driven solutions have resulted from the revolutionary combination of Machine Learning (ML) with Fullstack Development, which has changed the way intelligent apps are produced. Using Microsoft`s flexible ML.NET framework, this article delves into how to incorporate Machine Learning models into Fullstack Development without a hitch. Without deep knowledge of data science, ML.NET allows developers to build, train, and deploy ML models inside.NET apps. At the outset, the research delves into the difficulties encountered by conventional full-stack apps, including their lack of predictive power and static data processing. Fullstack designs that use ML.NET allow applications to automate decision-making, tailor content, and evaluate and forecast user actions in real-time. We showcase ML.NET`s interoperability with common programming languages like C# and F#, its support for various data types, and automated machine learning (AutoML) to show how it can be used in fullstack applications. Model building, training, and deployment are the three main areas covered in the methodology part as they pertain to developers utilizing ML.NET in a fullstack setting. To facilitate effective data processing and model inference, the focus is on connecting backend systems with ML.NET pipelines. Our research also delves into frontend integration strategies, showing how features driven by ML may improve user interfaces with capabilities like natural language processing, visual analytics, and real-time suggestions. To show how ML.NET may be used in fullstack development, many example studies are given. The development of e-commerce platform recommendation systems, company dashboard predictive analytics, and customer feedback management sentiment analysis tools are all examples of what is involved.