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. Each example demonstrates how easy it is to include ML models into preexisting fullstack frameworks like as Angular, React, and ASP.NET Core. In terms of computational efficiency, scalability, and accuracy, the study compares and contrasts the performance of ML.NET models with those of established ML frameworks. We talk about the problems and possible solutions that come up during integration, including dealing with huge datasets, improving the performance of the models, and making sure they work on different platforms. To sum up, ML.NET`s integration with Fullstack Development paves the way for developers to build smart, scalable, user-centric apps. Utilizing ML.NET, developers can connect the dots between classic software engineering and cutting-edge AI, revolutionizing user interfaces and data processing for organizations.

Fullstack Development, Intelligent Applications, ML.NET, Predictive Analytics Machine Learning.

[1] S. Boovaraghavan, A. Maravi, P. Mallela, and Y. Agarwal, "MLIoT: An end-to-end machine learning system for the Internet-of-Things," in Proceedings of the International Conference on Internet-of-Things Design and Implementation, May 18, pp.169-181, 2021.
[2] A. Gurusamy and I. A. Mohamed, "The role of AI and machine learning in full stack development for healthcare applications," Journal of Knowledge Learning and Science Technology, Vol.1, No.1, pp.116-123, 2021.
[3] K. Xu, X. Wan, H. Wang, Z. Ren, X. Liao, D. Sun, C. Zeng, and K. Chen, "Tacc: A full-stack cloud computing infrastructure for machine learning tasks," arXiv preprint arXiv:2110.01556, 2021.
[4] S. Xi, Y. Yao, K. Bhardwaj, P. Whatmough, G. Y. Wei, and D. Brooks, "SMAUG: End-to-end full-stack simulation infrastructure for deep learning workloads," ACM Transactions on Architecture and Code Optimization (TACO), Vol.17, No.4, pp.1-26, 2020.
[5] S. Prakash, T. Callahan, J. Bushagour, C. Banbury, A. V. Green, P. Warden, and V. J. Reddi, "CFU Playground: Full-stack open-source framework for tiny machine learning (TinyML) acceleration on FPGAs," in 2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), Apr., pp.157-167, 2023.
[6] H. Genc, S. Kim, A. Amid, A. Haj-Ali, V. Iyer, P. Prakash, J. Zhao, et al., "Gemmini: Enabling systematic deep-learning architecture evaluation via full-stack integration," in 2021 58th ACM/IEEE Design Automation Conference (DAC), pp.769-774, 2021.
[7] A. Saiyeda and M. A. Mir, "Cloud computing for deep learning analytics: A survey of current trends and challenges," International Journal of Advanced Research in Computer Science, Vol.8, No.2, 2017.
[8] J. M. Dharmalingam and M. Vadlamaani, "A novel intelligent agent equipped with machine learning for route optimization in effective supply chain management for seasonal agricultural products," International Journal of Computer Information Systems and Industrial Management Applications, Vol.16, No.3, pp.18-18, 2024.
[9] G. Ruchitha, D. Jyoshna, G. S. K. Galla, I. S. M. Varma, C. M. Babu, and L. Pallavi, "BookGenius: Elevating reading exploration through full-stack mastery," in 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), Aug., Vol.1, pp.443-448, 2024.
[10] C. Jansen, J. Annuscheit, B. Schilling, K. Strohmenger, M. Witt, F. Bartusch, C. Herta, P. Hufnagl, and D. Krefting, "Curious Containers: A framework for computational reproducibility in life sciences with support for deep learning applications," Future Generation Computer Systems, Vol.112, pp.209-227, 2020.