Leveraging AI in .NET 8: Implementing Machine Learning Models with ML .NET

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@article{1707657,
      author = {Sohan Singh Chinthalapudi},
      title = {Leveraging AI in .NET 8: Implementing Machine Learning Models with ML .NET},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {6},
      pages = {1052-1063},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707657.pdf},
      abstract = {The quick advancement of artificial intelligence (AI) technology transformed software development into a tool that enables predictive analytics with intelligent automation capabilities throughout various commercial sectors. An evaluation of AI integration in .NET 8 utilizes ML.NET as the targeted machine learning framework, which aims to serve .NET developers. ML.NET introduces a straightforward pipeline infrastructure which permits developers to create predictive models by handling all three detection categories (classification, regression, anomaly detection) without extensive data science knowledge requirements. The research delivers detailed information about ML.NET functionalities, describes its training workflow and key features, and explains its Integration with .NET 8 applications. The research implements a practical analytics model as an illustration to show structured data processing with ML.NET while demonstrating its ability to generate precise predictions. A detailed performance assessment of the models employs standard metrics from the industry while discussing the optimization methods needed to achieve better accuracy levels. The examination of ML.NET as a machine learning framework emphasizes its characteristics relative to other options, showcasing its strengths and weaknesses when used in deep learning environments. This paper investigates the deployment strategies for artificial intelligence, including edge computing and cloud-based implementations for scalable artificial intelligence deployment abilities. Empirical tests reveal deployment hurdles AI models face in .NET environments, which help determine potential upgrades for ML.NET's functionality. The study demonstrates how ML.NET can improve .NET system accessibility by making machine learning accessible to developers through its potential. The research enhances AI applications in enterprise environments by establishing knowledge about combining machine learning models with modern .NET architecture systems.},
      keywords = {ML.NET, .NET 8, Machine Learning, Predictive Analytics, AI Integration, Model Deployment, AI in Software Development},
      month = {December},
  }