Cross-platform AI Development - A Comparative Analysis of .Net and Other Frameworks

Abstract

The landscape of artificial intelligence (AI) development has witnessed a paradigm shift with the increasing demand for cross-platform solutions. This paper presents a comprehensive comparative analysis of AI development frameworks with a specific focus on .NET and other prominent frameworks. The goal is to provide developers, researchers, and decision-makers with insights into the strengths and weaknesses of .NET in comparison to alternative frameworks. The paper begins by outlining the significance of cross-platform AI development in today's diverse computing environment. It explores the challenges and opportunities associated with creating AI applications that seamlessly run across multiple platforms, including desktop, web, and mobile. The discussion encompasses the need for adaptability, scalability, and performance in AI models deployed on various operating systems. Subsequently, the paper delves into an in-depth examination of the .NET framework for AI development. The paper concludes by summarizing the key findings and offering recommendations for developers and organizations based on their unique requirements. It also discusses emerging trends and future considerations in cross-platform AI development, providing valuable insights for stakeholders navigating the evolving landscape of artificial intelligence.