Artificial Intelligence and Machine Learning with .NET

.NET is not the first framework or platform that comes to mind when you think of developing and training an Artificial Intelligence model. It has become one of the most extensively used frameworks/platforms in the world. Despite this popularity, its use in AI and ML has yet to gain the same level of traction that languages (and communities) like Python have. However, it can be a powerful platform for building powerful AI and ML applications, especially for businesses that already use .NET for many of their development needs. As a framework, it supports multiple languages, but arguably, the most extensively used language under the .NET umbrella is C#.

The Language and Platform Agnostic Nature of AI

Before we explore the possibilities of building AI-powered applications with .NET, it’s important to understand the relationship of AI and ML with computer languages and development platforms. As technology, AI, and ML are not tied to any specific language, framework, development platform, or even environment. Theoretically, you can develop an AI model even on a low-level language like Assembly, but it can be incredibly difficult to do so.

One of the reasons why some languages, like Python, are preferred for AI and ML applications and models while others are not is that community and libraries are available.

A large ecosystem of libraries and a strong community where similar projects are being built and shared around can make a language very desirable for something as complex and cutting-edge as AI. Interestingly, .NET also boasts a strong community, which can be very helpful in developing and managing AI and ML projects.

Developing AI-powered applications with .NET

Developing AI-powered applications is not just possible but very practical. A .NET development company might build a sophisticated and comprehensive AI-powered application without ever leaving the environment. One of the most significant assets in this regard is ML.NET. It’s a .NET-based ML model builder that makes the process of building custom ML models quite easy. It’s available in the Visual Studio environment and can help you integrate the models you build in any .NET application. So you can create and deploy powerful AI and ML models in this environment and integrate them into your .NET applications to turn them into AI-powered/AI-augmented applications.

A Growing Ecosystem and Community

One of the benefits of developing AI-powered applications with .NET is that you get access to many of the top AI models. This includes OpenAI models since Microsoft is heavily invested in the company. However, there are also open-source models like Mistral, Meta, and Cohere. You can also leverage libraries and the Software Development Kit (SDKs) in .NET, which is developed exclusively for AI applications. It integrates with other open-source communities, like Huggingface, where you can get models. The models can be hosted on Azure or other clouds. The .NET community is already quite strong (thanks partly to a massive C# developer community), and with the right catalysts and enough AI projects, it may become as inviting for companies and new developers as Python in the coming years.

Support

Microsoft develops and maintains the .NET platform, even though it’s open-source. The company also offers ample support for AI projects. This includes guides and demos for building specific AI-powered applications like chatbots and data analysis systems, as well as beginner videos for developers who have just started exploring AI.

Another benefit of using .NET for AI development is that you can get started easily. Whether you hire .NET developers or start building in-house capabilities, access to powerful tools like Azure AI Model catalogs can give you a strong footing for creating business/use-case-specific AI applications.

Natural Language Processing (NLP) in .NET

Generative AI is one of the most common and well-understood AI applications, and it is at the back end of many generative AI tools and applications in Natural Language Processing (NLP). An NLP is essentially any model/tool/system that leverages AI to understand human language, i.e., natural language. So whether you want something like ChatGPT that can reply to text with text or Midjourney that creates images based on text prompts, it relies upon NLP.

Like other AI models, NLPs are not tied to specific languages and development platforms like .NET and instead rely upon frameworks, algorithms, and libraries that might be available for multiple languages. One example would be Tensorflow, which is a very commonly used deep learning framework and is available for .NET via Tensorflow.NET. It can be used to induce NLP functionalities/capabilities in .NET-based AI-powered applications.

NLP functionality can be added to a .NET project regardless of the language you are using. C#. F#. and Visual Basic can access all the relevant libraries available for NLP in the .NET environment with the relevant wrappers. This includes spaCy, which is one of the most powerful NLP libraries. It was originally written for Python but has a .NET wrapper called spaCy.NET. You can also use HuggingFace Transformers.

Relevant .NET AI and ML Tools

If you hire .NET developers for your AI/ML project, they would most likely already be familiar with many of the libraries and frameworks useful in .NET environments and beneficial for AI and ML projects. This includes ML.NET, which we have already discussed, and libraries with wrappers for .NET. Others include Open Neural Network Exchange or ONNX runtime. This serves as a powerful intermediary when you are shifting from different ML frameworks. It’s inherently cross-platform, and the environment is compatible with many systems and languages, including .NET and C#.

There are also Azure AI services. These include various AI models hosted on Azure servers, many of which are proprietary to Azure. Examples include AI Speech, AI Vision, AI Document Intelligence, etc. Using these tools (wherever applicable) can streamline the process of developing AI and ML-powered applications on .NET.

Final Words

One of the things you should keep in mind is that .NET, regardless of its strengths and limitations, is merely a tool. And while it can have a significant impact on how effectively you execute your AI project and the maintainability of your AI application (and several other aspects), it can only do so much for your AI application’s success. A well-designed AI application has several layers, starting with understanding business/client needs. They may supersede and, to an extent, influence your choice of the language or environment you are getting the AI application built in.