ML.NET Samples is a collection of example projects demonstrating how to use ML.NET, a cross-platform open-source machine learning framework for .NET developers.
The samples address the problem of helping .NET developers learn and integrate machine learning into their applications. The repository provides two types of examples: Getting Started samples that focus on specific ML tasks implemented as simple console applications, and End-to-End applications that show how to infuse machine learning models into real web and desktop applications. This dual approach lets developers learn ML.NET concepts in isolation or see how they fit into complete, production-like scenarios.
Developers should use this repository if they are building .NET applications and want to understand how to apply machine learning techniques within the .NET ecosystem. The samples are organized by scenario and machine learning problem type, making it straightforward to find examples relevant to your use case. This is particularly valuable for teams already invested in .NET who want to avoid context-switching to other language ecosystems when adding ML capabilities.
The repository serves as a companion to the ML.NET framework itself, with a clear separation of concerns: issues related to the framework are directed to the main ML.NET repository, while this repository focuses specifically on sample code quality and correctness. The samples cover multiple machine learning tasks and scenarios, providing breadth across different problem domains that developers might encounter.
