ML.NET is a cross-platform open-source machine learning framework for .NET that enables developers to build, train, deploy, and consume machine learning models within .NET applications.
The framework addresses the challenge of integrating machine learning into .NET workflows without requiring expertise in Python, R, or specialized ML development. It provides built-in data loading from files and databases, data transformation capabilities, and a range of ML algorithms. The tool supports multiple scenarios including classification, forecasting, and anomaly detection. A key capability is the ability to consume both TensorFlow and ONNX models, allowing developers to integrate pre-trained models from other ecosystems into their .NET applications.
Developers working within the .NET ecosystem who want to add machine learning capabilities without switching languages should consider this framework. It suits projects where ML functionality needs to be tightly integrated into existing .NET applications. The tool runs on Windows, Linux, and macOS, with support for both .NET Core and .NET Framework, and extends to ARM64, Apple M1, and Blazor WebAssembly, though some platform limitations exist. It works with .NET Core 2.1 or later, or .NET Framework 4.6.1 and above.
The project maintains an active roadmap documenting planned work for the coming year. Release notes and blog posts accompany each update, providing transparency about new features and changes. The framework is distributed through NuGet packages with daily builds available through an Azure DevOps feed, enabling developers to access the latest development versions. A dedicated samples repository and community samples collection support adoption and learning.
