Understand the project
Practical overview
When it fits
Use ML.NET when a .NET application needs an integrated workflow for data preparation, model training and prediction, or when existing TensorFlow and ONNX models need to be consumed from .NET.
Who it is for
- .NET developers building machine-learning features in C#.
- Teams working across Windows, Linux, macOS, cloud, server or desktop .NET environments.
- Developers who need to consume TensorFlow or ONNX models from a .NET application.
Documented uses
- Load training data from files and databases.
- Transform and prepare datasets for machine-learning workflows.
- Train models for classification, forecasting and anomaly detection.
- Deploy and consume custom models in .NET applications.
- Consume TensorFlow and ONNX models from .NET software.
Before you choose
Requirements & limits
The repository identifies ML.NET as open source under the MIT licence. Packages are distributed through NuGet-related tooling, including the .NET Core CLI and Visual Studio's NuGet Package Manager.
- The application must use the .NET ecosystem; ML.NET is implemented for .NET and its primary language is C#.
- Supported scenarios include classification, forecasting and anomaly detection.
- Data can be loaded from files and databases and transformed before training.
- TensorFlow and ONNX model consumption is supported.
- Windows, Linux and macOS are supported, with additional ARM64, Apple M1 and Blazor WebAssembly support subject to stated platform limitations.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- dotnet/machinelearning · Official homepage title · retained in its source language