ML.NET-Machinelearning

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dotnet/machinelearning: ML.NET is an open source and cross-platform machine learning framework for .NET. (github.com)

Machine Learning for .NET

ML.NET is a cross-platform open-source machine learning (ML) framework for .NET.

ML.NET allows developers to easily build, train, deploy, and consume custom models in their .NET applications without requiring prior expertise in developing machine learning models or experience with other programming languages like Python or R. The framework provides data loading from files and databases, enables data transformations, and includes many ML algorithms.

With ML.NET, you can train models for a variety of scenarios, like classification, forecasting, and anomaly detection.

You can also consume both TensorFlow and ONNX models within ML.NET which makes the framework more extensible and expands the number of supported scenarios.

Getting started with machine learning and ML.NET

Roadmap

Take a look at ML.NET's Roadmap to see what the team plans to work on in the next year.

Operating systems and processor architectures supported by ML.NET

ML.NET runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework.

ML.NET also runs on ARM64, Apple M1, and Blazor Web Assembly. However, there are some limitations.

64-bit is supported on all platforms. 32-bit is supported on Windows, except for TensorFlow and LightGBM related functionality.

ML.NET NuGet packages status

NuGet Status

Release notes

Check out the release notes to see what's new. You can also read the blog posts for more details about each release.

Using ML.NET packages

First, ensure you have installed .NET Core 2.1 or later. ML.NET also works on the .NET Framework 4.6.1 or later, but 4.7.2 or later is recommended.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:

dotnet add package Microsoft.ML

or from the NuGet Package Manager:

Install-Package Microsoft.ML

Alternatively, you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Daily NuGet builds of the project are also available in our Azure DevOps feed:

https://pkgs.dev.azure.com/dnceng/public/_packaging/dotnet-libraries/nuget/v3/index.json