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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 avariety 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.

• Learn more about thebasics of ML.NET.
• Build your first ML.NET model by following ourML.NET Getting Started tutorial.
• Check out ourdocumentation and tutorials.
• See theAPI Reference documentation.
• Clone ourML.NET Samples GitHub repo and run some sample apps.
• Take a look at someML.NET Community Samples.
• Watch some videos on theML.NET videos YouTube playlist.

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

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 somelimitations.

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

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

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 viaPaket.

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

To build ML.NET from source please visit ourdeveloper guide.

	Debug	Release
CentOS
Ubuntu
macOS
Windows x64
Windows FullFramework
Windows x86
Windows NetCore3.1

Major releases of ML.NET are shipped once a year with the major .NET releases, starting with ML.NET 1.7 in November 2021 with .NET 6, then ML.NET 2.0 with .NET 7, etc. We will maintain release branches to optionally service ML.NET with bug fixes and/or minor features on the same cadence as .NET servicing.

Check out theRelease Notes to see all of the past ML.NET releases.

We welcome contributions! Please review ourcontribution guide.

• Join our community onDiscord.
• Tune into the.NET Machine Learning Community Standup every other Wednesday at 10AM Pacific Time.

This project has adopted the code of conduct defined by theContributor Covenant to clarify expected behavior in our community.For more information, see the.NET Foundation Code of Conduct.

Here is a code snippet for training a model to predict sentiment from text samples. You can find complete samples in thesamples repo.

vardataPath="sentiment.csv";varmlContext=newMLContext();varloader=mlContext.Data.CreateTextLoader(new[]{newTextLoader.Column("SentimentText",DataKind.String,1),newTextLoader.Column("Label",DataKind.Boolean,0),},hasHeader:true,separatorChar:',');vardata=loader.Load(dataPath);varlearningPipeline=mlContext.Transforms.Text.FeaturizeText("Features","SentimentText").Append(mlContext.BinaryClassification.Trainers.FastTree());varmodel=learningPipeline.Fit(data);

Now from the model we can make inferences (predictions):

varpredictionEngine=mlContext.Model.CreatePredictionEngine<SentimentData,SentimentPrediction>(model);varprediction=predictionEngine.Predict(newSentimentData{SentimentText="Today is a great day!"});Console.WriteLine("prediction: "+prediction.Prediction);

ML.NET is licensed under theMIT license, and it is free to use commercially.

ML.NET is a part of the.NET Foundation.