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.
- Learn more about the basics of ML.NET.
- Build your first ML.NET model by following our ML.NET Getting Started tutorial.
- Check out our documentation and tutorials.
- See the API Reference documentation.
- Clone our ML.NET Samples GitHub repo and run some sample apps.
- Take a look at some ML.NET Community Samples.
- Watch some videos on the ML.NET videos YouTube playlist.
Take a look at ML.NET's Roadmap to see what the team plans to work on in the next year.
ML.NET runs on Windows, Linux, and macOS using .NET, or Windows using .NET Framework.
ML.NET also runs on ARM64, Apple silicon, and Blazor WebAssembly. 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.
Check out the release notes to see what's new. You can also read the blog posts for more details about each release.
The main Microsoft.ML package targets .NET Standard 2.0, so it can be used by compatible .NET implementations. Some optional ML.NET packages target newer frameworks; check the target frameworks listed on the package before adding it to your application.
Once you have an app, you can install the ML.NET NuGet package using the .NET CLI:
dotnet add 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
To build ML.NET from source please visit our developer guide.
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 the Release Notes to see all of the past ML.NET releases.
We welcome contributions! Please review our contribution guide.
This project has adopted the code of conduct defined by the Contributor 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 the samples repo.
var dataPath = "sentiment.csv";
var mlContext = new MLContext();
var loader = mlContext.Data.CreateTextLoader(new[]
{
new TextLoader.Column("SentimentText", DataKind.String, 1),
new TextLoader.Column("Label", DataKind.Boolean, 0),
},
hasHeader: true,
separatorChar: ',');
var data = loader.Load(dataPath);
var learningPipeline = mlContext.Transforms.Text.FeaturizeText("Features", "SentimentText")
.Append(mlContext.BinaryClassification.Trainers.FastTree());
var model = learningPipeline.Fit(data);Now from the model we can make inferences (predictions):
var predictionEngine = mlContext.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(model);
var prediction = predictionEngine.Predict(new SentimentData
{
SentimentText = "Today is a great day!"
});
Console.WriteLine("prediction: " + prediction.Prediction);ML.NET is licensed under the MIT license, and it is free to use commercially.
ML.NET is a part of the .NET Foundation.