ML.NET is an open source machine learning framework, created by Microsoft, for the .NET developer platform. ML.NET is cross platform and runs on macOS, Linux and Windows. In short, ML.NET is a machine learning framework built for .NET developers. Use your .NET and C# or F# skills to easily integrate custom machine learning into your applications without any prior expertise in developing or tuning machine learning models.
So what it can be used for,
- ML.NET is used to develop and integrate custom machine learning models into .NET apps of any type – web, mobile, desktop, gaming, and IoT.
- It contains machine learning libraries created by Microsoft Research and used by Microsoft products.
- Over time, you will also be able to leverage other popular libraries like Accord.NET, CNTK and TensorFlow through the extensible platform.
- This is a open source and backed by the .NET Foundation. ML.NET is currently in preview. You can find the ML.NET project on GitHub.
- It combines data loading, transformations, and model training into a single pipeline. The transformations defined in your pipeline are applied to both your training data and your input data for making predictions with your trained model.
- Using ML.Net now you can add machine learning to your existing .NET apps. ML.NET supports popular machine learning scenarios like sentiment analysis, forecasting, recommendation, and more. ML.NET also supports deep learning scenarios like image classification with TensorFlow.
- Probabilistic programming is supported through Infer.NET, which extends ML.NET with capabilities for bayesian analysis and online learning.
- Now you can use ML.NET to save your trained model as a binary file that you can integrate into any .NET application.
For more info, you can refer get started with https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet