What is ML.NET?

Now that you have a fairly firm understanding of the core machine learning concepts, we can now dive into Microsoft's ML.NET framework. ML.NET is Microsoft's premier machine learning framework. It provides an easy-to-use framework to train, create, and run models with relative ease all in the confines of the .NET ecosystem.

Microsoft's ML.NET was announced and released (version 0.1) in May 2018 at Microsoft's developer conference BUILD in Seattle, Washington. The project itself is open source with an MIT License on GitHub (https://github.com/dotnet/machinelearning) and has seen a total of 17 updates since the first release at the time of writing.

Some products using ML.NET internally at Microsoft include Chart Decisions in Excel, Slide Designs in PowerPoint, Windows Hello, and Azure Machine Learning. This emphasizes the production-readiness of ML.NET for your own production deployments.

ML.NET, from the outset, was designed and built to facilitate the use of machine learning for C# and F# developers using an architecture that would come naturally to someone familiar with .NET Framework. Until ML.NET arrived, there was not a full-fledged and supported framework where you could not only train but also run a model without leaving the .NET ecosystem. Google's TensorFlow, for instance, has an open-source wrapper written by Miguel de Icaza available on GitHub (https://github.com/migueldeicaza/TensorFlowSharp); however, at the time of writing this book, most workflows require the use of Python to train a model, which can then be consumed by a C# wrapper to run a prediction.

In addition, Microsoft was intent on supporting all of the major platforms .NET developers have grown accustomed to publishing their applications in the last several years. Here are some examples of a few of the platforms, with the frameworks they targeted in parentheses:

• Web (ASP.NET)
• Mobile (Xamarin)
• Desktop (UWP, WPF, and WinForms)
• Gaming (MonoGame and SharpDX)
• IoT (.NET Core and UWP)

Later in this book, we will implement several real-world applications on most of these platforms to demonstrate how to integrate ML.NET into various application types and platforms.