Earlier this month Microsoft announced ML.NET 1.2, along with updates on its Model Builder and CLI.
ML.NET is an open-source, cross-platform machine learning (ML) framework for the .NET ecosystem. Its main purpose is to allow the development of custom ML models using either C# or F#. It includes a UI tool for Visual Studio (called Model Builder, available only for Windows) and a Command-line interface (CLI).
The new version of ML.NET includes a TimeSeries package for forecasting and anomaly detection scenarios. It also includes the general availability of TensorFlow and ONNX model integration features, which allow the construction of machine learning and deep learning models involving image classification and object detection.
A new integration package called Microsoft.Extensions.ML was also released as a preview feature. The purpose of this package is to make it easier to integrate ML.NET models with ASP.NET apps, Azure Functions, and web services. An example of how to use it to integrate an ML.NET model with ASP.NET Core can be found here.
Another new feature is the addition of tree-based featurization. This is a popular data mining technique used in scenarios such as predicting clicks on online advertisements, for example.
Other updates include bug fixes in the ML.NET CLI and smaller additions to the Model Builder, such as support for .txt files (used for model training) and removal of size limit for training data (originally capped at 1GB). The release notes for ML.NET 1.2 can be found here.
ML.NET is available for Windows, Linux, and macOS. The Windows release requires Visual Studio 2017 15.9.12 or later since the model builder is available as a Visual Studio extension. There are no prerequisites for macOS and Linux, but in these platforms, the ML.NET models are built using a CLI. You can learn more about ML.NET here.