[7] Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions.

The framework is built upon .NET Core and .NET Standard inheriting the ability to run cross-platform on Linux, Windows and macOS.

ML.NET was derived from the TLC library and has largely surpassed its parent says Dr. James McCaffrey, Microsoft Research.

Support for the open-source Open Neural Network Exchange (ONNX) Deep Learning model format was introduced from build 0.3 in ML.NET.

The release included other notable enhancements such as Factorization Machines, LightGBM, Ensembles, LightLDA transform and OVA.

It included the addition of a Model Builder tool and AutoML (Automated Machine Learning) capabilities.

The 1.4.0 preview added ML.NET scoring on ARM processors and Deep Neural Network training with GPU's for Windows and Linux.

[16] Microsoft's paper on machine learning with ML.NET demonstrated it is capable of training sentiment analysis models using large datasets while achieving high accuracy.