Extensibility of ML.NET

Lastly, ML.NET, like most robust frameworks, provides considerable extensibility. Microsoft has since launched added extensibility support to be able to run the following externally trained model types, among others:

• TensorFlow
• ONNX
• Infer.Net
• CNTK

TensorFlow (https://www.tensorflow.org/), as mentioned previously, is Google's machine learning framework with officially supported bindings for C++, Go, Java, and JavaScript. Additionally, TensorFlow can be accelerated with GPUs and, as previously mentioned, Google's own TPUs. In addition, like ML.NET, it offers the ability to run predictions on a wide variety of platforms, including iOS, Android, macOS, ARM, Linux, and Windows. Google provides several pre-trained models. One of the more popular models is the image classification model, which classifies objects in a submitted image. Recent improvements in ML.NET have enabled you to create your own image classifier based on that pre-trained model. We will be covering this scenario in detail in Chapter 12, Using TensorFlow with ML.NET.

ONNX (https://onnx.ai/), an acronym for Open Neural Network Exchange Format, is a widely used format in the data science field due to the ability to export to a common format. ONNX has converters for XGBoost, TensorFlow, scikit-learn, LibSVM, and CoreML, to name a few. Microsoft's native support of the ONNX format in ML.NET will not only allow better extensibility with existing machine learning pipelines but also increase the adoption of ML.NET in the machine learning world. We will utilize a pre-trained ONNX format model in Chapter 13, Using ONNX with ML.NET.

Infer.Net is another open source Microsoft machine learning framework that focuses on probabilistic programming. You might be wondering what probabilistic programming is. At a high level, probabilistic programming handles the grey area where traditional variable types are definite, such as Booleans or integers. Probabilistic programming uses random variables that have a range of values that the result could be, akin to an array. The difference between a regular array and the variables in probabilistic programming is that for every value, there is a probability that the specific value would occur.

A great real-world use of Infer.Net is the technology behind Microsoft's TrueSkill. TrueSkill is a rating system that powers the matchmaking in Halo and Gears of War, where players are matched based on a multitude of variables, play types, and also, maps can all be attributed to how even two players are. While outside the scope of this book, a great whitepaper diving further into Infer.Net and probabilistic programming, in general, can be found here: https://dotnet.github.io/infer/InferNet_Intro.pdf.

CNTK, also from Microsoft, which is short for Cognitive Toolkit, is a deep learning toolkit with a focus on neural networks. One of the unique features of CNTK is its use of describing neural networks via a directed graph. While outside the scope of this book (we will cover neural networks in Chapter 12 with TensorFlow), the world of feed-forward Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks is extremely fascinating. To dive further into neural networks specifically, I would suggest Hands-On Neural Network Programming with C#, also from Packt.

Additional extensibility into Azure and other model support such as PyTorch (https://pytorch.org/) is on the roadmap, but no timeline has been established at the time of writing.