How to Deploy Machine Learning Models to a .NET Environment

Python and R are among the most popular programming languages for data-centric engineers. However, they are not always the languages that the rest of an application is built on. This is why you sometimes need to find a way to deploy machine-learning models written in Python or R into an environment based on a language such as .NET.
In this article, I show how to use Web APIs to integrate machine learning models into applications written in .NET.
Poodle, Pug or Weiner Dog? Deploying a Dog Identification TensorFlow Model Using Python and Flask
In my last post I surveyed the growing array of options for deploying your ML models into production. In this post, we’ll create a demo to see how simple it is to develop your own service using Python’s Flask library.
There are a number of cases where you might not be able to use a cloud service to host your model and would be required to roll-your-own inference service. In many large enterprises, on-premise solutions are mandatory. Approvals to purchase third-party solutions can also be lengthy and complex. So developing your own small service may be the best solution.