Summary
In this chapter, we learned about the full machine learning-based .NET application workflow. We equipped ourselves with the requisite knowledge of machine learning terminologies that are commonly used in machine learning documentation all over the web.
We then learned how the ML.NET API and ML.NET Model Builder can help us quickly build applications by taking advantage of comprehensive algorithms, as well as using a model building library provided by ML.NET that supports the entire ML workflow.
After that, we built couple of sample .NET applications using the regression and binary classification algorithms. Then, we learned how in a real-world application, these machine learning services could be hosted as Azure Functions to be consumed by larger .NET applications. These skills will not only help you build smart applications that use ML services, but also integrate with data models that have been developed by other data scientists within and outside your organization...
