Introduction
You work for a manufacturing company that uses industrial devices and equipment as part of its operations. When one of these devices breaks, it costs your company time and money. That's why performing maintenance on these devices is important.
Scenario: Predictive maintenance
There are many different factors, like usage, that affect the need for maintenance. No one device is the same. Being proactive with maintenance can help minimize the time and money that your company spends when a device breaks. Up to this point, you've been manually keeping track of which devices require maintenance. As your company expands, this process becomes more difficult to manage.
What if you could automate predicting when a device is going to need maintenance by using sensor data?
Machine learning can help you analyze historical data from these sensors. Machine learning can also involve learning patterns to help you predict whether a machine needs maintenance or not.
You want to take advantage of your .NET skills and use familiar tools like Visual Studio to build a solution, but you don't have much experience with machine learning. As a result, you've decided to use ML.NET, an open-source machine learning framework for .NET. You'll also use the framework's Visual Studio extension, Model Builder, to build your machine learning model.
What will you learn?
In this module, you'll learn what Model Builder is, how to use it to train machine learning models, and how to consume those models inside .NET applications.
What is the main goal?
The goal of this module is to show you the process of using machine learning models to solve real-world business problems.