Hands-On Industrial Internet of Things

Understanding the ML capabilities of the Azure ML service

As we learned in Chapter 13, Understanding Diagnostics, Maintenance, and Predictive Analytics, to develop an analytics model, we can follow five main steps:

1. Defining a problem statement and preparing the data
2. Exploratory Data Analysis (EDA)
3. Building the model—either physics-based or data-driven

4. Packaging and deployment
5. Monitoring

In the previous example, we covered all of these steps in our creation of a physics-based digital twins model. When we work with a data-driven approach, however, we need an additional sub-step to train and test the model using real data. We can use the machine learning abilities and high computational abilities (both GPU-and CPU-based) provided by Azure ...