Understanding how AutoML works on Azure

Before running your first AutoML experiment, it's important to understand how AutoML works on Azure. AutoML is more than just machine learning, after all. It's also about data transformation and manipulation.

As shown in the following diagram, you can divide the stages of AutoML into roughly five parts: Data Guardrails Check, Intelligent Feature Engineering, Iterative Data Transformation, Iterative ML Model Building, and Model Ensembling. Only at the end of this process does AutoML produce a definitive best model:

Figure 2.17 – The Azure AutoML process

Let's take a closer look at each step in this process.

Ensuring data quality with data guardrails

Data guardrails check to make sure that your data is in the correct format for AutoML, and if it is not, it will alter the data accordingly. There are currently six main checks that are performed on your data. Two of the checks – one to...