Exercise – using GCP in AutoML to train an ML model

As we learned earlier in this chapter, AutoML is an automated way for you to build an ML model. It will handle model selection, hyperparameter tuning, and various data preparation steps.

Note that for the data preparation part, it will not be smart enough to transform data from very raw tables, aggregate based on business context, and automatically clean all data to create features. Those activities are still the responsibilities of data engineers and data scientists.

What AutoML will do, however, is perform simple data preparation tasks, such as detecting numeric, binary, categorical, and text features, and then apply the required transformation to be used in the ML training process. Let’s learn how to do this. Here are the steps that you will complete in this exercise:

1. Create a Vertex AI dataset.
2. Train the ML model using AutoML.
3. Choose the compute and budget for AutoML.

For the use case...