Training an ML model

In Chapter 1, Introduction to ML Engineering on AWS, we trained a binary classifier model that aims to predict if a hotel booking will be canceled or not using the available information. In this chapter, we will use the (intentionally simplified) dataset from Downloading the Sample Dataset and train a regression model that will predict the value of y (continuous variable) given the value of x. Instead of relying on ready-made AutoML tools and services, we will be working with a custom script instead:

Figure 2.23 – Model life cycle

When writing a custom training script, we usually follow a sequence similar to what is shown in the preceding diagram. We start by defining and compiling a model. After that, we load the data and use it to train and evaluate the model. Finally, we serialize and save the model into a file.

Note

What happens after the model has been saved? The model file can be used and loaded in an inference endpoint...