Using Amazon SageMaker to serve a model
In this section, we will use Amazon SageMaker to serve a model from end to end. You will need an AWS account if you want to follow the examples. Please refer to the Technical requirements section to see how to create an AWS account. We will use an XGBoost model created using the same dataset shown here, . We will not discuss the steps to create and train the model here. We will reuse the trained model created in the tutorial at the link.
We will split the exercise into the following subsections for better understanding:
- Creating a notebook in Amazon SageMaker
- Serving the model using Amazon SageMaker
Creating a notebook in Amazon SageMaker
In this subsection, we will create a notebook that can be used to write our code:
- First of all, let’s log in to our AWS account, and we will see the AWS console home page, as in Figure 15.1.
Figure 15.1 – AWS console home page
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