Building and running SageMaker Autopilot experiments from the notebook

Customer churn is a real problem for businesses and in this example, we will use our knowledge of completing AutoML in Amazon SageMaker Autopilot to build a customer churn prediction experiment using the notebook. In this experiment, we will use a publicly available dataset of US mobile customers provided by Daniel T. Larose in his book Discovering Knowledge in Data. To demonstrate running the full gamut, the sample notebook executes the Autopilot experiment by performing feature engineering, building a model pipeline (along with any optimal hyperparameters), and deploying the model.

The evolution of the UI/API/CLI paradigm has helped us utilize the same interface in multiple formats; in this case, we will be utilizing the capabilities of Amazon SageMaker Autopilot directly from the notebook. Let's get started:

1. Open the autopilot_customer_churn notebook from the amazon-sagemaker-examples/autopilot...