Example notebooks: Explore modeling with Amazon SageMaker Autopilot

Direct marketing with Amazon SageMaker Autopilot: This notebook demonstrates how uses the Bank Marketing Data Set to predict whether a customer will enroll for a term deposit at a bank. You can use Autopilot on this dataset to get the most accurate ML pipeline by exploring options contained in various candidate pipelines. Autopilot generates each candidate in a two-step procedure. The first step performs automated feature engineering on the dataset. The second step trains and tunes an algorithm to produce a model. The notebook contains instructions on how to train the model and how to deploy the model to perform batch inference using the best candidate.

Customer Churn Prediction with Amazon SageMaker Autopilot: This notebook describes using machine learning for the automated identification of unhappy customers, also known as customer churn prediction. The example shows how to analyze a publicly available dataset and perform feature engineering on it. Next it shows how to tune a model by selecting the best performing pipeline along with the optimal hyperparameters for the training algorithm. Finally, it shows how to deploy the model to a hosted endpoint and how to evaluate its predictions against ground truth. However, ML models rarely give perfect predictions. That's why this notebook also shows how to incorporate the relative costs of prediction mistakes when determining the financial outcome of using ML.

Top Candidates Customer Churn Prediction with Amazon SageMaker Autopilot and Batch Transform (Python SDK): This notebook also describes using machine learning for the automated identification of unhappy customers, also known as customer churn prediction. This notebook demonstrates how to configure the model to obtain the inference probability, select the top N models, and make Batch Transform on a hold-out test set for evaluation.

Bringing your own data processing code to Amazon SageMaker Autopilot: This notebook demonstrates how to incorporate and deploy custom data processing code when using Amazon SageMaker Autopilot. It adds a custom feature selection step to remove irrelevant variables to an Autopilot job. It then shows how to deploy both the custom processing code and models generated by Autopilot on a real-time endpoint and, alternatively, for batch processing.