Regression with Amazon SageMaker Autopilot (Parquet input)

This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.

This is the accompanying notebook for the blog post Run AutoML experiments with large parquet datasets using Amazon SageMaker Autopilot. The example here is almost the same as Regression with Amazon SageMaker XGBoost algorithm (Parquet).

This notebook tackles the exact same problem with the same solution, but has been modified for a Parquet input to be trained in SageMaker Autopilot. The original notebook provides details of dataset and the machine learning use-case.

This notebook was tested in Amazon SageMaker Studio on a ml.t3.medium instance with Python 3 (Data Science) kernel.

We will use PyArrow library to store the Abalone dataset in the Parquet format.

After setting the parameters, we kick off training, and poll for status until training is completed, which in this example, takes under 1 hour.

Please refer to other Autopilot example notebooks such as Direct Marketing with Amazon SageMaker Autopilot and Customer Churn Prediction with Amazon SageMaker Autopilot to see how to investigate details of each training, deploy the best candidate and run inference.

Notebook CI Test Results

This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.