Nov 2, 2022, 8:30-9:30 PM Pacific Time
Beginner to Intermediate
About the Workshop
In this workshop we’ll show how to turn Kaggle’s American Express – Default Prediction competition into a Kubeflow Pipeline using the KFP SDK and the Kale JupyterLab extension.
About the Kaggle Competition
A default happens when a borrower ceases to make the due payments on a loan. Whether the debt is secured, like a mortgage secured by real estate, or unsecured, like credit cards or student loans, defaults can happen.
Credit default prediction is central to managing risk in a consumer lending business. Credit default prediction allows lenders to optimize lending decisions, which leads to a better customer experience and sound business economics. Current models exist to help manage risk. But it’s possible to create better models that can outperform those currently in use.
In this competition, you will develop machine learning models that can effectively predict credit defaults using a large-scale industrial dataset from American Express.