Feast Fraud Tutorial is a tutorial and reference implementation that demonstrates feature engineering and model serving for fraud detection on Google Cloud Platform.
The tutorial addresses the problem of building end-to-end fraud detection systems by showing how to use Feast, a feature management platform, in conjunction with GCP services. The approach works by providing Jupyter notebooks that generate raw data in BigQuery and cloud functions that manage table timestamps, establishing a complete workflow from data preparation through feature engineering.
Developers should use this tutorial if they are building fraud detection models on GCP and want to understand how to integrate Feast into that workflow. The tutorial requires a GCP account with permissions to create and manage projects, buckets, and BigQuery datasets. It suits teams already committed to the GCP ecosystem who need concrete examples of feature management patterns rather than abstract documentation.
The project consists primarily of Jupyter notebooks organized in a dedicated directory alongside supporting cloud function code. The materials are designed as a reference implementation rather than a production framework, making it most valuable for learning and prototyping rather than as a foundation for large-scale systems.
