Feast AWS Credit Scoring Tutorial is a tutorial project that demonstrates building a real-time credit scoring application using Feast with AWS services.
The tutorial addresses the challenge of managing features for both model training and online inference in a production credit scoring system. It shows how Feast orchestrates data from multiple AWS sources: Redshift and S3 for offline feature enrichment during training, and DynamoDB for low-latency feature serving during online inference. The example uses a loan dataset enriched with zipcode and credit history features to train a model that predicts loan defaults, then serves those same features for real-time credit decisions.
This tutorial suits developers building machine learning systems on AWS who want to understand how to structure feature management across training and serving pipelines. It is particularly relevant for those working with Redshift as a data warehouse and needing to serve features from DynamoDB in production. The project provides a complete working example rather than abstract documentation, including infrastructure setup via Terraform, feature repository configuration, model training, and an interactive Streamlit interface for testing predictions.
The project maintains a single, focused example that remains stable and self-contained. Development activity centers on keeping the tutorial aligned with Feast's evolving API and AWS service changes, ensuring the infrastructure-as-code templates and configuration examples remain functional. The repository serves as reference material for a specific architectural pattern rather than as an actively developed tool with frequent feature additions.
