LogoLogo
v0.12-branch
v0.12-branch
  • Introduction
  • Community
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • Concepts
      • Overview
      • Data source
      • Entity
      • Feature view
      • Feature service
      • Feature retrieval
    • Architecture
      • Overview
      • Feature repository
      • Registry
      • Offline store
      • Online store
      • Provider
    • FAQ
  • Tutorials
    • Overview
    • Driver ranking
    • Fraud detection on GCP
    • Real-time credit scoring on AWS
  • How-to Guides
    • Running Feast with GCP/AWS
      • Install Feast
      • Create a feature repository
      • Deploy a feature store
      • Build a training dataset
      • Load data into the online store
      • Read features from the online store
    • Running Feast in production
    • Upgrading from Feast 0.9
    • Adding a custom provider
    • Adding a new online store
    • Adding a new offline store
  • Reference
    • Data sources
      • File
      • BigQuery
      • Redshift
    • Offline stores
      • File
      • BigQuery
      • Redshift
    • Online stores
      • SQLite
      • Redis
      • Datastore
      • DynamoDB
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
Powered by GitBook
On this page

Was this helpful?

Edit on Git
Export as PDF
  1. Tutorials

Real-time credit scoring on AWS

Credit scoring models are used to approve or reject loan applications. In this tutorial we will build a real-time credit scoring system on AWS.

PreviousFraud detection on GCPNextRunning Feast with GCP/AWS

Last updated 3 years ago

Was this helpful?

When individuals apply for loans from banks and other credit providers, the decision to approve a loan application is often made through a statistical model. This model uses information about a customer to determine the likelihood that they will repay or default on a loan, in a process called credit scoring.

In this example, we will demonstrate how a real-time credit scoring system can be built using Feast and Scikit-Learn on AWS, using feature data from S3.

This real-time system accepts a loan request from a customer and responds within 100ms with a decision on whether their loan has been approved or rejected.

This end-to-end tutorial will take you through the following steps:

  • Deploying S3 with Parquet as your primary data source, containing both and

  • Deploying Redshift as the interface Feast uses to build training datasets

  • Registering your features with Feast and configuring DynamoDB for online serving

  • Building a training dataset with Feast to train your credit scoring model

  • Loading feature values from S3 into DynamoDB

  • Making online predictions with your credit scoring model using features from DynamoDB

Real-time Credit Scoring Example
loan features
zip code features
View Source on Github