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.
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 loan features and zip code features
    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
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