A Credit Scoring Application Using Feast on AWS

Speaker: Achal Shah, Software Engineer, Tecton.ai

Feature stores play a pivotal role in the modern machine learning stack. More and more data scientists and engineers are working together to create and manage features for both model training and for real-time inference. But how do you build, deploy, and use a feature store in the first place?In the tutorial, we will walk through a use case to build a real-time credit scoring application using Feast and AWS storage components: Redshift (offline store) and DynamoDB (online store). In particular, we will talk through how to:

• Create a training dataset as a loan table, which holds historical loan data with accompanying features, including a target variable: whether a user has defaulted on their loan.
• Demonstrate on-demand feature transformations
• Build a predictive model and use SageMaker for model experiments
• Serve real-time predictions with this model by using DynamoDB as the online feature store