Understanding a feature store as a data management tool for machine learning, which allows users to share features and create robust ML pipelines, is important for data scientists and engineers.

Feature stores helps MLOps with better collaboration, faster development and deployment of models in production, better model accuracy, speeds up use case adoption, democratize ML, and more.

learn what Feast is as an open-source feature store, how it serves features in production, operationalizes your analytics data, tracks and retrieves features for training and inference, and a live demonstration on how it helps the Fintech space in credit scoring cases.

Discussion Topics:

What is a feature store? How Feast helps?
How to setup Feast in an AWS environment?
What challenges do MLOps address?
How could Feast make a sustainable Feature Store infrastructure?
A live demo on real-time credit scoring via Feast
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