Set Up a Feature Store with Feast

Step 1: Install and initialize Feast

Install Feast and create a new feature repository. Understand the Feast architecture and core concepts.

pip install feast
feast init my_feature_store

Step 2: Define feature definitions

Create feature definitions for your ML use cases. Define entities, features, and data sources in your feature store.

Step 3: Set up data sources

Connect Feast to your data sources (databases, data warehouses, or files). Configure batch and streaming sources.

Step 4: Generate training data

Use Feast to generate point-in-time correct training datasets. Ensure features are consistent between training and inference.

Step 5: Deploy online feature serving

Set up online feature serving for real-time inference. Configure feature stores and ensure low-latency access.

Step 6: Integrate with your ML pipeline

Connect your feature store to model training and serving pipelines. Use Feast SDKs to retrieve features in your applications.