Feature stores serve as centralized repositories for features used in ML models, promoting consistency and reusability. They support both:

• Batch features for model training.
• Real-time features for serving models in production.

Snowplow’s enriched event data provides a rich source of raw information for feature generation. Once processed, these features can be stored in a feature store such as Feast or Tecton, enabling fast, consistent access during both training and inference.