Qwak Feature Store provides a unified feature store for training and real-time operations without the need to write additional code or create manual processes to keep features consistent. There are many ways to create features in the Qwak Feature Store, including Batch-based features, streaming or non-materialized features.
Starting today, Qwak supports Snowflake as a data source for Batch features
The Data Source connectors provide you with a consistent data source interface for any database and create a standard way to combine stream and batch data sources for Feature Transformations.
Defining a Feature Set enables you to create features from your analytical data: when calculating feature values, Qwak will simply read from the underlying data source.
For example, in a fraud detection model use case, we might have two values to pull from a Snowflake data source:
And two from Streaming events from Kafka:
Snowflake data source connector definition:
Register batch feature using the Snowflake connector:
Qwak Feature Store helps ensure models make accurate predictions by making the same features available for both training and inference.
For more information, feel free to reach out at firstname.lastname@example.org