Work with online stores

This article describes how to work with online feature stores for real-time serving of feature values. With real-time serving, you publish feature tables to a low-latency database and deploy the model to a REST endpoint.

Databricks Feature Store also supports automatic feature lookup. In this case, the input values provided by the client include values that are only available at the time of inference. The model incorporates logic to automatically fetch the feature values it needs from the provided input values.

The diagram illustrates the relationship between MLflow and Feature Store components for real-time serving.

Databricks Feature Store supports these online stores:

Online store provider	Publish	Feature lookup in Legacy MLflow Model Serving	Feature lookup in Model Serving
Amazon DynamoDB (Feature Store client v0.3.8 and above)	X	X	X
Amazon Aurora (MySQL-compatible)	X	X
Amazon RDS MySQL	X	X

Start using online stores

See the following articles to get started with online stores:

• Authentication for working with online feature stores

• Publish features to an online feature store

• Automatic feature lookup with MLflow models on Databricks (includes example notebook)