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