Use features in online workflows

When you use feature engineering in Unity Catalog, every step of your model development process is integrated into the Databricks Data Intelligence Platform. This means you can build automated data pipelines to compute and serve feature values while Databricks handles the infrastructure for you. The Databricks platform provides real-time serving for both features and models, including on-demand computation of feature values.

Automatic feature lookup

When you train a model using Databricks feature engineering and serve it with Databricks Model Serving, the model automatically looks up feature values from Databricks online tables or from third-party online stores. This happens automatically with no setup required.

When a scoring request comes in to the model, Model Serving automatically retrieves the published feature values needed by the model. In this way, the most recent feature values are always used for predictions. For details and example notebooks, see Automatic feature lookup with Databricks Model Serving.

The following diagram illustrates the relationship between the platform components for real-time serving.

On-demand features

Machine learning models for real-time applications often require the most recent feature values. In the example shown in the diagram, one feature for a restaurant recommendation model is the user’s current distance from a restaurant. This feature must be calculated “on demand”—that is, at the time of the scoring request. Upon receiving a scoring request, the model looks up the restaurant’s location, and then applies a pre-defined function to calculate the distance between the user’s current location and the restaurant. That distance is passed as an input to the model, along with other precomputed features from the feature store. For more information, see Compute features on-demand.