Question

A data scientist has created a feature table in Unity Catalog named prod.retail.customer_features with primary key customer_id. They build a FeatureLookup to join these features onto a labeled training DataFrame using FeatureEngineeringClient.create_training_set(). When they call training_set.load_df(), the resulting DataFrame contains null values for all feature columns even though the feature table is fully populated. What is the most likely cause?

Accepted Answer

The lookup_key specified in the FeatureLookup does not match the values or column present in the labeled DataFrame, so no rows join to the feature table's primary key.

Answer

The feature table must be published to an online store before create_training_set() can retrieve any values.

Answer

MLflow autologging was disabled, which prevents the Feature Engineering client from reading feature values.

Answer

The Databricks Runtime for ML version is too old to support Unity Catalog feature tables, silently returning nulls.