Feature lineage in a feature store is the process of tracking the origin, transformation, and usage of features throughout the machine learning pipeline.

To trace the lineage of features used for scoring the model in Databricks, first, retrieve the features from the feature store table using the feature store API. Then, analyze the metadata associated with the features, such as the source dataset, transformation steps, and version history. Finally, use this information to understand the origin and transformation of the features used for scoring the model.

Feature lineage in a feature store is not necessary as it does not provide any significant benefits and can be avoided.

To trace the lineage of features used for scoring the model in Databricks, manually track the features used at each step of the machine learning pipeline and document the changes made to the features.