Feature discovery in a feature store is the process of identifying and selecting relevant features for a machine learning model based on their importance and correlation with the target variable.

To discover features in Databricks, first, create a feature store table with the dataset. Then, use the feature store API to retrieve the features and perform feature selection techniques such as correlation analysis, feature importance, and dimensionality reduction. Finally, select the most relevant features for the machine learning model.

Feature discovery in a feature store is not possible as it requires manual feature engineering and selection.

To discover features in Databricks, simply list all the features in the dataset and choose the ones that seem most relevant for the machine learning model.