To deploy AI across the enterprise, you must be able to access relevant features to make the best use of predictive models. Often, the starting point of your data does not contain the right set of features. Feature Discovery discovers and generates new features from multiple datasets so that you no longer need to perform manual feature engineering to consolidate multiple datasets into one.
Select topics from the following table to learn about the feature engineering workflow:
|Feature Discovery projects||Create and configure projects with secondary datasets, including a simple use-case-based workflow overview.|
|Time-aware feature engineering||Configure time-aware feature engineering.|
|Derived features||Introduction to the list of aggregations and the feature reduction process.|
|Predictions||Score data with models created using secondary datasets.|