Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.

Abstract: Machine learning feature engineering systems and methods comprise an event ingestion module that receives event data associated with entities. The ingestion module determines which entities are associated with events of the event data. The ingestion module stores the events, grouped by associated entity, in a related event store. A user defines features associated with the entities via an API and/or a feature studio. A feature computation layer determines values for the features based on the grouped events stored to the related event store. The feature computation layer stores the computed feature values and timestamps to a feature store. When new data is received, the feature computation layer computes one or more of the feature values for different times based on the timestamps. Feature vectors are generated using the computed feature values and output to the user via the API and/or feature studio.

Abstract: A method for generating machine learning training examples using data indicative of events associated with a plurality of entities. The method comprises receiving an indication of one or more selected entities of the plurality of entities, receiving information indicative of selecting one or more prediction times associated with each of the one or more selected entities, and receiving information indicative of selecting one or more label times associated with each of the one or more selected entities. Each of the one or more label times corresponds to at least one of the one or more prediction times, and the one or more label times occur after the corresponding one or more prediction times. Data associated with the one or more prediction times and the one or more label times is extracted from the data indicative of events associated with the plurality of entities.

Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.