Machine learning feature engineering

A non-transitory computer-readable medium storing instructions that, when executed, cause operations comprising:

• receiving, from one or more data sources by a feature engine, a data stream indicating a plurality of events associated with a plurality of entities wherein the event dataset comprises a plurality of timestamps associated with the plurality of events, wherein each of the plurality of timestamps comprises a time component;
• generating, by the feature engine, an event dataset comprising groups of the plurality of events associated with one or more entities of the plurality of entities;
• receiving, via an application programming interface (API), an indication of one or more features associated with the plurality of entities and a configuration for determining the one or more features;
• generating, using the configuration and the event dataset, a feature vector dataset comprising a plurality of feature vectors associated with the plurality of entities, wherein the plurality of feature vectors comprise lists of values of the one or more features;
• receiving additional event data in the data stream indicating an additional plurality of events;
• generating, based on the plurality of timestamps, at least one of the values of the plurality of feature vectors in the generated feature vector dataset based on updating the lists of values using the list of values and the additional event data;
• receiving, via the API, an indication of at least one entity of the plurality of entities;
• and
• outputting at least one feature vector, from the feature vector dataset, associated with the at least one entity.

A feature engine comprising:

• an event store configured to store an event dataset comprising groups of a plurality of events associated with one or more entities of a plurality of entities;
• at least one processor;
• a non-transitory computer-readable memory coupled to the at least one processor, the computer-readable memory having stored thereon computer readable instructions that upon execution on the at least one processor at least cause the feature engine to:
• receive, from a data source, a stream of data indicating the plurality of events associated with the plurality of entities wherein the plurality of events comprise a timestamp associated with each of the plurality of events, wherein the timestamps comprises a time component;
• generate, using the event dataset, a feature vector dataset comprising a plurality of feature vectors associated with the plurality of entities, wherein the plurality of feature vectors comprise lists of values of one or more features;
• receive an indication of an additional plurality of events in the stream of data;
• re-generate, based on the timestamps, at least one of the values in the plurality of feature vectors in the generated feature vector dataset based on updating the lists of values using the list of values and the additional plurality of events;
• receive an indication of at least one entity of the plurality of entities;
• and
• output at least one feature vector, from the feature vector dataset, associated with the at least one entity.