Abstract: A system for generating machine learning feature vectors or examples is disclosed herein. The system comprises at least one database configured to store data indicative of events associated with a plurality of entities, an application programming interface (API) server configured to receive a user query from at least one user device, and at least one computing node in communication with the API server and the at least one database. The at least one computing node is configured at least to receive, from the API server and at a first time, a first indication of the user query. The at least one computing node is configured to generate, based at least on the data indicative of events and the first indication of the user query, results associated with the user query, wherein the results comprise one or more feature vectors or examples for use with a machine learning algorithm. The at least one computing node is configured to cause storage of data indicative of the results in the at least one database.

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 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.