Machine learning framework and method for using the same

A system comprising:

• a network interface;
• a memory to store instructions;
• one or more processors coupled to the memory and the network interface to execute the stored instructions to:
• define a plurality of features as independent features, wherein each feature of the plurality features is defined by a key and a value type, and each feature of the plurality of features is further defined to execute a mapper that converts data records generated by disparate systems and having the key and value type to a stream of events of a same event object type having a unified data format;
• execute the mapper defined for each feature of the plurality of features to access a plurality of data records generated by the disparate systems and generate the stream of events of the same event object type in the unified data format from the plurality of data records to model a temporal stream of time-stamped records, the mapper adding a read-only timestamp to each of the stream of events;
• define a final feature that groups the plurality of features together, such that each feature of the plurality of features represents a sub-feature of the final feature;
• compile the plurality of features and the final feature into a plurality of jobs, wherein each job of the plurality of jobs is compiled by generating a combiner that aggregates events from a corresponding stream of events;
• and
• execute each of the plurality of jobs using a computing platform, wherein execution of a job comprises the one or more processors to:
• execute combiners compiled for jobs associated with the plurality of features to generate a value for each of the plurality of features, including sending network related communications to access one or more of the time-stamped records, based on a key that corresponds to said each of the plurality of features, from one or more remote storage locations, and
• execute a combiner compiled for the final feature to generate a value for the final feature separately from computing the values for the plurality of features, including grouping together the values for the plurality of features.

One or more non-transitory computer readable storage media having instructions stored thereupon which, when executed by a computing platform having at least a processor and a memory therein, cause the computing platform to perform operations comprising:

• defining, at the computing platform, a plurality of features as independent features, wherein each feature of the plurality of features is defined by a key and a value type, and each feature of the plurality of features is further defined to execute a mapper that converts data records generated by disparate systems and having the key and value type to a stream of events of a same event object type having a unified data format;
• executing, by the computing platform, the mapper defined for each feature of the plurality of features comprising:
• accessing, by the mapper, a plurality of data records generated by the disparate systems and generating, by the mapper, the stream of events of the same event object type in the unified data format from the plurality of data records to model a temporal stream of time-stamped records, the mapper adding a read-only timestamp to each of the stream of events;
• defining, at the computing platform, a final feature based on the plurality of features, such that each feature of the plurality of features is used for computation of the final feature;
• compiling, by the computing platform, the plurality of features and the final feature into a plurality of jobs, wherein each job of the plurality of jobs is compiled by generating a combiner that aggregates events from a corresponding stream of events;
• and
• executing each of the plurality of jobs using the computing platform, wherein executing a job comprises:
• executing, using the computing platform, combiners compiled for each of the plurality of features to generate a value for each of the plurality of features, including sending network related communications to access one or more of the time-stamped records, based on a key that corresponds to said each of the plurality of features, from one or more remote storage locations, and
• executing, using the computing platform, a combiner compiled for the final feature to generate a value for the final feature separately from computing the values for the plurality of features, including grouping together the values for the plurality of features.