Data Processing System with Machine Learning Engine to Provide Dynamic Data Transmission Control Functions

A method, comprising:

• at a computing platform comprising at least one processor, memory, and a communication interface:
• receiving, by the at least one processor and via the communication interface, a plurality of content data streams;
• extracting, by the at least one processor and from the received plurality of content data streams, data associated with an event;
• responsive to extracting the data, determining, by the at least one processor and based on one or more machine learning datasets, at least one of:
• a likelihood that a potential issue associated with the event will occur and an efficiency modification;
• identifying, by the at least one processor and based on the at least one of:
• the likelihood that a potential issue associated with the event will occur and the efficiency modification, a modification to execute on a system from which the extracted data was received;
• and
• executing, by the at least one processor, the identified modification to the system.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to:

• receive a plurality of content data streams;
• extract, from the received plurality of content data streams, data associated with an event;
• responsive to extracting the data, determine, based on one or more machine learning datasets, at least one of:
• a likelihood that a potential issue associated with the event will occur and an efficiency modification;
• identify, based on the at least one of:
• the likelihood that a potential issue associated with the event will occur and the efficiency modification, a modification to execute on a system from which the extracted data was received;
• and
• execute the identified modification to the system.