System and method for api resource prediction

A method for resource prediction, the method comprising:

• receiving, by a processor (202), one or more requests from one or more computing devices (104), wherein the received one or more requests are based on a training of one or more models via a machine learning (ML) engine (108);
• determining, by the processor (202), at least recently used trained model from the one or more trained models and unloading a plurality of least used models from the one or more trained models based on the received one or more requests;
• utilizing, by the processor (202), a caching mechanism to optimize a memory space associated with a memory (204) of the processor (202), by selectively loading the at least recently used trained model;
• predicting, by the processor (202), via the at least recently used trained model from the one or more trained models, resource data based on the optimized memory space;
• and
• enabling, by the processor (202), the resource prediction based on the predicted resource data.

A user equipment (UE) (104) for resource prediction, the UE (104) comprising:

• one or more processors communicatively coupled to a processor (202) in a system (110), wherein the one or more processors are coupled with a memory, and wherein said memory stores instructions which when executed by the one or more processors causes the one or more processors to:
• transmit one or more requests to the processor (202) via a network (106), wherein the processor (202) is configured to:
• receive the one or more requests from the UE (104), wherein the received one or more requests are based on a training of one or more models via a machine learning (ML) engine (108) operatively coupled to the processor (202);
• determine at least recently used trained model from the one or more trained models and unload a plurality of least used models from the one or more trained models based on the received one or more requests;
• utilize a caching mechanism to optimize a memory space associated with a memory (204) of the processor (202) by selectively unloading the at least recently used trained model;
• predict, via the at most recently used trained model from the one or more trained models, resource data based on the optimized memory space;
• and
• enable the resource prediction based on the predicted resource data.