Method and electronic device for managing machine learning services in a wireless communication network

An electronic device for managing machine learning (ML) services in a wireless communication network, the electronic device comprising:

• memory storing instructions;
• at least one processor coupled to the memory, wherein the instructions, when executed by the at least one processor, cause the electronic device to:
• store a plurality of ML packages in the memory;
• receive, from a server, a trigger which is generated based on at least one network service request indicating an anomaly corresponding to a network slice;
• determine a plurality of parameters corresponding to the at least one network service request, in response to receiving the trigger from the server, wherein the plurality of parameters corresponding to the at least one network service request comprises:
• information of service profile of a network, ML requirements of at least one network operator, network traffic pattern for a specific service and unfilled ML templates associated with the specific service;
• determine at least one ML package from the plurality of ML packages stored in the memory based on the trigger and the plurality of parameters corresponding to the at least one network service request;
• and
• deploy the determined at least one ML package for executing the at least one network service request, wherein each of the deployed at least one ML package includes at least one of a predicted requirement of network resources for implementing a ML technique, a predicted ML model, an error prediction window for the predicted ML model, a periodicity of predicting an error for the predicted ML model, and at least one of a training accuracy and a prediction accuracy for the predicted ML model.

A non-transitory computer-readable storage medium storing instructions, wherein the instructions, when executed by at least one processor of an electronic device for managing machine learning (ML) services, cause the electronic device to perform operations comprising:

• storing a plurality of ML packages in memory of the electronic device;
• receiving, from a server, a trigger which is generated based on at least one network service request indicating an anomaly corresponding to a network slice;
• determining a plurality of parameters corresponding to at least one network service request, in response to receiving the trigger from the server, wherein the plurality of parameters corresponding to the at least one network service request comprises:
• information of service profile of a network, ML requirements of at least one network operator, network traffic pattern for a specific service and unfilled ML templates associated with the specific service;
• determining at least one ML package from the plurality of ML packages stored in the memory based on the trigger and the plurality of parameters corresponding to the at least one network service request;
• and
• deploying the determined at least one ML package for executing the at least one network service request, wherein each of the deployed at least one ML package includes at least one of a predicted requirement of network resources for implementing a ML technique, a predicted ML model, an error prediction window for the predicted ML model, a periodicity of predicting an error for the predicted ML model, or and at least one of a training accuracy and the prediction accuracy for the predicted ML model.