Method and electronic device for optimizing training of data driven model in wireless network

An electronic device, comprising:

• at least one processor comprising processing circuitry;
• and
• a memory communicatively coupled to at least one processor, wherein the memory stores one or more computer programs including processor-executable instructions, and the at least one processor, individually and/or collectively, is configured to:
• obtain at least one candidate data driven model from a plurality of candidate data driven models;
• determine whether the at least one candidate data driven model meets a predefined prediction;
• deploy the at least one candidate data driven model to at least one target deployment environment upon determining that the at least one candidate data driven model meets the predefined prediction;
• and
• tuning at least one hyper-parameter and at least one data driven technique upon determining that the at least one candidate data driven model does not meet the predefined prediction.

One or more non-transitory computer readable storage media storing computer-executable instructions that, when executed by at least one processor of an electronic device, causes the electronic device to perform operations, the operations comprising:

• obtain at least one candidate data driven model from a plurality of candidate data driven models;
• determine whether the at least one candidate data driven model meets a predefined prediction;
• deploy the at least one candidate data driven model to at least one target deployment environment upon determining that the at least one selected candidate data driven model meets the predefined prediction;
• and
• tuning at least one hyper-parameter and at least one data driven technique running in the at least one data driven model upon determining that the at least one candidate data driven model does not meet the predefined prediction.