14. The computer-implemented method of claim 11 , further comprising: performing feature engineering on a training corpus for generating a machine learning model that is used to determine solutions for issues with the wireless carrier network; training an initial type of machine learning algorithm using the training corpus to generate the machine learning model; determining a training error measurement of the machine learning model is above a training error threshold; selecting an additional type of machine learning algorithm based on a magnitude of the training error measurement according to one or more algorithm selection rules in response to the training error measurement being above the training error threshold; training the additional type of machine learning algorithm using the training corpus to generate training results; augmenting the machine learning model with the training results from the additional type of machine learning algorithm; and determining that generation of the trained machine learning model is complete when additional training error measurement of the machine learning model following the augmenting is at or below the training error threshold.
14. The computer-implemented method of claim 11 , further comprising: performing feature engineering on a training corpus for generating a machine learning model that is used to determine solutions for issues with the wireless carrier network; training an initial type of machine learning algorithm using the training corpus to generate the machine learning model; determining a training error measurement of the machine learning model is above a training error threshold; selecting an additional type of machine learning algorithm based on a magnitude of the training error measurement according to one or more algorithm selection rules in response to the training error measurement being above the training error threshold; training the additional type of machine learning algorithm using the training corpus to generate training results; augmenting the machine learning model with the training results from the additional type of machine learning algorithm; and determining that generation of the trained machine learning model is complete when additional training error measurement of the machine learning model following the augmenting is at or below the training error threshold. 15. The computer-implemented method of claim 14 , further comprising refining the trained machine learning model based on user feedback regarding an effectiveness of the solution for the root cause, the refining including retraining the machine learning model based on at least one of the training corpus that is modified based on the user feedback or one or more modified algorithm selection rules.