Systems and methods implementing an intelligent machine learning tuning system providing multiple tuned hyperparameter solutions

A method for tuning hyperparameters for improving an effectiveness including accuracy and computational performances of a machine learning model, the method comprising:

• receiving a tuning work request for tuning two or more hyperparameters of a third-party machine learning model;
• performing, by a machine learning-based tuning service, a first tuning of the two or more hyperparameters in a first tuning region;
• identifying tuned hyperparameter values for each of the two or more hyperparameters based on results of the first tuning;
• setting a failure region based on the tuned hyperparameter values of the first tuning;
• performing, by the machine learning-based tuning service, a second tuning of the two or more hyperparameters in a second tuning region that excludes the failure region;
• identifying additional tuned hyperparameter values for each of the two or more hyperparameters based on results of the second tuning;
• and
• returning the tuned hyperparameter values and the additional hyperparameter values for implementing the third-party machine learning model with one of the tuned hyperparameter values and the additional hyperparameter values.

A computer-implemented method for tuning hyperparameters of one or more of a machine learning model, a complex system, a model, and a simulation, the computer-implemented method comprising:

• at a remote tuning service, the remote tuning service being hosted on a distributed networked system of hardware computing servers:
• receiving a tuning work request for tuning two or more hyperparameters of a third-party model or system;
• performing, by a machine learning-based tuning service, a first tuning of the two or more hyperparameters in a first tuning region;
• identifying tuned hyperparameter values for each of the two or more hyperparameters based on results of the first tuning;
• setting a failure region based on the tuned hyperparameter values of the first tuning;
• performing, by the machine learning-based tuning service, a second tuning of the two or more hyperparameters in a second tuning region that excludes the failure region;
• identifying additional tuned hyperparameter values for each of the two or more hyperparameters based on results of the second tuning;
• and
• returning the tuned hyperparameter values and the additional hyperparameter values for implementing the third-party model or system with one of the tuned hyperparameter values and the additional hyperparameter values.