Systems and methods for implementing an intelligent application program interface for an intelligent optimization platform

A method implementing an application programming interface that interfaces with a machine learning tuning service, the method comprising:

• implementing an application programming interface (API) that is in operable communication with the machine learning tuning service that:
• implements a first API call function that:
• includes an tuning work request that sets a plurality of tuning parameters for tuning, by the machine learning tuning service, hyperparameters of a machine learning model, wherein the machine learning model includes a machine learning model that is implemented by a subscriber to the machine learning tuning service;
• initializes an asynchronous operation of each of a plurality of distinct tuning worker instances of the machine learning tuning service that execute distinct tuning tasks for tuning the hyperparameters of the machine learning model according to the plurality of tuning parameters;
• implements a second API call function that:
• generates proposed hyperparameter values selected from a plurality of raw values for each of the hyperparameters of the machine learning model;
• and
• implements an API function that returns to the machine learning tuning service performance metrics of the machine learning model based on the proposed hyperparameter values.

A system implementing an application programming interface that interfaces with a machine learning tuning service for tuning a machine learning model, the system comprising:

• an intelligent application programming interface (API) that is in operable communication with a machine learning tuning service that:
• implements a first API request function that:
• includes a tuning work request that sets a plurality of tuning parameters for tuning, by the machine learning tuning service, hyperparameters of a machine learning model, wherein the machine learning model includes a machine learning model that is external to the machine learning tuning service and implemented by a subscriber to the machine learning tuning service;
• initializes an operation of each of a plurality of distinct tuning worker instances of the machine learning tuning service that execute distinct tuning tasks for tuning the hyperparameters of the machine learning model according to the plurality of tuning parameters;
• implements a second API request function that:
• provides one or more suggestions comprising proposed hyperparameter values selected from a plurality of raw values for each of the hyperparameters of the machine learning model.