View source for US Patent Application 18326467. SYSTEMS AND METHODS FOR IMPLEMENTING AN INTELLIGENT APPLICATION PROGRAM INTERFACE FOR AN INTELLIGENT OPTIMIZATION PLATFORM simplified abstract

=SYSTEMS AND METHODS FOR IMPLEMENTING AN INTELLIGENT APPLICATION PROGRAM INTERFACE FOR AN INTELLIGENT OPTIMIZATION PLATFORM=


==Organization Name==

[[:Category:Intel Corporation|Intel Corporation]]


[[Category:Intel Corporation]]

==Inventor(s)==

[[:Category:Alexandra Johnson of San Francisco CA (US)|Alexandra Johnson of San Francisco CA (US)]][[Category:Alexandra Johnson of San Francisco CA (US)]]

[[:Category:Patrick Hayes of San Francisco CA (US)|Patrick Hayes of San Francisco CA (US)]][[Category:Patrick Hayes of San Francisco CA (US)]]

[[:Category:Scott Clark of San Francisco CA (US)|Scott Clark of San Francisco CA (US)]][[Category:Scott Clark of San Francisco CA (US)]]

==SYSTEMS AND METHODS FOR IMPLEMENTING AN INTELLIGENT APPLICATION PROGRAM INTERFACE FOR AN INTELLIGENT OPTIMIZATION PLATFORM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18326467 titled 'SYSTEMS AND METHODS FOR IMPLEMENTING AN INTELLIGENT APPLICATION PROGRAM INTERFACE FOR AN INTELLIGENT OPTIMIZATION PLATFORM

==Simplified Explanation==

The patent application describes a system and method for implementing an API that controls a machine learning tuning service. This service is designed to improve the accuracy and computational performance of a machine learning model.

* The API allows users to make API calls to the tuning service to control its operations.
* The first API call function sets tuning parameters for tuning the hyperparameters of the machine learning model.
* The tuning service initializes distinct tuning worker instances to perform separate tuning tasks for the hyperparameters.
* The second API call function identifies raw values for the hyperparameters.
* The tuning service generates suggestions for proposed hyperparameter values based on the raw values.
* These suggestions are selected from a range of raw values for each hyperparameter.
* The third API call function returns performance metrics that measure the real-world performance of the machine learning model when executed with the proposed hyperparameter values.


==Original Abstract Submitted==

Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the proposed hyperparameter values.



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