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

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

Organization Name

Intel Corporation

Inventor(s)

Alexandra Johnson of San Francisco CA (US)

Patrick Hayes of San Francisco CA (US)

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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