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公开(公告)号：US11301781B2
公开(公告)日：2022-04-12
申请号：US16796489
申请日：2020-02-20
Applicant: Intel Corporation
Inventor： Patrick Hayes , Michael McCourt , Alexandra Johnson , George Ke , Scott Clark
Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
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公开(公告)号：US11163615B2
公开(公告)日：2021-11-02
申请号：US16741895
申请日：2020-01-14
Applicant: Intel Corporation
Inventor： Alexandra Johnson , Patrick Hayes , Scott Clark
Abstract: 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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公开(公告)号：US20220107850A1
公开(公告)日：2022-04-07
申请号：US17516296
申请日：2021-11-01
Applicant: Intel Corporation
Inventor： Alexandra Johnson , Patrick Hayes , Scott Clark
Abstract: 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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Patent Agency Ranking
公开(公告)号：US12141667B2
公开(公告)日：2024-11-12
申请号：US17561480
申请日：2021-12-23
Applicant: Intel Corporation
Inventor： Patrick Hayes , Michael McCourt , Alexandra Johnson , George Ke , Scott Clark
Abstract: A disclosed example includes implementing a first worker instance and a second worker instance to operate in parallel; running a first tuning operation via the first worker instance to tune first hyperparameters; running a second tuning operation via the second worker instance using a Bayesian-based optimization to determine a hyperparameter configuration to evaluate next; evaluating the hyperparameter configuration for an external model using a surrogate model; and selecting the hyperparameter configuration for the external model.
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公开(公告)号：US11709719B2
公开(公告)日：2023-07-25
申请号：US17516296
申请日：2021-11-01
Applicant: Intel Corporation
Inventor： Alexandra Johnson , Patrick Hayes , Scott Clark
CPC classification number: G06F9/54 , G06F11/3495 , G06N20/00
Abstract: 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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公开(公告)号：US20220121993A1
公开(公告)日：2022-04-21
申请号：US17561480
申请日：2021-12-23
Applicant: Intel Corporation
Inventor： Patrick Hayes , Michael McCourt , Alexandra Johnson , George Ke , Scott Clark
Abstract: A disclosed example includes implementing a first worker instance and a second worker instance to operate in parallel running a first tuning operation via the first worker instance to tune first hyperparameters; running a second tuning operation via the second worker instance using a Bayesian-based optimization to determine a hyperparameter configuration to evaluate next; evaluating the hyperparameter configuration for an external model using a surrogate model; and selecting the hyperparameter configuration for the external model.
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公开(公告)号：US12236287B2
公开(公告)日：2025-02-25
申请号：US18326467
申请日：2023-05-31
Applicant: Intel Corporation
Inventor： Alexandra Johnson , Patrick Hayes , Scott Clark
Abstract: 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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8.发明公开公开(公告)号：US20230385129A1
公开(公告)日：2023-11-30
申请号：US18326467
申请日：2023-05-31
Applicant: Intel Corporation
Inventor： Alexandra Johnson , Patrick Hayes , Scott Clark
CPC classification number: G06F9/54 , G06N20/00 , G06F11/3495
Abstract: 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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