US 2022/0044130 A1 · HYPER PARAMETER TUNING FOR MACHINE LEARNING MODELS

Abstract

Techniques for selecting universal hyper parameters for use in a set of machine learning models across multiple computing environments include detection of a triggering condition for tuning a set of universal hyper parameters. The set of universal hyper parameters dictate configuration of the set of machine learning models that are independently executing, respectively, in the multiple computing environments. Based on the detected triggering condition, a first subset of universal hyper parameters from the set of universal hyper parameters are altered to generate a second set of universal hyper parameters. The second set of universal hyper parameters are applied to the set of machine learning models across the multiple computing environments.

Claims20

Dependent claims

A method comprising:

obtaining a set of universal hyper parameters used in a plurality of machine learning models that are independently executing, respectively, in a plurality of computing environments, wherein the set of universal hyper parameters dictate configuration of the plurality of machine learning models;
detecting a triggering condition for tuning the set of universal hyper parameters;
based on the detected triggering condition:
adjusting a first subset of universal hyper parameters from the set of universal hyper parameters to generate a second set of universal hyper parameters;
and
applying the second set of universal hyper parameters to the plurality of machine learning models across the plurality of computing environments,
wherein the method is performed by at least one device including a hardware processor.

4 more elements

A system, comprising:

one or more hardware processors;
a non-transitory computer readable medium comprising instructions which, when executed by the one or more hardware processors, causes performance of operations comprising:
obtaining a set of universal hyper parameters used in a plurality of machine learning models that are independently executing, respectively, in a plurality of computing environments, wherein the set of universal hyper parameters dictate configuration of the plurality of machine learning models;
detecting a triggering condition for tuning the set of universal hyper parameters;
based on the detected triggering condition:
adjusting a first subset of universal hyper parameters from the set of universal hyper parameters to generate a second set of universal hyper parameters;
and
applying the second set of universal hyper parameters to the plurality of machine learning models across the plurality of computing environments.

5 more elements

A non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising:

obtaining a set of universal hyper parameters used in a plurality of machine learning models that are independently executing, respectively, in a plurality of computing environments, wherein the set of universal hyper parameters dictate configuration of the plurality of machine learning models;
detecting a triggering condition for tuning the set of universal hyper parameters;
based on the detected triggering condition:
adjusting a first subset of universal hyper parameters from the set of universal hyper parameters to generate a second set of universal hyper parameters;
and
applying the second set of universal hyper parameters to the plurality of machine learning models across the plurality of computing environments.

3 more elements

Dependencies are inferred from the claim text.

Hyper parameter tuning for machine learning models

Life of this patent

Hyper parameter tuning for machine learning models

Life of this patent

Abstract

Claims20