Accelerated machine learning
AssigneeInternational Business Machines Corporation
InventorsMartin Wistuba, Tejaswini Pedapati
Life of this patent
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Abstract
Various embodiments are provided for accelerating machine learning in a computing environment by one or more processors in a computing system. Selected data may be received for training machine learning pipelines. Each of the machine learning pipelines may be scored according to one or more learning curves while training on selected data. Completion of the training on the selected data may be permitted for those of the machine learning pipelines having a score greater than a selected threshold. The training on the selected data may be terminated, prior to completion, on those of the machine learning pipelines having a score less than a selected threshold.
Claims25
A method for accelerating machine learning in a computing environment by one or more processors comprising:
- selecting a machine learning pipeline trained on a dataset according to a ranking of a plurality of machine learning pipelines each permitted to complete training on the dataset in response to applying, during the training, one or more learning curves that predicts a machine learning pipeline performance level.
A system for accelerating machine learning in a computing environment, comprising:
- one or more computers with executable instructions that when executed cause the system to:
- select a machine learning pipeline trained on a dataset according to a ranking of a plurality of machine learning pipelines each permitted to complete training on the dataset in response to applying, during the training, one or more learning curves that predicts a machine learning pipeline performance level.
A computer program product for accelerating machine learning in a computing environment, the computer program product comprising:
- one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instruction comprising:
- program instructions to select a machine learning pipeline trained on a dataset according to a ranking of a plurality of machine learning pipelines each permitted to complete training on the dataset in response to applying, during the training, one or more learning curves that predicts a machine learning pipeline performance level.
A method for accelerating machine learning in a computing environment by one or more processors comprising:
- receiving selected data for training a plurality of machine learning pipelines, wherein a machine learning pipeline includes one or more machine learning models, a plurality of various data curations, one or more processing operation, or a combination thereof;
- scoring each of the plurality of machine learning pipelines according to one or more learning curves while training on selected data;
- permitting completion of the training on the selected data for those of the plurality of machine learning pipelines having a score greater than a selected threshold;
- and
- terminating, prior to completion, the training on the selected data those of the plurality of machine learning pipelines having a score greater than a selected threshold.
A method for accelerating machine learning in a computing environment by one or more processors comprising:
- training one or more machine learning pipelines using selected data;
- assigning a learning curve score, using one or more learning curves, to the one or more machine learning pipelines during the training;
- allowing the training of those of the one or more machine learning pipelines having the learning curve score greater than a selected threshold while terminating the training of those of the one or more machine learning pipelines having the learning curve score less than a selected threshold;
- and
- identifying a trained machine learning pipeline from those of the one or more machine learning pipelines having completed the training based on a ranking of each of the scores.
Dependencies are inferred from the claim text.