| CPC G06N 20/00 (2019.01) [G06F 9/45558 (2013.01); G06F 12/0871 (2013.01); G06N 3/08 (2013.01); G06F 2009/45583 (2013.01); G06F 2009/45595 (2013.01)] | 20 Claims |
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1. A method, comprising:
training, by a first training process, a machine learning model using a given training dataset comprising a plurality of input training data elements and respective ones of a plurality of output labels, wherein the training learns one or more parameters of the machine learning model, using the given training dataset to learn to recognize a given output label of the plurality of output labels for a given input training data element, wherein the one or more learned parameters of the machine learning model comprise one or more weights for respective connections between a plurality of layers of the machine learning model, and wherein the trained machine learning model generates one or more of: (i) at least one prediction and (ii) at least one classification, wherein the one or more learned parameters of the machine learning model are distinct from the plurality of input training data elements and from the plurality of output labels; and
caching, in at least one cache memory, at least one of the one or more learned parameters of the machine learning model from the training with the given training dataset, wherein the cached at least one learned parameter of the machine learning model is reused for a subsequent training, by a subsequent training process, wherein the subsequent training process is distinct from the first training process, wherein the cached at least one learned parameter of the machine learning model is provided, in response to a request by the subsequent training process, to the subsequent training process by a processor-based cache manager that manages the at least one cache memory for a plurality of training processes;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
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