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US 12,602,611 B2Granted
Dynamic updating of machine learning models
AssigneeAT&T Intellectual Property I, L.P.
InventorRaghuraman Gopalan
Life of this patent
Estimating the term…
Abstract
A method may include a processor training a machine learning model with a training data set, computing a data distribution of the training data set, processing a stream of new data to determine a likelihood of the new data from the data distribution that is computed, incrementing a counter when the likelihood of the new data is less than a first threshold, and retraining the machine learning model when the counter exceeds a second threshold.
Claims20
A device comprising:
- a processor;
- and
- a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations, the operations comprising:
- training a machine learning model with a training data set;
- computing a data distribution of the training data set;
- processing a stream of new data to determine a probability of the new data from a data distribution of the new data and the data distribution of the training data set that is computed;
- incrementing a counter when the probability of the new data is less than a first threshold;
- determining, in response to the counter exceeding a second threshold, a future time at which to retrain the machine learning model;
- retraining the machine learning model at the future time that is determined;
- calculating an optimization function that identifies a plurality of relevance scores of a plurality of features of the machine learning model with respect to a service of the machine learning model;
- and
- transmitting an instruction to at least one network element to decrease at least one of:
- a data sampling rate or a rate of data collection with respect to at least a first feature of the plurality of features with at least a first relevance score of the plurality of relevance scores that is below a third threshold.
10 more elements
A method comprising:
- training, by a processor, a machine learning model with a training data set;
- computing, by the processor, a data distribution of the training data set;
- processing, by the processor, a stream of new data to determine a probability of the new data from a data distribution of the new data and the data distribution of the training data set that is computed;
- incrementing, by the processor, a counter when the probability of the new data is less than a first threshold;
- determining, by the processor in response to the counter exceeding a second threshold, a future time at which to retrain the machine learning model;
- retraining, by the processor, the machine learning model at the future time that is determined;
- calculating, by the processor, an optimization function that identifies a plurality of relevance scores of a plurality of features of the machine learning model with respect to a service of the machine learning model;
- and
- transmitting, by the processor to at least one network element, an instruction to decrease at least one of:
- a data sampling rate or a rate of data collection with respect to at least a first feature of the plurality of features with at least a first relevance score being of the plurality of relevance scores that is below a third threshold.
7 more elements
A non-transitory computer-readable storage medium storing instructions which, when executed by a processor, cause the processor to perform operations, the operations comprising:
- training a machine learning model with a training data set;
- computing a data distribution of the training data set;
- processing a stream of new data to determine a probability of the new data from a data distribution of the new data and the data distribution of the training data set that is computed;
- incrementing a counter when the probability of the new data is less than a first threshold;
- determining, in response to the counter exceeding a second threshold, a future time at which to retrain the machine learning model;
- retraining the machine learning model at the future time that is determined;
- calculating an optimization function that identifies a plurality of relevance scores of a plurality of features of the machine learning model with respect to a service of the machine learning model;
- and
- transmitting an instruction to at least one network element to decrease at least one of:
- a data sampling rate or a rate of data collection with respect to at least a first feature of the plurality of features with at least a first relevance score of the plurality of relevance scores that is below a third threshold.
7 more elements
Dependencies are inferred from the claim text.