Cisco Technology, Inc.

Patrick James Riel

Xinyuan Huang

Ashutosh Arwind Malegaonkar

Elvira Dzhuraeva

Systems, methods, and computer-readable media are disclosed for dynamically adjusting a configuration of a pre-processor and/or a post-processor of a machine learning system. In one aspect, a machine learning system can receive raw data at a pre-processor where the pre-processor being configured to generate pre-processed data, train a machine learning model based on the pre-processed data to generate output data, process the output data at a post-processor to generate inference data, and adjust, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data.

Dynamic configuration of a machine learning system

Systems, methods, and computer-readable media are disclosed for validating a machine learning model. In one aspect, a machine learning model validation system can receive a test machine learning model, analyze an output of the test machine learning model, determine a degree of similarity between the test machine learning model and one or more machine learning models stored in a database based on the output of the test machine learning model, and determining whether the test machine learning model complies with a set of validation rules based on the degree of the similarity with respect to one or more thresholds.

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Concord, CA, United States of America

Patrick James Riel