公开(公告)号：US20230237779A1

公开(公告)日：2023-07-27

申请号：US17582959

申请日：2022-01-24

Applicant: Cisco Technology, Inc.

Inventor： Elvira Dzhuraeva ,  Xinyuan Huang ,  Ashutosh Arwind Malegaonkar ,  Patrick James Riel

IPC: G06V10/774 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06V10/7747 ,  G06V10/776 ,  G06V10/82

Abstract: 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.

公开(公告)号：US20230236960A1

公开(公告)日：2023-07-27

申请号：US17582997

申请日：2022-01-24

Applicant: Cisco Technology, Inc.

Inventor： Elvira Dzhuraeva ,  Patrick James Riel ,  Xinyuan Huang ,  Ashutosh Arwind Malegaonkar

IPC: G06F11/36

CPC classification number: G06F11/3692

Abstract: 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.

公开(公告)号：US20240428570A1

公开(公告)日：2024-12-26

申请号：US18824483

申请日：2024-09-04

Applicant: Cisco Technology, Inc.

Inventor： Elvira Dzhuraeva ,  Xinyuan Huang ,  Ashutosh Arwind Malegaonkar ,  Patrick James Riel

IPC: G06V10/774 ,  G06V10/776 ,  G06V10/82

Abstract: 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.

公开(公告)号：US20230393896A1

公开(公告)日：2023-12-07

申请号：US17830716

申请日：2022-06-02

Applicant: Cisco Technology, Inc.

Inventor： Ashutosh Arwind Malegaonkar ,  Patrick James Riel ,  Xinyuan Huang ,  Elvira Dzhuraeva

IPC: G06F9/50 ,  G06N20/00

CPC classification number: G06F9/5038 ,  G06N20/00

Abstract: Systems, methods, and computer-readable media are disclosed for a dynamic and intelligent machine learning scheduling platform for running multiple machine learning models simultaneously. The present technology includes receiving output data of a first machine learning model running on an edge device. Further, the present technology includes accessing a set of dynamic rules for scheduling a second machine learning model to run on the edge device. As follows, the present technology includes determining to run the second machine learning model on the edge device in accordance with the set of rules where the first machine learning model and the second machine learning model are run on the edge device in parallel.

公开(公告)号：US12165390B2

公开(公告)日：2024-12-10

申请号：US17582959

申请日：2022-01-24

Applicant: Cisco Technology, Inc.

Inventor： Elvira Dzhuraeva ,  Xinyuan Huang ,  Ashutosh Arwind Malegaonkar ,  Patrick James Riel

IPC: G06V10/774 ,  G06V10/776 ,  G06V10/82

Abstract: 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.

公开(公告)号：US11983104B2

公开(公告)日：2024-05-14

申请号：US17582997

申请日：2022-01-24

Applicant: Cisco Technology, Inc.

Inventor： Elvira Dzhuraeva ,  Patrick James Riel ,  Xinyuan Huang ,  Ashutosh Arwind Malegaonkar

IPC: G06F11/36

CPC classification number: G06F11/3692

Abstract: 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.