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    ANOMALY DETECTION OF MODEL PERFORMANCE IN AN MLOPS PLATFORM
    11.
    发明申请
    ANOMALY DETECTION OF MODEL PERFORMANCE IN AN MLOPS PLATFORM 有权

    公开(公告)号：US20220353166A1

    公开(公告)日：2022-11-03

    申请号：US17696532

    申请日：2022-03-16

    Applicant: Cisco Technology, Inc.

    Inventor： Vinay Kumar Kolar ,  Jean-Philippe Vasseur ,  Grégory Mermoud ,  Pierre-André Savalle

    IPC: H04L43/00 ,  G06F17/18 ,  G06K9/62 ,  G06N20/00

    Abstract: In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.

    GLOBALLY AVOIDING SIMULTANEOUS REROUTES IN A NETWORK
    13.
    发明申请
    GLOBALLY AVOIDING SIMULTANEOUS REROUTES IN A NETWORK 有权

    公开(公告)号：US20220294738A1

    公开(公告)日：2022-09-15

    申请号：US17829435

    申请日：2022-06-01

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Grégory Mermoud ,  Vinay Kumar Kolar

    IPC: H04L47/127 ,  H04L47/2475 ,  H04L47/28

    Abstract: In one embodiment, a device obtains, from a plurality of routers in a network, a set of routing policies that collectively specify a first set of paths in the network, a second set of paths in the network, and time periods during which traffic is to be rerouted from one of the first set of paths to one of the second set of paths in the network. The device identifies overlapping path segments of the second set of paths in the network. The device makes, based in part on the overlapping path segments, a prediction that two or more of the set of routing policies will cause congestion along paths with overlapping path segments. The device adjusts, based on the prediction, the set of routing policies, to avoid causing the congestion.

    Compressed transmission of network data for networking machine learning systems
    14.
    发明授权
    Compressed transmission of network data for networking machine learning systems 有权

    公开(公告)号：US11438240B2

    公开(公告)日：2022-09-06

    申请号：US16808896

    申请日：2020-03-04

    Applicant: Cisco Technology, Inc.

    Inventor： Mukund Yelahanka Raghuprasad ,  David Tedaldi ,  Vinay Kumar Kolar ,  Jean-Philippe Vasseur

    IPC: H04L41/16 ,  G06N3/08 ,  H04L47/2441 ,  H04L41/5019 ,  G06K9/62

    Abstract: In one embodiment, a service receives telemetry data indicative of a plurality of performance metrics captured in a network. The service jointly trains, using the received telemetry data, a compression model and an inference model, the compression model being a first machine learning model trained to convert the telemetry data into a compressed representation of the telemetry data and the inference model being a second machine learning model trained to take the compressed representation of the telemetry data as input and apply a classification label to it. The service deploys the compression model to the network. The service receives compressed telemetry data generated by the compression model deployed to the network. The service uses the inference model to classify the compressed telemetry data generated by the compression model deployed to the network.

    Anomaly detection of model performance in an MLOps platform
    15.
    发明授权
    Anomaly detection of model performance in an MLOps platform 有权

    公开(公告)号：US11310141B2

    公开(公告)日：2022-04-19

    申请号：US16710836

    申请日：2019-12-11

    Applicant: Cisco Technology, Inc.

    Inventor： Vinay Kumar Kolar ,  Jean-Philippe Vasseur ,  Grégory Mermoud ,  Pierre-Andre Savalle

    IPC: H04L12/26 ,  G06N20/00 ,  H04L43/00 ,  G06F17/18 ,  G06K9/62

    Abstract: In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.

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