Scheduling predictive models for machine learning systems
AssigneeCisco Technology, Inc.
InventorsJean-Philippe Vasseur, Sukrit Dasgupta, Grégory Mermoud
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Abstract
In one embodiment, a device in a network monitors performance data for a first predictive model. The first predictive model is used to make proactive decisions in the network. The device maintains a supervisory model based on the monitored performance data for the first predictive model. The device identifies a time period during which the supervisory model predicts that the first predictive model will perform poorly. The device causes a switchover from the first predictive model to a second predictive model at a point in time associated with the time period, in response to identifying the time period.
Claims18
A method comprising:
- monitoring, by a device in a network, performance data for a first predictive model, wherein the first predictive model is used to make proactive decisions in the network;
- maintaining, by the device, a supervisory model based on the monitored performance data for the first predictive model;
- identifying, by the device, a time period during which the supervisory model predicts that the first predictive model will perform poorly at a future time;
- based on the identified time period, scheduling, by the device, a switchover from the first predictive model to a second predictive model at a point in time associated with the time period;
- using, by the device, change point analysis to detect a change in the performance data for the first predictive model;
- and
- causing, by the device, a switchover from the first predictive model to a default predictive model, in response to detecting the change in the performance data using change point analysis.
An apparatus, comprising:
- one or more network interfaces to communicate with a network;
- a processor coupled to the one or more network interfaces and configured to execute a process;
- and
- a memory configured to store the process executable by the processor, the process when executed operable to:
- monitor performance data for a first predictive model, wherein the first predictive model is used to make proactive decisions in the network;
- maintain a supervisory model based on the monitored performance data for the first predictive model;
- identify a time period during which the supervisory model predicts that the first predictive model will perform poorly;
- based on the identified time period, schedule a switchover from the first predictive model to a second predictive model at a point in time associated with the time period;
- use change point analysis to detect a change in the performance data for the first predictive model;
- and
- cause a switchover from the first predictive model to a default predictive model, in response to detecting the change in the performance data using change point analysis.
A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor on a device in a computer network operable to:
- monitor performance data for a first predictive model, wherein the first predictive model is used to make proactive decisions in the network;
- maintain a supervisory model based on the monitored performance data for the first predictive model;
- identify a time period during which the supervisory model predicts that the first predictive model will perform poorly at a future time;
- based on the identified time period, schedule a switchover from the first predictive model to a second predictive model at a point in time associated with the time period;
- use change point analysis to detect a change in the performance data for the first predictive model;
- and
- cause a switchover from the first predictive model to a default predictive model, in response to detecting the change in the performance data using change point analysis.
Dependencies are inferred from the claim text.