Device health assessment data summarization using machine learning

An apparatus, comprising:

• one or more network interfaces to communicate with a network;
• a processor coupled to the network interfaces and configured to execute one or more processes;
• and
• a memory configured to store a process executable by the processor, the process when executed configured to:
• extract device health status indicators from health assessment data that the apparatus uses to determine a device health status of a networking device;
• form, using the extracted device health status indicators, a health status signature for a particular device health status by labeling each of the extracted device health status indicators with a corresponding health status;
• train, based in part on the health status signature, a machine learning-based model to classify whether the networking device has the particular device health status, wherein training data for the machine learning-based model is generated synthetically by converting logic inside the health status signature into a truth table representation of the logic;
• deploy the machine learning-based model to a target network for local device health assessment of one or more networking devices in the target network;
• generating, by the device health assessment service, a command line output parser configured to form a device signature from one or more command line outputs of the networking device;
• and
• deploying, by the device health assessment service, the command line output parser to the target network, wherein the machine learning-based model takes the device signature from the command line output parser as input to classify whether a networking device with the device signature has the particular device health status.

A tangible, non-transitory, computer-readable medium storing program instructions that cause a device health assessment service to execute a process comprising:

• extracting, by the device health assessment service, device health status indicators from health assessment data that the service uses to determine a device health status of a networking device;
• forming, by the device health assessment service and using the extracted device health status indicators, a health status signature for a particular device health status by labeling each of the extracted device health status indicators with a corresponding health status;
• training, by the device health assessment service and based in part on the health status signature, a machine learning-based model to classify whether the networking device has the particular device health status, wherein training data for the machine learning-based model is generated synthetically by converting logic inside the health status signature into a truth table representation of the logic;
• deploying, by the device health assessment service, the machine learning-based model to a target network for local device health assessment of one or more networking devices in the target network;
• generating, by the device health assessment service, a command line output parser configured to form a device signature from one or more command line outputs of the networking device;
• and
• deploying, by the device health assessment service, the command line output parser to the target network, wherein the machine learning-based model takes the device signature from the command line output parser as input to classify whether a networking device with the device signature has the particular device health status.