ServiceNow Machine Learning Service Mapping

Our team of Engineers has helped multiple customers deploy Machine-Learning Service Mapping to help accelerate their mapping efforts in a more seamless and intelligent manner. The ServiceNow Quebec release introduced new functionality that allows users to create service maps via Machine Learning algorithms that monitor how machines are communicating using TCP ports.

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The ‘Predictive Intelligence’ Plugin leverages machine learning to analyze data and create process clusters that leverage traffic fingerprinting to provide a streamlined workflow to build service maps. One of the main benefits to ML service mapping is the ability to leverage discovery data from existing sessions to build relationships intuitively and efficiently.

RapDev’s team of ServiceNow engineers specialize in helping you simplify your mapping journey through thorough Discovery Workshops & Implementation efforts to identify and prioritize your key processes and generate the insights required to enhance your service delivery and achieve true end-to-end visibility.

How does Machine Learning based Service Mapping work?

System Running Processes are identified and related to servers through Discovery. These processes are then automatically identified and classified as application fingerprints which in turn create cataloging suggestions for administrators. The algorithm in the Quebec release also intelligently suppresses connections and back-up services to avoid adding potential noise to the new service maps. This becomes crucial for service mapping administrators to make informed decisions as to which connections can be added to application services and which CIs to retain or eliminate.

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