Machine Learning Service Mapping

In 2021, ServiceNow introduced the ability to create service maps using machine learning algorithms. This approach enables users to monitor how machines are communicating using TCP ports and determine which processes relate to a particular enterprise service — without sifting through all the noise. Now, teams can leverage traffic fingerprinting and connection suggestions to create service maps faster. However, many enterprises do not have the internal resources to get up and running with this service quickly.

Maximize your ServiceNow machine learning service mapping investment

The problem

The ServiceNow ITOM modules are game changers for enterprises undergoing a DevOps transformation.

The ITOM modules offer unparalleled visibility and insights into your infrastructure. This means you can find (and fix) infrastructure issues faster. A crucial step in the process of deploying the ServiceNow ITOM modules is identifying the individual components of your IT infrastructure and using service mapping tools to determine how they relate to each other. For a larger enterprise, this process can be complex, and if completed manually, tedious.

The solution

RapDev helps companies who have invested in ServiceNow better harness machine learning to quickly gain complete visibility into your enterprise’s services and applications, mitigating risk and improving visibility.

RapDev enables its customers to implement ServiceNow ITOM Visibility, including service mapping using their machine learning algorithms, faster and more efficiently. Our team of 70+ experienced engineers and experts can get enterprise service mapping implementations off the ground far faster than most in-house teams. This saves hundreds if not thousands of engineering and/or sysadmin hours.

Machine Learning Service Mapping

Drive Enterprise Visibility & Mitigate Risk

Step 1

Build a service mapping strategy

We start by building each client a unique service mapping strategy to use in tandem with the ServiceNow machine learning connection suggestions. This ensures that service maps are complete and can be maintained after the implementation process. Again, this saves engineering DevOps teams massive amounts of time.

Service mapping workshops & hands-on implementations

Step 2

At RapDev, we’ve been handling service mapping since before ML algorithms were able to handle many aspects of the job. This means we’re experts in both tag-based and top-down service mapping. We use these strategies to augment the ML service mapping feature depending on each customer’s unique use cases. 

Moreover, RapDev’s engineering team has deep expertise in working with the major cloud platforms. This ensures you’ll have the help you need to understand cloud discovery and the relationship between components.

Whether you choose to attend a workshop to learn how to DIY or partner with RapDev on your implementation, you’ll gain in-depth guidance. This will help you identify and prioritize your key system processes using machine learning and achieve true end-to-end visibility. Ultimately, this puts you in the driver’s seat to enhance your enterprise service delivery.

Benefits of service mapping with RapDev

Step 3

After leveraging ML-based service mapping (especially if you choose to lean on RapDev’s implementation expertise), you’ll be able to better track the underlying technology stack for your business applications. This level of business service mapping enables you to identify and mitigate potential issues before they impact your application services and end users.

Through the service mapping process, you may discover that a business-critical application has a service dependency you were unaware of. Once you’re aware, you can ensure this particular service and its infrastructure components employ proper measures to mitigate the risk of downtime or degraded performance. Other benefits include: 

  • Optimized service maps help streamline operational workflows 
  • Service maps provide insights on how to scale your enterprise

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