[Music] thank you hi everyone welcome to the now platform Utah release features I'm Emily Walker one of our I.T operations management solution Consultants here at servicenow today I'm going to be talking through one of the new features within itom for service mapping called Automated machine learning service suggestions or application service candidates as you'll also hear it referred to I'll be showcasing a quick demonstration of this new feature within my demo instance so with that let's jump into it as always here's our Safe Harbor notice just letting you know that this presentation may contain four looking statements that reflect the current beliefs of servicenow and are based on current information available however these forward-looking statements should not be relied upon in making purchasing decisions so let's dive into Automated machine learning service mapping suggestions if you are new to service mapping I highly recommend checking out our other videos here on the service value YouTube channel that talk through other mapping methods that help to really build up to these automated service suggestions these videos focus on service mapping from a top-down approach using patterns and traffic Intelligent Traffic based connections that use machine learning and tag-based service Maps let's talk for just a minute about how intelligent traffic-based connections this method of machine learning based service mapping enables you to build on service maps with connection suggestions however since these connection suggestions are additional nodes to an existing service you would still really need to have a good understanding of your infrastructure and services as you create Services manually with entry points but if you don't have a good understanding of the overall infrastructure or maybe the specific services that can still really be challenging and sometimes leave you feeling like where do I even begin with service mapping and this is where automated service suggestions come in it's taking machine learning based service mapping really to the next level by providing application service candidates as suggestions where then you and your teams can review by looking at a map preview and starting to create services with auto added entry points for top-down Discovery to start building service Maps which you can keep enriching with that machine learning based connection suggestions these previous service mapping methods are very powerful but they can be time consuming and labor intensive to actually execute and you need to show value in days and weeks and not just months and years with automated server suggestions we're doing just that so by leveraging the TCP traffic analyzed by Deep blurring machine algorithms customers can easily find common Services quickly and immediately see the value for service operations so let's see this in action here we have our application service Readiness dashboard and you can find this service mapping workspace within our new workspaces here on the application service Readiness dashboard we have a few different areas that we're able to check out as we're getting ready and prepared to start using application and service candidates so a few things kind of starting from left to right mapping status of an application service machine learning related issues in mapped application services this is what we're looking at here the next one down we have application fingerprint training status so this is really a report that shows the status of applications fingerprint training predictive intelligence trains these predictive models and machine learning Solutions and here we can view the training status for these application fingerprints to really understand if your deployment is ready for mapping using this predictive intelligence the next part up here we have our prerequisite status application service mapping requires integration of several several modules and applications credentials predictive intelligence enhanced application dependency mapping and sketch of jobs and here this gives us a list to review all of the prerequisites and ensure that the state of all of these prerequisites is set to ready and Below we have discovered connection suggestions which gives us a report of the discovered connection suggestions and really in order for machine learning based service mapping to function there are really two key prerequisites good discovered TCP data and good discovered process data so let's move over to our service candidates so we can see this in action here we're looking at our application service candidate dashboard and we'll talk through a few of these filters and uh the fields that we have at the top here so we'll start with number number is what helps us really uniquely identify each application service candidate suggestion AFP is our application fingerprint based suggestions and here this is how we're getting that name of this particular application service candidate that we have so we identify the Fingerprints of the running processes that are part of this candidate and then we show the max three fingerprint as its name and this really helps you to decide maybe is this candidate worth considering or not we've also set some filter conditions on here so by default these are added to help us eliminate specific candidates that fall Within These filters so resources less than four with resources more than 100. um we want to filter out anything below or above that we also want to look at anything any endpoint already in use for some service and we want to look we want to filter out recalculate requests if it's empty so let's take a look at one of these services and you can hover over it and click on that box and we will go into preview map here and with the preview map option this helps us to make a decision a little bit further as to whether we want to actually create a service or not so by clicking on the map it opens this particular candidate and here we can see what all processes are part of this candidate Communications that are happening between those processes and what are the effective entry points that are part of this candidate so hovering over it gives us a little bit more insight and again this information just helps us to decide is this candidate worth considering converting to an application service or not so when we actually go back here we can click on create application service candidate and what you get again is kind of that preview to preview the map so that you can review it or you can go ahead and start to provide some information if you were going to actually create that service so maybe we want to give this specific application service a name we want to enter a description and we want to do this so that everyone else who's going to be looking at this map can see exactly what it is we also want to set a service owner so that you can work with this other service owner to make sure that this map is accurate and correct so once we get all of this information filled in we can actually click on create service and it will create a record in the application service table within an entry point and it will go ahead and start top-down Discovery to really help you build out this map I hope that this was helpful and thank you for your time today