 Let's get started to stay here. Johnny, if you can hear me, please invite Marty to that Google chat that I sent you. So one of the things that we've noticed, one of the reasons we embarked on this effort that we're doing called Validated Patterns, see the cool t-shirt here, is that we wanted to try to take patterns that were developed with customers that were successful and try to take the information that we learned from them and automate it and say, this is a good pattern. It worked. So rather than having a lot of different people in the field, whether they're solution architects or consultants or the customers themselves, trying to reinvent very complex architectures. And we're going to see this in a moment when you go into your lab. In the chat, I put a link. And Billy, if you're on, I'm not sure if you're on, Billy, but if you are on, we might walk people through that link. Let me just show you here. If you go to this link, what's going to happen, you can still see my screen, correct? Lucas, can you still see my screen? You can see a Request Lab GUID, is that correct? Yeah, we can. So I think you need to click on that link. And I do need the activation key, is that correct? I need the activation key for this. Did I get the activation key for this? Let me just check. I got the activation key. So I am going to pop that into the chat as well. So that's the activation key. And when you use that activation key and press Submit, you're going to get a screen that will allow you to claim an entire cluster of OpenShift with the application already up and running on it. So what you're going to get, and I'll show you, you're going to get something that looks like this. When you do the claim. So let's go back a little bit first and talk. As I said, what we're wanting to do is codify this stuff. So let's talk about edge computing. It's very complex. We want to try to make faster decision making on the edge. We want to try to enhance user experiences, whether it's with mobile devices or whether it's at the factory with tablets or at the retail store, et cetera. We want to do remote operations. So we want to be able to fix anomalies in factories kind of automated if we can. We want to have data compliance. So we want to make sure that across those vital networks between the data center and the edge that we're doing everything in a safe manner. So trying to do all of these things together can be hard. When we look at what's going on in the what it looks like, there's different tiers dependent on the industry. So you can have all sorts of different machines and machinery in the middle here, including some of the network providers as well, right? Which you as a practitioner, someone who's building out your edge solution, you may not care about too much because you're just relying on their network. For the example we're going to be looking at today, we're going to be looking at the manufacturing edge, right? And by the way, can you see my mouse when I do this? Yes. OK, good. So what we're going to be looking at today is we're going to be looking at a cloud data center. Often there will be a regional data center, but we're not going to worry about that too much here. And then we're going to have this factory, our planned data center. And it's going to be talking to things like in a factory what they call a line server. So there might be a server that's looking at a specific factory line, right, an output line, a production line. And that production line server is monitoring different devices and seeing, of course, those devices may be running on very proprietary machines, but they may be sending data over specific protocols like MQTT is a kind of popular protocol there. So what they're trying to do is move sensor data and advance and all that information to the planned data center for some local automation. But some of that gets sent all the way up to the data center so that we can do things like machine learning and send that updated machine learning models back out to the planned data center, update the systems in an automated fashion, and so that we can detect better anomaly detection, et cetera. So the example we're looking at today from a high level scenario is, again, we have those line sensors down here that are talking MQTT. They're going to be talking to an AMQ broker. That AMQ broker is getting converted, the message is both getting converted using camelK and sent via Kafka. So it was a Kafka taking the information from here and sending that stream up to the data center. The data center is then updating the model or whatever in the machine learning model. We're not going to care too much about this effort here today. It's going to be running. Well, actually ironically, it may not be running. So we'll see. And what we're really going to be looking at today is we're going to, at the data center, make a change to turn the simulators, the sensors here, to tell them, hey, we want to start checking out temperatures. You're now, you're already looking, you're only looking at, you're only looking at, let's see if the data is there yet, it's not. You're only looking at, let's see if I go, you can see this, right? It's still not there. Okay. So we are going to try to update the model and send that down to the sensors and then those sensors will turn around and start, we'll start taking data from on the temperature information. Okay. So I want to understand first, can people in the messaging here, have people been able to go and claim a cluster? Oh, and this is in. You have 32 people on. So I remember folks, if you're not in, you should be able to, so when you say you're in, you're on the welcome screen, if I'm correct, or are you also, have you already logged into the OpenShift server? I'm going to walk people through that in a moment. So you're on the workshop page, that's fine, stay there, that's really, really good. Okay. Are you still on? So, and by the way, Marty, if you could mute, that would be great. Okay. So again, this is the sort of information, a very high level when you consider it. If you're not me, press mute for the moment, thanks. And the data center here, is it generally got all the operational stuff, like GitOps, cluster management, a lot of different application services that are often distributed in different namespaces for different applications. You essentially the machine learning part, and then we have this essentially a test environment for the edge application, right? So rather than I'm going to show you in a moment an edge cluster working, I'm going to show you that in a moment. But we're going to actually be doing the change here, and this part is the simulated edge piece. So this is running in a single cluster, and hopefully we get that working. But the real magic of this, by the way, is that when you look at the different components here, let me, if I do share slideshow here, will it, or if I do the slideshow, Lucas, will it, can you see the full screen or does it not do that? Yes, I can see the full screen. Oh, perfect. Okay, so you can see there's a whole lot of stuff working here. There's going to be a data lake, there's advanced cluster management, there's the GitOps controller, there's image registries, there's a source core management system, there's data science, which is, you know, Jupyter and the open data hub, there's the DevOps pipelines, which is Tecton. So the GitOps is Argo CD, we have distributed streaming, Kafka, we have the machine learning and CI CD component. Then out of the edge, we have, again, a whole bunch of stuff, MQTT integration using CamelK, we have a AMQ broker, we have the container control plan, which is essentially the open shift control plan, anomaly detection application, shared storage, we have the local edge GitOps so that when we tell, when the data center automates the starting of a factory, it's going to kick off all these applications and I'm going to show you that in a moment. And then you have all these other pieces, it's super complex. So for someone to have to turn around and go out to a factory and try to do all this and do it consistently is not really on. And what we would love to be able to do is to automate that and make sure every factory comes up consistently. But beyond that, we want to be able to make changes like we talked about earlier where we turn around and say, hey, we want to turn on the temperature sensors on this new device or whatever. And so we would go into the configuration, we would make the change, we would check it out in our little test environment here and we would say, this is great. And then we would basically accept the merge commit and it would roll out to the factories, right? Does everybody follow me there? Let's see what we have so far. This is very good, very good. Everybody's on the getting started page. Okay, so before you do that though, I am going to do something really cool. I am going to do a cat of all of the slush. You can't see this because I didn't share my entire screen because someone just told me this morning when I share my entire screen, it's one of those wide screens and therefore it doesn't work so well. So here is a factory server I've got outside of our lab environment, okay? And as you can see, all of these operators, sorry, let me show you all of the operators, all projects, all of these operators are started. Did I do this by hand? No, I did not. I essentially took a pattern that we have from our validated patterns website, which I'll tell you more about later. And I did a, once I had a cluster up and running an OpenShift cluster no matter where it is in the world, right? And you start when yourself and you do a make install, that's it literally in the home directory and it will actually install the entire solution, right? And the way it does it is it starts Argo CD and it tells Argo CD, here's all the helm charts for everything I need to go and get running. And Argo CD, which is OpenShift GitOps, will then turn around and start deploying everything. And one of the things that deploys is this Advanced Cluster Management piece. I'm gonna go over to Advanced Cluster Management right now and I'm going to say import a cluster, okay? And the cluster I am going to, I'm gonna give it a name called IP babble dash factory. I'm gonna say the site equals factory here, it's a label. So what this is gonna do is it's going to tell Argo CD that hey, this is a, it's gonna tell ACM, this is a factory, it's gonna turn around and tell Argo CD, oh, by the way, this is a factory. And when it does that, Argo CD will then turn around and what we're doing here is we're passing the cube config information, we're gonna import that. And I am going to go over here to my OpenShift console on the factory side. And you're all still following me along. Lucas, you can be the voice of the class there as you look in the chat and see anything or. Well, there is no response yet or any question. Okay, but I can follow it. If you look here, the only installed operator on my factory right now, this is a blank OpenShift cluster, right? There is nothing running on this except this packet server. And all I have done as you saw is on advanced cluster management, I have essentially imported that factory cluster. And right now it's not recognizing it, or sorry, it's not up and going yet. It takes about three minutes for this thing to kick in. So in the interest of time, actually, you can, Johnny, if you can, or someone can tie me, so I come back to that in a moment. But we're gonna see automatically everything get installed for the factory here because ACM will turn around and register this particular factory. This particular factory, it'll know that it's a site factory, you see down there the label. And when it'll start all those things, but we're gonna go back here because you guys are now looking at this page. Let me just slide show that again. The things you care about on this page are this URL, the OpenShift console URL, so copy and paste that into your browser somewhere. You're gonna get some, you know, the usual certificate, you know, questions, are you sure you wanna go here and it's safe and all that carry on? Accept it through and then make sure that you log in in your environment with the admin and the password that's here for your cluster administration, okay? The other page you're going to want to open is this, this down here, this industrial edge GitOps registry one, see that? GitEA apps, we're not using GitHub, we're using, well, kinda using GitHub, but this is a special Git for the lab and the password for that will be lab user and password OpenShift. So if you can, please log in to both the OpenShift cluster that's been set up for you with the password, you know, with admin password and the industrial edge repo here with the lab user and password. And from there, we should be able to do everything else that we need to do. I want you to let me know when you've, we've done that. Thank you for doing that, Billy. And by the way, if people could turn around and do me a favor and say, when you're logged in to your OpenShift cluster, just type yes in the chat and also type what city and country you're in. I'd love to know how many people and where you are of these 32 folks who's, who's on and trying to, going to try to do this lab if it's working because we've noticed this morning that Kafka might've gotten updated overnight and the irony of that is that is the exact example I've been using for the last couple of years to say, hey, what happens if you're, if you do an install of Kafka and it breaks things and we should be doing QE on that and we are not apparently. So, oh, very, very good. Anish is in Ghana. Thank you, Anish, you're in. Excellent. Anybody else in? Thank you, Anish, for following along. Oh, let's go back to our cluster and see what's happening. Oh, boom, look at that. So I didn't do that. Literally all this, you can see it's all, everything is starting or getting ready and going. So I literally just registered this. You see it says seven nodes are up with the factory. So you can see I have a factory. There's my local cluster. My factory is up and ready. Site factory and because it was a site factory, we are now seeing it install all these wonderful pieces. If you go into the Argo CD, by the way, and we're going to see if we can do that in a moment, that's part of our example, Anish, you're in. So we're going to look at a couple of things here, right? Specifically, we want you to go in your OpenShift console. In your OpenShift console, I want you to change the project. Let me show you how you do that if you don't know it already. So where is my cluster here? I think it's this one. Yeah, here's my cluster. So if you go in here and you go to test all and go to industrial edge data center and go to, so industrial edge data center is the project and then go to the networking routes and you'll see a data center getups server, okay? And I want you to click on that and it's going to ask you to log in. And that again is where you will see the Argo CD log in for that is here for the data center. Let me show you that on big screen. Do you see Argo CD admin password for the data center? Here, so use that. So it's admin and that password will get you to this screen here, sorry, this screen here. And now you see I have a problem here where my central Kafka is down. And that is actually a big deal for this demo. And I'll tell you why in a moment. So I'm going to go back to here in the lab. Ah, Anish works on Open Data Hub, excellent. We have Robert in Poland, thank you. Okay, this is good. Thank you for following along. Okay, so here's the issue that we're having this morning. As far as I can tell, when we look at this Kafka, when we go into the central Kafka and go down, we see that there's some issues here with the cluster and it says degraded. Johnny, have we discovered anything here that we can do within this because the irony is hilarious? Can't, because what we want to do is go to Manila, test all workspace, you see this one here, the projects Manila, test all. Oh, it's there on my one, it's there. Let's see if it's working, please. Let's see if it's working. Oh no, it's there of course. Oh, it is there. Okay, if your one is there too, that would be fantastic news. This is my lab, it is, great. Okay, as you can see in this example though, we do not have, we're checking for a vibration on both the pump one and pump two vibration, right? Does everybody see that? Let's say yes. Yay, this is fantastic news. Okay, excellent. So what we're going to do now is if you haven't logged into the lab already, let's see, if you haven't logged into the lab already, I want you to make sure you log in to the industrial edge GitOps repository, right? That was the link that you got with industrial edge on it for Gitya. And the password I mentioned back here, right? So it's lab user and OpenShift. So if you're in there, then I want you to, we're going to do a few things. We're going to select a file. And now you can select one or both of the machine sensor config maps in this particular sub-directory. So you're going to go to charts, data center, Manila test, templates, machine sensor, machine sensor one config map or machine sensor two config maps or if you're really fast and good at this, you can go to machine one and make the change and go to machine two and make the change. And you're going to look in that file, you're going to make sure you edit the file using the little pen mark over here. We're not going to do this where you're going to make a clone onto your laptop and make the change and do a Git push. We're just going to do it directly in the URL here because we don't have time. We are doing a very ambitious thing today in a very, very short amount of time. So I want you to change the sensor temperature and enable value to true from false. And then I want you, you can add a comment if you want and I want you to commit the changes. Okay, I'm going to leave the screen up for a minute until people do that. In fact, I'm going to do it myself here first. Okay, so charts, data center, Manila test, templates, machine sensor, and I'm actually going to do both of them. Okay, so I'm going to edit mine here. Did that edit? Oh, I need to sign in William, William, William, lab user, OpenShift. And I'm going to, actually I'll save that because it seems it's the thing to do here. So now I'm going to say true and add temperature, add sensor temperature, commit the change. And then I'm going to go back over, oh, William, what do you do? Charts, data center, William, William, William, come on. I'm going to go and have a look at my applications and you should do this too. So make sure you go do your Argo CD and look under Manila test, which is the namespace Manila test all. And you'll actually see that, I'm going to zoom out. You'll see that these machine sensor information, they'll actually synchronize, actually we're going to manually synchronize. So because you have to wait several minutes, so the Argo CD tests every so many minutes. So I'm just going to do a manual synchronization. And in this case, what we'll see is, we should see the sensors starting to update here in a moment. Yep, let's see the two machine sensors are updating. Okay, so that's what's happening here. We're going to see the machine sensors. And then we're going to go, you guys, oh, if you hadn't done it already and I bet you hadn't, darn it, I forgot to tell you to make sure you go over to the Manila, sorry, to the edge, yeah, Manila test all and make sure you click on that line dashboard. Cause what's going to happen here in your line dashboard is you're going to start seeing the seatbelt. There goes the temperature, it gets turned on. So it went from just the vibration to the temperature. And both of them are on now and working. How many people got that working? Anish, Robert, did you get it done? Let me go back over the slides again. We're going to log in. Well, first of all, we're going to make sure that line dashboard was up as well, log in, edit, make the change and commit the changes. Then go back, you can look at Argo CD and you'll see those little spinny things here and then check the dashboard. If you only changed one, you'll just see one temperature thing come up. If you changed both, then you're going to see something similar to this one here where the two temperatures are up. Okay, so now I want to go back a little bit and talk about what actually happened here. So when we look at Industrial Edge, what we see is there's a make file here and this make file is where the make install happens. And what make install does is it uses a number of other files like the values global and the values data center. So let's have a look at quickly values global and in values global, you'll see some information here about Git and image registries and the data center cluster name, which should correspond to what you're looking at up there. So all of this stuff is really good. That's nice. So let's go back to here and then we go and look at the data center. Let's look at global and global has some information about the Git repos and the image registries as well and there's a factory values one, two and it has some other information like, hey, for the factory, these are the namespaces we care about and here's some subscriptions you're going to need for it, right? And the applications. So there's a data lake, there's a storm shift, there's an ODH. So if we went and looked at the Argos CD screen for the factory, I would see a different one, I would see different applications than the ones that are running in my server, okay? So the other information that we need and it's a bit tricky is we have the secrets file and the secret file is something that you copy to your home directory and never push it to Git and you're going to need some secrets for this like the image registry and the Git, your Git token all encoded in base 64 and in this case, we're using AWS as the underlying infrastructure. So for the S3 information, so you need to put that in there too. We have tested this by the way on, we have tested this on GCP, GCK, sorry, GCP and on Azure. So the idea is no matter where you put your OpenShift cluster, if you do your make install, it should just make install like no problem, right? Now the other thing that we've done is in a different pattern. So this is the industrial edge pattern. We have also done a medical diagonal, no, sorry, the multi-cloud GitOps pattern. It uses vault to manage the secrets for us. So it's something that we might add to industrial edge later on, but it's something we're doing there too. So the idea then is that just really quickly, I just want to explain some stuff here. What happens for us is, let me share the slide, on the normally you're not doing this today because you're not doing the make install, everything was installed for you in the lab, right? So normally what would happen is on your laptop, you would turn around and make a fork, first of all on Git, you'd make a fork of the validated pattern for your organization or whatever. You would clone it on your laptop, you'd make your secrets changes, and then you would do your make install, which would deploy it. And what that happens is on the server side, it runs OpenShift GitOps, Argo CD, very first OpenShift GitOps then starts running, bringing up the different applications. Remember, these are the pieces that we're talking about here, right? These different applications. Oh, look at this, Central Kafka's getting processed and up and sink. Okay, so all of these different applications which have a lot of different things running it. I mean, look at Manila test, for example. There's a lot of different steps here that happen, right? If we look at ACM, again, there's a bunch of different pieces that have to happen for ACM. If we look at the pipelines, again, there's a bunch of different processes that have to happen for the pipelines. So there's a lot of work going on for us when we do this automation, right? And it brings it up, and one of the things it also brings up is advanced cluster management on the server, it's just easier for us to run it that way. And then on the edge side, what happens is when we register, when we do that, hey, join our import, and you can do this on the command line as well, and I will give you instructions on where to go to find that. But what it does is it runs ACM on the edge cloud, it runs an agent there, not a full advanced cluster management, just an agent. And the agent then turns around and goes, great, what are my subscriptions? And it says, well, really, here's your GitOps piece. And GitOps goes, oh, great, and this is a factory, great. So now I'm gonna go and get all the subscriptions I know about for the factory from that values factory file. And it goes great. And now we'll run through all of these wonderful Helm charts that we have for either the data center or the factory. So let's look at the factory. And we can see that we have the data lake and the storm shift and there's templates in here that will run like let's say, look at the line dashboard. And if we look at the config map for that, we'll see a number of things. We'll see both the name of the config map, the namespace it's in, but look at all this, there's a lot of information here that's parameterized, right? So we're using Helm templates to help us generate this. And that's why back here information and things like your values global where you specify your data center and things like your cluster name and your domain, that stuff all gets injected into the Helm charts to bring it all up like magic. So this guy back here and you're installed operators for this. This is my, sorry. This is my factory. If I go over here and I look at my line dashboard and go to routes, networking routes. This is not the line dashboard. We were looking at in the factory. This is actually the line dashboard that's running on the factory. So you could actually look at each individual factories line dash IoT line dashboard. Gonna go back here and see how are people doing on this so far? Everybody happy? Good, we don't have much time left. So I'm going to go back here and just show you, well, where can you find, or what more could you do with this particular industrial edge pattern? There's actually a lot you can do with it. You can, what we talked about today is the configuration with GitOps, right? You can actually do application changes using DevOps too, right? So obviously we can go in. This one is you'd be better off doing on your laptop and all that good stuff with your clone, but you would turn around and make a change to code and push it back. It takes a little bit longer to run. So to bring that change back up and roll it all the way out to the factory. So it wouldn't be enough time today to do that because it takes actually about 15 minutes or so for the DevOps thing to roll out. And as you can imagine, there's a lot going on there to do that. You have to build the pipelines, push things up to registries. You get pulled down into the test environment. The test environment runs it on all that good stuff. And then you have to accept it and merge it to roll it out to the factory. Now all that information is in that link here and I'll give you that information later. I'll probably send out these slides with the links in them for you as soon as we get done here. And then you can do actually make changes to the AI ML model as well. So you can change the model for machine learning and add some stuff there. And there's a full guide on how to do that on this page. And there's, you can turn on and off event streaming between the edge of the data center as well. Where can you find more information? So there's documentation link here on the slide. So it's hybridcloudpatterns.io. And that page there is up there under the industrial edge application. And then there's the actual GitHub environment is on GitHub, hybridcloudpatterns. Oh, look at the S trailing there. That was, I should have a catch. And of course you can contact the team. Sorry, Marty. And I'm sure our friend Andrew here will be checking this later and he'll see this wonderful picture unless there's actually more to the team. But I thought I'd make fun of these three people as well. There's also Johnny who's on the call with us and McKella and there's our QE person, Mark who's on the team. So these folks are the ones who are both building out these patterns and automating them and testing them. But also we're trying to do QE on them. So that's Andrew there with Hasselhoff and he's gonna love that picture. So I wanna thank you very much for your time. I wanna see if people got the lab done. I'm gonna stop sharing I think right now. If anybody would like to ask questions, please unmute or ask request audio feedback. I am, this is amazing that'll work. When we got on this morning, some of the stuff wasn't there. Hopefully it worked for you guys too. Marty says there's no such thing as bad publicity. Not absolutely true Marty. So Anish do you wanna turn on your microphone for a moment? If you can and tell us how you did. If you didn't get it working and you're upset then don't bother. No, I'm joking. Tell us what happened. Tell us what broke. Oh, you couldn't log on to Argo CD. Did you look, sorry, I'm looking at Peter. Peter when you were looking at the, you found it now, right? Yeah, there's a special admin password. I've had that problem before, especially when you're moving things and you're trying to listen to someone you sometimes forget and you pick up the wrong password. So I'm glad you found it and you got in. Now there's progress. I'll share my screen again with you and I'll just bring this up again so that you can see. Oh, somebody's talking. This is great. Yeah, that's me Peter. I made a change in the, in the git using gitty and then I watched the Argo CD and I didn't see any progress. Can you press sync on Argo? Yeah, eventually I pressed sync but even then nothing happened but like, I don't know, five minutes later I can see the Argo CD is progressing. Oh, okay, good. And have you checked the line server? Do you see temperature coming up? So remember, go to routes. Test all and you see that? I was, I was a bit lost. So I disabled the vibration. So I thought that. Okay, can you go to? It should disappear but I see it. Okay, so do you see more than two? Do you see three or four? Did you make a change to both files or just one file? Ah, just one file. So now I see only one, one graph. One, one graph or three graphs? Just a single one. Oh, did you, did you change the vibration or did you change the temperature? I disabled the vibration sensor. Ah, yeah, okay. So if you can go, if you want, you can go back in and make a change, turn that back on and then go down to look at the temperature for that instead of the vibration. So go in and do the temperature piece, templates, let me see, okay, machine sensor and I'll show you a machine sensor two, for example. So if you turn around and turn the temperature onto true, then you will see one of the temperature things come up, okay? So feel free to do that. Yeah, so, Anisha, I ended up with three graphs. So, okay, good. Have you any, I mean, I hope you guys understand as simple as this lab is in terms of the amount of things we did, which was really only minute, right? The complexity of this particular architecture and to be able to do a make install and bring up an entire data center and then essentially import another cluster and say it's a factory and have it bring up the entire factory is pretty cool stuff and it really is a credit to the team who worked on this. And then as I said, this particular pattern enables both GitOps, DevOps, machine learning changes and all that good stuff. Now the idea would be that a client of yours or a customer would be able to turn around and say, okay, well, this isn't exactly what I need but it's got most of it what I need, right? I might have to change things around my line servers because I'm looking at different stuff or maybe I'm actually, maybe I'm not a factory, maybe I'm a, maybe I'm monitoring cameras somewhere, right? And I've got a, I've got a whole bunch of stores somewhere and I wanna check on anomaly detection for, I don't know, shoplifters or I'm doing anomaly detection around if there's someone in the, around the building after 10 p.m. at night, that's an anomaly and I need to flag a system and tell the security people that there's a, someone on the premises that shouldn't be there. So there's different anomaly detection you could be doing that could be based on this model. So you take it, make the changes you need, change the model in the way you need it and then deploy that and that becomes your anomaly detection pattern for your example, right? Does, does that help? Like we're also seeing this within smart cities as well. There is one question in the QA section. Should I read it? Yeah. It's from Anish. Is there a cataclysm scenario or something similar? There's not, there's not yet, Anish. What we're doing is, as I said, let me share my screen again with you. Keep doing this. So what we're doing, Anish, is we're, this is super complex. The catacoda thing, we have these labs that people are able to do, particularly if you're an internal at Red Hat. But we haven't got a catacoda up. But what we do have, oh man, I did it again. Let me fix this. I keep doing this wrong. So workshop, share, entire screen share, single window, workshop. Okay, I'm back. Here's the Hybrid Cloud Patterns website. So look hybridcloudpatterns.io, okay? And the patterns we have so far that we've done is the industrial edge one, the multi-cloud GitOps one, and the meta. So this is like, if you've got like four or five different data centers and they're running on, potentially you could have a data center running on premises, and then you have some data centers that are using the cloud. You could use this multi-cloud GitOps example, right? There's the industrial edge one. The industrial edge has a landing page here that gives you the background, tells you all of the different technologies that are running in it, and then it also has a get it started. Now, this getting started will tell you how to do essentially bring up an environment for yourself. And once you have a OpenShift cluster, you can step through this. I've tried to make this information as explicit as possible. So I'm trying not to make any assumptions about your knowledge and stuff. If you notice any problems with this, please file a bug up on the GitHub open hybrid cloud patterns. Sorry, open hybrid, sorry, the hybrid cloud patterns slash docs repo, D-O-C-K-S, I'll show you that in a moment. And you can file a bug and I'll fix it. But it basically tells you how to walk through this and then it has some next steps. For example, deploy the factory pieces. So here's how to deploy the factory pieces. It tells you all about how to do what I was doing today with advanced cluster management and make the changes. And it also shows you how to do it with a tool called CM, but that's a little bit complex. And so is cluster admin. I prefer to just use the console to do it. And then it has that application demo section which lets you walk through the, after I've gotten everything up and running, what do I do next, right? There's a troubleshooting guide and cluster sizing. And we also have the medical diagnosis one, which is about, this one's pretty cool. It's about checking anomalies within X-rays for pneumonia. And so there's information there on that one. And it's pretty sweet. And again, there's a getting started. This is much more data centric at the moment. We have to add the edge piece later where the actual application, just like in the factory, the application at a remote medical facility, is providing alerts to doctors to say, hey, we think this person has pneumonia. You should check this. And if the doctor says, oh, actually, no, that's not pneumonia. But thanks for telling me. Any flags that is not pneumonia, that would get sent back up to the data center for the machine learning people to go, oh, well, this was flagged, but it wasn't. So what do we need to do here to change the rules? So there's a medical diagnosis one as well. Right now it's pneumonia, but it could be. You can be looking at MRIs or other things as well. So there's the patterns. We also have information on how to create a new pattern or we're starting that information right now. I need to finish that up at the moment. Any other questions? Has this been useful? Could we have a vote? Lukas? Yes, definitely. And sorry for interrupting you, but in a few minutes, there will be another session. Thank you for a great presentation because I think this will be very useful for production, especially in different areas. So really interesting. Thank you. And I think that those labs will be, if you've been on a lab, it should be available for the next, a couple of hours. I'm not sure how long it'll stay there, but if you wanna play around with the lab, by all means do. Thank you so much, everyone. Much appreciated.