KodeKloud Cohorts Check-in #3: Kubestronaut & AWS AI Practitioner 2026

Key Takeaways

[music] Heat. Heat. Heat. [music] [music] [music] >> [music] [music] [music] [music] >> Heat. Heat. [music] [music] [music] Hey hey hey everyone welcome welcome to yet another session on the u uh cubern cohort check-in well AWS ai cohort check-ins now my name is mumshad and this is I'm with Michael fer and we'll be your host uh for today so um this is our B bye-weekly check-in for those who are uh new here. And um we go through and look at uh the progress in the cohorts and um you know we celebrate those who have recently completed the certifications and um you know we share updates about uh what's happening at core cloud and you know what you can expect in the future. So, lots of exciting things to discuss today and uh we'll get started. And if you're here uh wherever you're from, do let us know in the chat who you are, your name, as well as where you're from. And um if you have any questions throughout, please do drop them in the um in the chat sessions uh comments below. All right. So, yeah. Hey, Michael, welcome. Welcome to the session. >> Hi, >> good day. Yeah, looking forward to the session today. This is going to be fun. Yeah, you you seem to be in a at a different location today. >> Yes, I am definitely in a location that is violating all of the proper constraints around lighting. And so I'm getting full sun as we're kind of into this. [laughter] >> Yeah. So, um just so we all know like which where where are you located? Which which part of the world really? >> So, usually I'm in Atlanta, Georgia, and right now I'm in Austin, Texas, actually. So, >> Okay. >> Yeah. >> Yeah. >> Cool. Yeah. So, we have u couple of them um share their message in the chat. So, this is DD from Germany just passed a CK today with your help. Oh, congratulations. This just just right on time. Um and let us know what you're up uh against next. Do do keep us posted um in the comments. Um we have Ali Resza. Hey Ali, is that our Ali Resa? >> [laughter] >> created a few courses for us. Um, we have Lindseay from Atlanta, GA USA here. That's your place, right, Michael? >> Yeah, that's that's my hometown right there. >> Um, welcome, Lindsay. Also, it's worth mentioning that if it if it is Alisa, right, then he actually co-created the AI practitioner course with us. So, okay, it's great to have him on. Yeah. >> Cool. All right. Uh so yeah, let's uh let's dive in and today uh we we'll start with uh sharing updates about those who have recently completed the certifications. Uh so in the future if you if you'd like us to feature you, please do you know send me a message or anyone any of us a message on LinkedIn and um you know we'll be happy to feature you on these uh on these calls. Uh we'll also share a little bit about some of the upcoming uh course updates. we'll check in on the um the core progress as well as and Michael's going to share a little bit about AI powered learning uh DevOps learning paths and tools. Um so AI we've been getting a lot a lot of questions regarding how to u upskill uh in this era AI era how how do you use AI tools in your in your workflows and um you know where to get started and how to get started uh with the a learning journeys in AI. So if you have if you're new to all of that, if you if you have those questions, then do stay tuned because Michael is going to walk us through uh a bunch of material that is put together around um around that. >> Yeah. >> All right. So we have a bunch of Kubernetes rock stars. Uh we have Sea um SAI um Nishadam who is a DevOps and site reliability engineer. He's recently completed uh the CKA certification. So congratulations to Sichai. We have Pablo Salvador who's recently completed the CKD. So um congratulations to uh to Pablo. We have Taur Ali Yakub uh who's recently completed the CKA certification. Uh we h have Praiba [clears throat] who's recently become a cubstronaut completing all the five certifications. So congratulations to Praiba. Uh we have uh Stas who's uh recently also become a CNCF cube. uh completing all the five exams. Uh so congratulations um to Staz. We have uh Yan Borzanski who's recently completed the CKA certification. Um he's a DevOps engineer. So congratulations to Yan. We have Marcos Herrera who's uh also completed all the five certifications to become a cubestronaut. Congratulations to um Marcos. We have Larry Lie uh who is who's completed the is that the golden cubeot? Um >> yeah earned the golden cube badge. Awesome stuff. Yeah. So that's completing the 14 15 certifications. So congratulations. That's a um a huge one. And we have Vitali who's uh recently completed the CKA uh certification. So congratulations uh to to everyone. We also have a few other um those who have committed a few other certifications as well. So we have Adita who is uh a technical architect at Luminor and uh he is um on his way to achieve the golden cubstronaut cleared 12 of 13 uh 12 of 16 uh certifications overall and has recently completed the CNPA psyllium and kerno. So congratulations to Adita. We have Anthony Williams who recently completed the CNPA uh certification. He's a um he's a DevOps engineer and we have Kuri Romesh who's recently completed the certified GitHubs associate certification. So um uh congratulations. Uh we have Samit Singh um who's recently uh completed the STO uh certification. Uh so that's kind of a a tough one. So congratulations to Sumit [snorts] and um we have uh Yoga who's recently completed the site reliability uh he's a site reliability engineer at Trimble and he's recently completed the Microsoft Azure um uh administrator associate certification and we have Santo who's recently done the Argo CD um the GitHubs certified associate certification um on the way to Kishnat. So uh congratulations once again to everyone who's recently completed the searchs. Um an update on CubeCon. So yeah in a in a couple of weeks time we'll be at uh CubeCon uh Cloud Native Con Europe. So if you happen to be um in the area please do uh attend and and come by and meet us at our booth. So we have a booth. We also have a few talks that are lined up. So we have about three sessions uh that are lined up uh for uh CubeCon. So Michael and um I'll be delivering a session on how Kubernetes actually ships. This is at the cloud native university uh and that's happening on the 24th of March. And we have Abinav who's um who's our uh engineer and he's he'll be working he'll be delivering a talk on back and first IDP. So uh that's again happening at the cloud native uh theater on the 23rd of March. And we have Norhan who will be delivering a talk on AI on Kubernetes without the chaos. And um this is uh happening on the 24th of March. So yeah, if you happen to be there do do register for these sessions and and come and visit us. >> Yeah. What is that like uh two weeks less than two weeks away now? >> Yeah. Less than two weeks. >> Yeah. >> Really excited about this. >> You all set, Michael? >> I think so. a few more, you know, practice runs just to shake the nerves out and I think we're good to go. >> Yeah. Okay. >> Yeah. >> Awesome. Cool. Um, so the cohorts, uh, we we are at court five uh, of the of all the core. So we have successfully completed the two two cohorts and so if you happen to start the core five, join us in core five that's beginning of this year. Um, so you must be um going through the CKA right now and should be aiming to complete the CKA either by end of March or around April. And if you're a path two, then you should probably uh going through the um CK. Um and you can go to cohorts.co.com to um wait list for the future course that you'll be uh working on or uh if you're already in the cohorts to take check your progress um go in and check over there. And uh we also have I think there's a there's a code if you're uh if you'd like to purchase a discount from Linux Foundations. We are a partner and um you can get a 30% off. So using the code so um check that out. All right. So u yeah over to you Michael. Uh let's walk through uh what we have for today. >> Okay. Excellent. Yeah. So I think one of the things like a little just a very short backstory is that Mai if I remember correctly you and I have been talking about Genai since it first came out because you and I were both using Jasper and then in November of 2022 chatpt came out and you and I were like inactive about once a month maybe once every two weeks discussions just as side discussions about the impact of this over time. So I mentioned that because I think Mshad and I have been looking at this for years now and have been right in the middle of all the tooling and all the stuff around AI. And the reason I mentioned this and like why are we talking about this in a cohort that has Kubernetes and has like you know the certified AI practitioner like certification in it. We we always get questions while we're running these live streams about how do I you know upskill or what skills do I chase or does um codecloud have courses about AI and of course one of the certifications is the AI fundamentals course. So Mumshan and I were talking we thought you know what let's let's just talk about this for a second let's just take a little side step and talk about our perspective on where DevOps is headed from an AI agentic standpoint. So you're going to hear me use the word agentic. Um and what does code cloud have in that direction because we've invested heavily in this actually. So we've got a significant investment not just beyond the cloud you know uh the certifications. So I think we wanted to make sure that we that you were aware that not only are we chasing the CNCF and supporting all the certifications but also we are supporting the evolution of DevOps engineering from kind of I'll say the traditional space that it's been for the last 20 years 10 years to the AI space. So that's I just want to give context like that's why we're going to talk about it. So >> yeah agree. So yeah and I think I've been getting a lot of questions from everyone both internally and externally on um where things are headed and it's it's in everyone's minds and um you know is AI going to replace engineers or AI going to replace Kubernetes and you know these are the kind of the questions that I get very often and I think um it's important to kind of talk about that and and share our perspective uh on it and how you know anyone can kind of upskill and um get in on the on the on the trend and get themselves upskilled um in this era. So yeah, thanks a lot for putting this together and let's get going. >> Let's dive in. Yeah. Okay. So with that in mind, I think the traditional DevOps role I think we can easily say that the DevOps role is evolving. Um I mean kind of like cloud before it and then Kubernetes and then all of Kubernetes and CNCF and then platform engineering. there's been this kind of ac like accumulation of technologies and AI now sits at the top of all of that right now and so there's the traditional kind of like hey we're managing software we're doing pipelines you know automation was the biggest outcome and then in the last few years we've seen oh hey let's use cursor or let's use you know GitHub copilot or let's use claude and chat gbt let's let help it like let like write our terraform scripts and help it like assist us as we code and that's probably been the last few years and then probably middle of last year and definitely this year we're seeing this kind of oh can DevOps interact with agents who are doing the toil task like they're doing security they're running tests they're you know they're helping to kind of run incident management and check logs and look for anomalies and pieces like that and so there's now in 2026 there's now this push of like hey can some of these tasks be offloaded to general generative AI and let them run autonomously and independently with a very kind of independent and small scope like I'm just going to do security or I'm just going to check the logs on a regular basis or I'm going to check uptime that scope is going to get bigger but you're we're seeing kind of trajectory wise and and just to say this people we are notoriously horrible at predicting the future but we can kind of tell directionally where things are headed and definitely things are headed to where the LLMs are taking on more of the implementation the toil tasks that typically would be an engineer doing that. So you're seeing this like kind of we have what we've been doing then we what we've been doing for the last few years and kind of what's been happening for the last three to six months and is probably about to happen for the next 12 is that we're seeing more of that agent independent LLM kind of run. So our skills Oh, go ahead. >> Yeah, sorry. I think I was I was getting to the um same uh before you move on. So um if if you were to look at uh you know someone say I'm a DevOps engineer and I've been you know I probably started my career as a CIS admin started doing a lot of you know Linux and you know administration tasks and then eventually upscaled myself to learning uh everything about DevOps. So now I'm learning containers and learning infrastructurees code and all of that. So past couple of years maybe you know maybe maybe half a decade u I've spent uh learning these tools and understanding them. Um and when AI came around I think the first thing that people did were like you know as GBT to generate uh say uh code maybe Terraform code or anible code or write docker files and things like that. um got us to a point uh and then we switched into using ids and you know cursor and cloud code and everything to uh do that part for us you know create files for us um so so that part where you know we got tod write stuff or you know we got an editor write stuff that's basically the AI enhanced devops part but then now when we uh ask an agent say like cloud code to kind of dig into my my infrastructure you know find things for me and and fix them or recommend fixes for them and even fix them uh maybe with an approval process >> right >> so that's the agentic part that we're getting to right so just so I I understand that that whole flow right so if that is the case then I think what the worry for most people is like how is that going to impact um the skills um you know all the skills that I learned so I've learned spent like the last five years learning uh and simple terraform you know maybe shuff pep and um you know docker kubernetes what happens to all of those skills and is that is that relevant at all is that useful I think that's kind of what what what's everyone what's on everyone's minds >> yeah because it's interesting because imagine we have like just to use analogy we have self-driving cars and so you spend most of your time driving in a car that you don't control but when something goes wrong. You now have to take over and be an expert at driving even though you're not driving anymore regularly. >> Right. >> Yeah. >> So, it's interesting because I I think that's kind of where things are headed is that the skills that we're learning now might actually be more valuable than ever in the moment where the automation fails. But the problem is is that we won't be practicing them regularly. So, it's actually more important than ever that we try to maintain some level of sharpness because we when we do need them, it means something has gone catastrophically wrong. So that's my supposition, but there's probably more to that. So we'll look at the rest of this and see if we can't answer that question in more depth. So I think if [snorts] we're talking about traditional versus AI assisted DevOps versus agentic DevOps, we tend to think of it as either way is like how can we learn AI as a skill? How can we learn with AI? Like have AI assist us in how we're learning things and then how can we practice this in environments because we want to get hands-on because one thing that hasn't changed is, you know, when you go multimodal with learning where it's not just taking in videos and it's not just hearing the words and it's not just taking notes, but then you're actually crafting something and you're doing things and you're correcting outputs. that still remains the best way to learn is to go like not just take things in but also interact with it which is why you know the code cloud even exists right is that we we want people to get hands-on learning not just the expertise but also the experience as well so so we think of it as these three layers is like first let's learn AI as a skill so this is where for example you would learn and you can see the courses listed there but like prompt engineering this is where you would learn things like oh how do I how do I you know understand how open AI functions. This is where things like AI practitioner, which is the very cohort that we're in now, this is where that would make sense because you learn the fundamentals of what AI is and you learn the fundamentals of how to interact. You learn things like what is, you know, context engineering, what is prompt engineering, um what are what does temperature mean? What does uh when we say tokens or embeddings, like what are those words actually mean when you hear someone say them in relation to generative AI? you kind of get the foundations and then you learn you can get more advanced if you want to or you could even go to the next step. you don't necessarily have to go to intermediate or to advanced is that you might say okay what are the frameworks that are developers are using and infrastructure engineers so I want to make sure the infrastructure engineers that you feel like you're included in this right because it's not just developers who are using these frameworks infrastructure engineers are using these frameworks to create the agents that we were talking about earlier as that third evolution and and I want to make sure that you as your like coupronauts and golden coupronauts and cis admins you understand your importance. You cannot automate a workflow that you don't understand and most developers are don't understand operational workflows just because they're not operators and they're not actually doing the operations work. Right? So, you got to keep that in mind is that it actually does require an operator to create some of these agents because these workflows require an an operator to understand it. And so in this particular case, you probably learn like lang chain or a langraph or any of the AI assisted stuff or the frameworks that would help you craft either at least a simple agent or maybe even something more complex. And then the third tier advance is that you're into advanced frameworks. You might be creating machine learning, you know, models. You might be creating multi- aent frameworks. You might learn, you know, M MCP, which is basically a tool enabler for generative AI. Our learning path says let's learn these foundations so you can learn AI as a skill. And these are the three different levels that we look at. Now we also because we are learn by doing, right? We also want to make sure that there isn't just like a pure like let's learn AI kind of path that there's also a hands-on path. And I'm gonna throw a term out here and Mumshad and I have talked about this quite a bit actually because there's there's an industry definition for AI ops and then there's kind of the evolving evolution of AI ops. So when you see this word AI ops, you might think, oh, is this MLOps? Is this machine learning? Is this DevOps? Like there's a bunch of ops kind of prefixes that live in this space and it can get very confusing very fast. And so DevOps is just obviously the one where we're trying to like flow and you know create automation and we're trying to you know create CI/CD pipelines and you know trying to manage deployments and this and it's like that's the whole traditional DevOps is kind of like the traditional evolution of like we're going to automate this we're going to not create silos like it's the traditional definition that most of us now are pretty crisp on. MLOps is where that same DevOps team might be providing data and models to a machine learning or a data science team. MLOps is remarkably applicable in the age of generative AI, but it's not a term that's commonly used outside of like data scientists, machine learning specialists. You don't you don't see it a lot, but it's been co-opted. like it's been expanded to say, hey, if I was a DevOps guy and I had to provide data and models and other stuff to data scientists and developers and people who are going to use it, can I use MLOps to figure that out? And the answer is of course you could. Then there's AI ops. AI ops has actually been around since 2017 and originally it meant I have a third-party system like a data dog or a dino trace or a new relic or an app dynamics and I've added something in to that like system where it's it's AI like related for example let's say you're you are monitoring logs and you want to look for changes in the logs automatically no humans are doing this just you're looking for changes in the logs and you want those flags And then you want a human to be notified. That's traditionally what AI ops meant. And by the way, that's called anomaly detection. You're looking for you're detecting anomalies in a standard pattern. And that is one aspect of what AI ops used to be. Now AI ops is that plus what can we do to kind of like put into production to operationalize because we're all operators, right? So whether you're a DevOps or you're an MLOps, you're an AI ops, we're trying to put into operations some kind of piece of software, whether it's a stateful application, a data like stateless application, a database, or a machine learning model like a generative AI model. We're trying to operationalize, we're trying to put something into operations into production. And so AI ops instead of being the old definition of just like, oh, is this anomaly detection? Is it part of some thirdparty service? as a part of some piece of software. It's starting to slowly be co-opted to where it's like, well, can AI ops mean how do we put generative AI into production? And so our courses here, you'll see some of the old definition like number two, for example, applied ML for AI ops. It says anomaly detection, it says log clustering, it says forecasting, right? That's taking machine learning and applying it to an existing system. Notice you don't necessarily see generative AI models in this course flow but don't worry that's coming right and the reason I say that is if you look at number six there where it says MLOps for AI ops that's actually ML flow and couplow that's the ability to stick models into production. So we're starting to cross the line where it's the traditional AI ops definition and then we're kind of expanding into the MLOps kind of in a sense like LLM ops kind of definition. So, just know that not only are we asking you to, hey, here's the skills you can build, but we've also got this learn by doing path that lets you really experience what the old definition of AI ops is and kind of what seems to be the new evolving definition of like, hey, can I stick generative AI into production and like operationalize it? Can I make can I deploy it? Can I monitor it? Can I secure it? >> So, just clarify that. >> Yeah. So that's kind of I think uh a common um I guess area of confusion for a lot of people when when they hear the word AI ops like we're naturally uh you know lean towards thinking that this is operations for AI rather than um you know AI assisting in our in our previous like existing uh operations right which is an ultimately detection and you know AI injected into um you know the monitoring tools and the and the log clustering tools and giving us insights based on that. That has been the traditional definition of AI ops. But is is that do you do you see people refer to AI ops as more ops for AI and AI workloads and for building and managing AI? Is that uh is that really getting kind of popular? >> Yeah. So what I've seen is like what you described which is that like hey we've there just a second apologize. Um so what we've got is we've got AI like injected into operational workflows. So I agree with that mad. I think that's a great definition. And then you've got, like you said, AI for deploying and enabling AI, right? And I think what we're seeing is that we're seeing some of that evolution, but I think the term that I'm hearing the most now is actually what we showed on this slide. And I'm going to go back to this so we can see it is that this agentic DevOps seems to be the more like centered on term even more than a second definition around AI ops. So we're seeing a shift where people are saying well now we're talking about agentic devops and that seems to in a sense encompass the deployment of models as well as using models as agents. So there seems to be kind of an interesting shift there but I just want to say your question is valid and I mean you tell me because you're just as involved in the industry as I am. >> It seems like we haven't centered on a good definition for the things that we're talking about just yet. Yeah. No, I mean from the research I've done uh because because it was originally a bit confusing to me um because when I first heard the term this that's what naturally kind of uh you know occurred to me but like when I read about it if you go to the the IBM blogs and um you know uh the blog post that really uh talk about AI ops it's it's all centered around injecting AI into the uh ops and you know uh getting insights from uh our monitoring and logging and you know uh operations. So yeah, um makes makes sense. >> So it's funny because we're still just talking about learning as AI is a skill and we're already kind of this deep into like you know details because I mean it and and I think this is why we agreed that we would talk about it because I think it is confusing for people and like we're in the middle of this and so if we don't have clarity it must look very confusing from someone coming from the outside in. So um and I think we have some clarity like we know where things are kind of headed. We know that these are the general agreed upon definitions but again this is this is living breathing technology so it's going to change over time. So we've got courses, we've got also our learn by doing path, right? Because for people who want to get right into the AI ops traditional and some evolution into the new definition of AI ops and then of course just to mention this, we have obviously certification prep for the few certifications that are available related to generative AI. I just want to say and unless this has changed, Munch, you tell me if if you've heard of anything. Currently, other than the the cloud providers and Nvidia, I don't know of anyone who has any generative AI certifications to date, right? Uh it's either college courses or people are just, you know, uh creating their own individual certifications. >> Yeah, agree. I think that's that's what I've seen too. like what you've listed there um are the top ones and and Google too. I'm I'm assuming. >> Oh yeah. Yeah. Google. >> Yeah. >> As well. [snorts] >> Yeah. And and um we by the way just so people ask us all the time about recency. Uh Mad raises a good point. We do know that like for example Google Microsoft and Google and uh AWS are all coming out with more certifications. So like AWS is coming out with the Genai professional developer professional. um Microsoft has I think two or three kind of business leader beginner AI courses coming out right so yeah and uh so just just know and we're keeping an eye on the whole space Nvidia by the way has 10 certifications now for generative AI um I will say this Nvidia has more infrastructure DevOps certifications than anybody else thus far uh at last look there was four uh DevOps uh infrastructure certifications for Nvidia but I just want to mention it it is for Nvidia's product ecosystem. So if you're looking for something that's not outside of that, Nvidia is not going to be uh your your choice. Stick with the cloud ones if you're looking for something a little bit more generic. >> Yeah. So the other thing is is that we talked about Agentic. So there's there's several courses on our platform like ADK, there's crash course on AI power DevOps, we've got Nad right for um automation and workflows, we've got AI agents, we've got AI agents fundamentals by Caleb which is also out on the courseware as well. So we have an AI agents and an AI agents fundamentals course. We have AI assisted anible as you can see there. We've got MCP, we've got rag, we've got vector database, we've got a bunch of items out there. Um and I'll show the learning path here in a second. Um but there's a bunch for the Agentic DevOps where we think kind of everything is kind of headed. So to re like post the question Mumshot that you asked. It's like well what do I do with my existing skills? You keep them [laughter] and you add I know it's going to sound crazy. You add more skills on top of those skills that you already have. Some of those are going to normally atrophy over time, but you do have them in your back pocket. And I I just want to throw this out there. You might say, "But Michael, that sounds impossible." And I I I understand. I just want to mention is that when I first got into the industry, there was no cloud. And then cloud became a thing. So you acquired cloud skills. And then, you know, not even what, seven years, eight years later, Docker and Kubernetes come out. So then you acquire Docker and Kubernetes skills. So now you've got Docker and Kubernetes skills. You've got some cloud skills and then you have the old kind of CIS admin skills that you had before Linux and all that and then now you know then platform engineering comes out and you figure that out and then and so you know if it feels like oof like we're we're adding yet one more thing. Don't get too overwhelmed. Skills will naturally fall away as you don't use them. So like I'm still pretty good at Linux and I'm still really good at VI and I'm still really good at text and bash scripting. But if you were to say, "Hey, Michael, how are you at Windows?" >> Well, I used to be a Windows administrator. I used to be MCSE and all the other stuff. That that skill is completely gone, right? I I couldn't administer a Windows box now um because I haven't kept up with the technology. So, just know as you work on projects and as you keep an eye on the industry, those things will naturally evolve in certain segments of your skills will fall away and that's totally okay, right? Like I used to know Docker Swarm really well. Docker Swarm's not the thing. Kubernetes is, right? So, I'm I don't really know Docker Swarm as well as I used to, right? But I know Kubernetes really well because that's the one that stayed dominant. So just keep that in mind if you're like looking at this and you're like, "Holy crap." You're like, "Mum, Michael, you got to be kidding. Like you've added more stuff here." And it's like that's it's okay. Like your your projects and what you chase and what you learn are going to form the basis of your skill set. And so just it just let it be okay that some things just fall off because that's that's natural. So >> yeah, >> and don't worry, the industry will give you signals. >> Yeah, I think that's a very important uh point. um like as you build experience in the industry uh I don't think um you should worry that all of a sudden one day you're just going to be irrelevant right I mean that's never going to happen it's just everything is going to add up and you know as you said like things that were not important that are not important are naturally going to die away but like if you stack all of the skills that you have with AI and anything that's new and kind of 10x your productivity or your efficiency and and um um you Now you can do a lot more uh because you have a lot more of that foundational knowledge than anyone else and then you add AI on top of that. Um um uh and you know you have enough uh I mean the things that the gaps that you had earlier could now be filled a little bit with AI and you can learn uh as you use AI um learn with AI as you have had it in one of the slides. So I think all of those are going to stack up and make you like really really valuable uh if uh kind of if you do the right thing now which is um you know upscale on some of the AI uh technologies that are that are in there and see how you can use that to make yourself like really really valuable. >> Yeah, agrees. Absolutely agreed. Um then this is just more of showing like our catalog like we also have SageMaker and you know I think I mentioned the AI agent fundamentals running agent like models with Lama which is super cool. Alama is a fun local system. I think the other thing is though is in addition to the just the course catalog is that we do actually have on our site right and it does vary a little by membership right but we do have on our site ways to actually have AI help you. So, in addition to learning AI as a skill, we have the ability for you to have an assistant support you in labs. And you'll usually see it when you're in the labs. You'll see like the little beta tab. And the interesting thing about our AI assistant is it it's actually looking at your labs. It's actually seeing what you're doing, right? And the reason this is significant for you to be aware of is that one thing we know in like learning and education is that personalized tutoring is the number one enhancer of learning outcomes. Hands down. You can practically say that at any industry education conference or to any educator and they'll be like if you have a tutor of any type, whether it's an AI tutor or um you know like a human being, tutoring and personalization are going to guarantee the greatest learning outcomes. So when especially when you're stuck in a lab and it's like you know let's say it's 2 o'clock in the morning somewhere, although we're we're pretty so you' be hardressed to catch us offline. um is and you're stuck on a lab and you let's say no one's responding on the forums and you just really want to understand what's happening. This is one of those great ways where you can actually get feedback immediately and it will look at what you're doing and help you get unstuck. In addition, the tutor, right, which again you can actually find it in our course catalog. The tutor by the way will answer questions that you might get stuck on. Now I know a lot of you have chatbt and I know a lot of you have Claude and Enthropic and I know you have things. This is also by the way inside the platform. So if you don't have access to those, it's right here. Like so you can see it if you have that level of membership. And so just something to be aware of is that we are trying to inject enhancements to help you learn better, quicker, faster, easier in addition to learning AI as a skill. Um, it's also worth mentioning that, you know, very similar to kind of like a an LLM gateway or like a a generative AI gateway, we have a a plug-and-play play API key that will allow you to get access to multiple models if you want to do some programmatic playing around. So, code key is also something that we have just as a feature to enable your ability to play with multiple models, right? And um, of course, then of course the last is that we actually have a lot of practice in real environments. So like if I were to switch gears for a second and just show our learning paths, right? You'll see a lot of the courses that I just talked about are like right here. Like this is for all engineers. This is for nontechnical users. And if you want to go over to practice, right, you can get into our sandbox environments and notice there's a whole category of artificial intelligence. So you can come in here and play with moonshot, you can play with cloud code, you can play with these here inside our environments. Now I want to be very forthright. We can't give you unfettered access to the most powerful unlimited like rate unlimited thing in the entire world. It it would it would break us and it would pro you know honestly probably break the learning as well. But know that you you can get access to get in here and play with a lot of these things. Like for example clock code you can see like what is a command line assistant? What makes it different? What can you do with it? And you can actually oh look it says that we're live right now. [laughter] you can launch it and play with it and you don't have to worry about setting up your own subscript all that you you can just come in here and play with it. In some cases you might need um a key but for the most part you come in here, you set it up, you use it and it works great. >> Yeah. And uh uh the the other one of the things to highlight is code key which is um maybe do you want to just open that? It's right there in the list. >> Yeah. Yeah. And I if you haven't if you're not aware of this so you know if you probably don't have uh a paid subscription to um any of the LLM models open AAI or anthropic and but if if you are part of code cloud then uh you get access to um you know a million tokens that you can use uh against any of these models. There are a bunch of models that are listed there. Um, and if you just want to use it for uh educational and training purpose, just understand uh if you're starting out and want to understand um how these work without worrying about being overcharged uh with token and um you know for tokens or uh security if you're uh don't want to be worried about hitting security limits then yeah this is a safe kind of playground that you can go to and the keys are rotated uh every hour or so. So uh yeah uh a safe way for you to play around and there are a lot of models there that uh that are really popular that you can that you can use. >> Yeah. Yeah. Yeah. That's very useful especially for if you want to just experiment and play with something. So >> yeah. So playgrounds are listed there and so as you can see we showed a bunch of them including MCP and code key and um uh any of like the limited but SageMaker models as well as codeex, cloud code, moonshot etc. So just know that that's available to play with. So we're not only interested obviously in you learning all of the concepts which of course our courses include labs as well but also the playgrounds and of course all the tooling that we talked about. So just know that these are options inside of our environment. We are trying to make sure that you have access to the things that you need to have access to when it comes to making sure that you are able to evolve your skills. Now this is a question for both of us is I get asked this a lot like what's the one thing I could start with today that like either of you right this is a question I actually got in LinkedIn either of you feel would like like take like move us forward. I have a particular answer that I give. I can go first if you want to, but if you have an idea, uh, I'd love to hear it. Um, but I usually have a thing that I say to people that is usually somewhat, uh, not controversial, but like they it's unexpected, right? When I'm like, hey, what do I do if I've learned all these traditional skills? What's the next thing that I do in order to kind of evolve and get into the whole AI thing, right? Um, maybe your response is the same as mine. I usually say just start using it. Like just it doesn't even matter. It [clears throat] doesn't matter if it's cursor. It doesn't matter if it's GitHub copilot. Doesn't matter if it's Claude code. Just start using it to start writing the things that you're probably doing by hand. Like just start using it. If you want to get cloud code to write it, great. If you want to get cursor to write it, great. If you want to use Klein, great. Like don't get hung up on the tool as much. I mean, and I have my own personal opinions and I, you know, I'm sure you do as well about which ones I think are my favorites, but in general rule of thumb is that don't don't sit there and get paralyzed about like what's the right thing. Just just start using it. That's my general statement. >> Yeah. No. Yeah, that's that's uh you know, that's something I would recommend too. I think that the most important thing is just start using it and then uh you learn on the go. Uh maybe one of the things I could add is um to me at least whenever I'm learning something new I I I found that I I can't learn uh just for the sake of learning you know like I I can't just speak I can't just decide okay I need to learn this particular tool just because I want to just stay up to date on it. Um I I I find it very hard to do that. Instead, I I want if if it has something to do with whatever I'm already working on, if it if it can improve my workflow, if it can improve whatever I'm building, then I get really excited about it and then I can dig in and I can learn and I can spend as much time as I want. So, I I need to have like some kind of purpose associated with it, some kind of project. So, this could be a uh a project that I'm working on internally. Uh and then I would decide, okay, I'm going to use uh I I might have templates for that project. I might have done similar things in the past but you know uh I'm going to work on it but this time I'm going to use an AI tool to help me build it or I'm going to integrate an AI tool uh in the project >> um for the sake of u kind of learning it right or uh this could be another thing that's always helped me is like creating courses to uh you know uh learn it so that I can create some kind of content uh out of it some kind of course out of it again kind of gives me a purpose so then I'm not I'm not just like learning something for like 10 minutes and um um you know deciding that I'm done instead now I have to like really research and I have to spend time and now I have a purpose attached to it so it just really helps uh me push kind of push that boundary right so yeah that's kind of my my take on that >> oh yeah that's good yeah you're right you're right I think when I say use it you're absolutely right I think that's a great addition like clarification of like hey make sure you using it for something meaningful either work or something that you really love that you are passionate about. Yeah, I totally I totally agree with that. That's a great qualifier. >> Yeah, because otherwise you're right. It's like you know like some people will look at K agent like for example as one of the courses here and be like do I do I need to know K agent? It's like well hold on what are you doing for work? What are you interested in? Are you studying? Are you chasing something? It's like make sure there's a good why right like for doing it. Yeah. Which is why I felt like certifications are also a decent why for a lot of learning because otherwise you learn something and then you decide after a couple hours that yeah I've learned enough you know that that's enough for me but like a certification because it has a structure to it and you have to complete certain modules it's it's a it's always been a clear why. So in the early days of my career um that was a big driver for me um you know to put credentials on my resume and to so and and I was never I didn't really have a lot of uh opportunities at work to work on like some of the really interesting stuff that I wanted to work on and one of the one of the hacks I found was to kind of commit to something else um either a certification or maybe to give a talk uh somewhere or maybe an internal presentation too like I take up like um commit to like a giving a training internally to a team on something uh something new and then now I have to do it like there's no other way. So I kind of use that as an opportunity to kind of go deep into that technology and learn that uh for myself. [laughter] That's a good point there. I there have been certifications where I forced myself to sign up for the certification to get the thing done instead of waiting to see if I was ready like to pass the practice exams before I would sign up for the certification. I think that's a great commitment, right? >> Um they say that business owners who commit fully to a business with no fallback plan are typically more successful than those who are kind of like half in, half out. So that kind of makes sense a little bit. Yeah, that's that's good. Yeah, I like that. And what a way to alleviate like boredom. Like when you're like working in something and you're like, "Okay, I've kind of learned this workflow and I'm doing this job and it's good, but I want to I'm really passionate about something else." And then you chase a certification. It's like and then you get validation at the end where it's like, "Oh, here you go. You did it." Yeah. That's good. >> Yeah. >> Yeah. >> Yeah. And I think that's why a lot of people are um fond of certification and going after it, right? because it's not like everything you learn in the certification are things that you do at work, but it's like really a good driver, a motivator for uh keeping ourselves accountable and getting things done. And that's kind of what I like about it. >> Yeah. Yeah. Yeah. Absolutely. I like it too. obviously since we both have certification [laughter] certifications. Um uh so just to make sure I'm mindful of time uh is that you know just to say this is that we've covered like quite a few things but I think that hopefully the thing you walked away with is one where just get started and pick something meaningful as Mumshad said right something that you feel like would be compelling either to help your career or to chase the thing that you're most interested in and really there are tons of courses on our website at least 30 plus around frameworks AI assisted development AI you know the different tools everything from cloud code to client to whatnot. And then we do have AI playgrounds, we have a tutor, we have a learning path, we've got code key, which will allow you to do all kinds of programmatic interaction without having a subscription. So there's there's a bunch of pieces there. And so, you know, our recommendation is just like get started. Now, obviously, if you're here for the AI practitioner cohort, you're already at number four, right? You're already chasing a certification, which is excellent, which is literally what Munch was just talking about how that can be incredibly useful if you're in the Kubernetes space. just to mention this is that the cooperat and the golden cooperat are still absolutely incredibly valuable right know also that kubernetes and the cncf is evolving over time and you're seeing more and more of things like k agent or couplow or kserve you're seeing more of the the or even for example video um sorry GPU scheduling inside of kubernetes if you're not familiar with that by the way that's the ability to take an AI workload and schedule a video card to attached to that workload um at at scale. So like it could be a hundred different like workloads being scheduled at the same time. So just know we're seeing a lot more of that. So if the question we're often asked is like are these skills still relevant in 2026? Yes. Are they going to continue to be relevant over time? Yes. But we are going to see a shift where more and more implementation is taken by AI agents because we're going to give it to them, right? And so again remember my analogy at the very beginning. Imagine that you are in a self-driving car and something goes slightly ary and you now have to drive but you haven't driven in a while because you've been in a self-driving car for a year, right? And so these skills can in a way will become even more and more important because unfortunately they will probably slightly atrophy from disuse. What's the tale? This is the other question I get like what how long is it going to take for us to see that diminishment of skills? No one really knows. But I would say this is that every prediction that we've made in the past about technologies usually go slower than we predict. So Mumshad and I have been talking for a while now about like is it 3 to 5 years? Is it 18 months? Is it you know like what is the time frame? And I guess the bottom line is is just keep an eye on the horizon like Mumshot and I are suggesting hey build your skills know how these things function and do the AI stuff. just add a little bit pieces and just slowly shift as the industry shifts. Don't worry, it won't be an overnight thing. You'll see it happening. So, just keep an eye on the industry. Let us keep talking to you. We'll let you know as things shift as well. And you'll see our courseware shift as the industry shifts. So, just stay with us and we'll make sure to take care of you as we see things change as well. >> Yeah. Yeah. I think that's a really good point. uh especially to those who are really worried about um you know becoming non nonrelevant in the in the era. I think yeah I don't think it's going to happen overnight and there's you know uh we're going to have enough time to upskill and and this goes back to like the similar kind of technological trends that you mentioned in the past um Michael like cloud I mean people who were um I don't know working with mainframes and stuff must have gotten uh irrelevant around the time cloud and everything else came in right so >> um before that there was virtualization, there was um so you know a a lot of things uh and we've done this a few times already uh at least in my short kind of career in the past like 20 years. Uh started off in and kind of the storage uh area and then moved on to virtualization. So did a bit of did a bit of work in that area and then moved into cloud um and then you know DevOps and containers and Kubernetes. So we've done that. We've seen the shift happen like at least a few times. Of course, this is much bigger. This is very different and we have to move a little bit of fast much faster this time. Uh but this is another shift and we just have to all kind of upscale ourselves and be ready for the kind of the next wave. So uh yeah I I guess um no no need to be uh worried but uh and at the same time we have to be prepared. >> Yes. Well said. Yeah we have to be prepared. >> All right. Well shall we shift to taking some questions? >> Yep. >> Okay. Let's take a look. >> Um so maybe we'll just go from the Yeah. just just picking the ones that I see software backend engineering. So uh software development has been has not been something that we've focused on as much um in the past. It's probably something that uh you know we might in the future but uh it's not something that has been our core area. We've been focusing mostly on DevOps and uh you know infrastructure side of things. Uh but yeah if you have uh similar requirements requests please do uh share your thoughts at requests.copcloud.com. So there's a voting board and where you can kind of you know add your votes and uh um we kind of pick that's kind of our go-to place for identifying what our next set of courses are going to be. So uh please do share uh the details there. Um course on CPU versus GPU and GPU multi-threading. So this is uh we recently have uh released the foundational material uh courses and um uh which is uh run by Alan uh who's one of our trainers and there are uh some sections there that are purely focused on GPU and um yeah I think uh that's something that would be uh helpful. So if you check the course out uh there there are details there about uh GPU Um so diddy uh has has replied so uh the next plan for him is CK8s in CKS already booked. >> Um any advice [clears throat] for CKS? Um so sorry there's uh so for CKS I think um it's kind kind of a probably one of the hottest uh in the in the series as you yeah as you hear. So that's that's right. First of all, congrats on the CKA and and all the best for the the CK CKD and CKA certification and I think that's a good idea to book book them in. Um uh but if you if you've done CKA then you have you know kind of done 30% of CKS right now you have to now you have the other areas that uh you need to uh upscale on on the security side of things. uh if you've done any uh any any work in that area already then it's easy but if if security is completely new then there are some foundational concepts that you'll have to learn uh you know in terms of um you know what's what's like appsac and you know security at the OS level uh you know basic security fundamentals foundations and things like that and and that can probably take a little bit of a time but uh yeah that's that's the main challenge really it's just a new area new set of tools but if you have some familiar ity with it uh from us is uh from your days working as a admin then uh it's kind of becomes easy. >> Yeah, I I will say also that um we do have um the ultimate CKS [snorts] um like mock exam series and the reason that was created is because so you could practice things like falco rules and hardening clusters and microservices and pieces like that. Um obviously it is worth noting that in addition to that which you can take as you know as many times as you want um you know with your kind of u applying for the certification like subscribing to it you do get access to I think two killer sh runs which are absolutely essential as well. I would save those towards the very end and stick with our stuff as much as possible and just save your killer sh runs until maybe like your final ones. But just know that like we have ultimate certified Kubernetes CKS mock exams that will allow you to practice this hard stuff because you know understanding Falcon rules are back network policies supply chain uh I mean it the CKS exam is formidable and I think requires more practice than any other exam. I ma and I have said before that like this it is by far the hardest of the five. >> Yeah. >> But yeah it's going to be a tough one and a challenging one. So will really push you to learn a lot of things that are probably outside of your comfort zone. And I think that's that's the real fun part in it. Um Lind says um yeah, she only has uh CK80 left to complete. So that's um yeah um so I'm assuming all the other four are complete. So you're probably on the way um to become a cube or not if or probably just completing the three. Um but whichever it's it's a great achievement and yeah all the best for your next >> um someone asked they said um can we please offer additional NAN courses and so Andreas Ortado let me just say this is that um if you want additional courses like we have a requested course page on our homepage um we probably would need more specificity like what is it like what NAS course do you want um because I mean we could we could create an additional course we just need to know what it that you're specifically requesting because we have the one. So, if you want something specific, just let us know. And know that like feedback when you when you go to our request a course page, that feedback is highly prioritized. So, any of you who vote around wanting a particular course, we look at that every time we decide courses. So, feel free to go in there and let us know what it is that you're interested in. >> Cool. Um just looking at other questions. >> Uh LFCS mock exams coming up in code cloud. >> LFCS. Um don't we have that already? We have an LFCS course, right? Yeah, we have a prep course and in that I believe there is at least one I think possibly more mock exams. So >> yeah, just taking a quick look now. I'm almost positive >> that there are some in Yeah, there's four mock exams actually in that course. I just looked. >> Yeah. >> So yeah, so there's four there. Um, Mohan, just to say this is that if you want something different, again, like go to a request a course page at the bottom of our homepage and like let us know what it is that you're looking for. Yeah. >> Because if if there needs to be an additional course or um a different flavor or something like that, that really helps us like understand and distinguish like what we should build for the next quarter or next year. So, yeah, let us know. >> Cool. Uh, cloud March, that's interesting. Um, again, would love to know a bit more of like what you what you're expecting, but we are actually working uh right now. Uh, we're probably almost done putting together a merch store. So, uh, it's in progress. Uh, but yeah, I would love ideas on uh what would be things that uh you expect to see. Uh, that would be very helpful. So if you can you know u send us a message and let us know uh what would be interesting that uh we'll be happy to consider that. >> Yeah in the works >> very sure is that before we got on I was telling Mshaw that I lost a codecloud hat that was one of our original kind of OG hats that we had very limited edition of uh and I lost it in New York City and so I was like lamenting so I can't wait to see it from a merch store. That would be awesome. >> Yeah. Yeah. We'll have that. >> Absolutely. Yeah. Uh let's see. Uh Promise um Promise Mor was asking like does one need to be a CIS admin for the sake of mastering Linux before moving to DevOps. I I'll give you my kind of abbreviated answer. Everything we do happens inside of Linux for the most part or Windows. So you know most of us in this space are in the Linux space. So you have to be able to manipulate Linux really well. Like it has to almost like be an afterthought. So do it is recommended that you have some level of mastery of Linux before because you won't be able to automate, write shell scripts, manipulate files, you know, so and it's kind of like something that is just required as a foundational skill. So do you need to know all the edges of Linux? No. Is it recommended? >> Yeah. >> Yes. [laughter] >> Highly recommended. Yeah. >> Cool. All right. I think we're at um at the time. Well, uh, thank you so much once again everyone for joining us. Uh, we will not be able to do ne the the week after because we're going to be at CubeCon again. If you're if you happen to be in Europe, please uh do uh come and uh meet us there. If not, we're going to uh come back and meet again u two weeks after that. Well, thank you so much uh once again and thanks Michael for joining us here today and yeah, I'll um see you all next time. >> Yeah, take care everyone. >> Right. Bye-bye. >> [music]