 Good afternoon, fellow cloud nerds, and welcome back to theCUBE's live coverage of AWS re-invent here in a fabulous Sin City, Las Vegas, Nevada. My name is Savannah Peterson, joined by my fabulous co-host, John Furrier. John, how you feeling? Great, feeling good. It's getting going. Day one of three more days after today. Yeah. So much conversation talking about business transformation as cloud goes next level, next generation. Later's classic is still around, but the next gen cloud's here, it's changing the game, a lot more AI, machine learning, a lot more business value. I think it's going to be exciting. It's the next segment, it's going to be awesome. It feels like one of those years where there's just a ton of momentum, I don't think it's just because we're back in person at scale, you can see the literally thousands of people behind us while we're here on set conducting these interviews. Our bold and brave guests, just like the two we have here, combating the noise, the libations, and everything else going on on the show floor. Please help me welcome Mike from ScienceLogic and AJ from Zebrium Gentlemen. Welcome to the show floor. Thank you, Savannah. It's great to be here. How you feeling, Nick? Are you feeling the buzz, Mike? Feel the energy? It's tough to not feel and hear the buzz. Yeah. Can you hear me? Yeah, yeah, yeah. Can you hear me now? What about you, AJ? How does it feel to be here? Yeah, this is high energy. I'm really happy to bounce back from COVID. I was a little concerned about attendance, but this is hopping. Yeah, I feel it just, you can definitely feel the energy, the sense of community. We're all here for the right reasons. So I know that I want to set the stage for everyone watching. Zebrium was recently acquired by ScienceLogic. Mike, can you tell us a little bit about that and what it means for the company? Sure, sure. Well, first of all, ScienceLogic, as you may know, has been in the monitoring space for a long time now. 20 years, I believe, just about. And what we've seen is a shift from kind of monitoring infrastructure and monitoring these increasingly complex, modern cloud native applications, right? And so this is part of a journey that we've been on at ScienceLogic to really modernize how enterprises of all sizes manage their IT estate, okay? So managing now workloads that are increasingly in the public cloud, outside the four walls of the enterprise, workloads that are increasingly complex. They're microservices-based, they're container-based. And the rate of change, just because of things like CICD and Agile development, has also increased the complexity in the typical IT environment. So all these things have inspired to make the traditional tools and processes of managing IT and IT applications much more difficult. They just don't scale. One of the things that we've seen recently, Savannah, is this shift in sort of moving to cloud native applications, right? Huge shift. Today it only incorporates about roughly 25% of the typical IT portfolio. The most of the projections we've seen indicate that that's going to invert in about three years. 75% of applications will be what I call cloud native. And so this really requires different technologies to understand what's going on with those applications. And so Zebrium interested us when we were looking at partners at the beginning of this year, as they had a super innovative approach to understanding really what's going on with any cloud native application. And they really distill, they separate the complexity out of the equation and they use machine learning to tremendous effect to rapidly understand the root cause of an application failure. And so I was introduced to Aaj, beginning of this year actually, it feels like it's been a long time now. But we've been on this journey together throughout 2022 and we're thrilled to have Zebrium now part of the ScienceLogic family. Aaj, Zebrium saves people a lot of time, obviously. I've worked with developers and seen that struggle when things break, shortening that time to recovery and understanding is so critical. Can you tell us a little bit about what's under the hood and how the ML works to make that happen? Yeah, so the goal is to figure out not just that something went wrong, but what went wrong. Right. And we took, based on a couple of decades of experience from my co-founders. Casual couple of decades came into this product. Just to pull that out, yeah, great. It took some general learnings about the nature of software and when software breaks, what tends to happen. You tend to see unusual things happen and they lead to bad things happening. Very simple. Yes, mutations leads to bad things happening, generally speaking. So what Zebrium's really good at is identifying those rare things accurately and then figuring out how they connect or correlate to the bad things, the errors, the warnings, the alerts. So the machine learning has many stages to it, but at its heart it's classifying the event catalog of any application stack, figuring out what's rare. And when things start to break, it's telling you this cluster of events is both unusual and unlikely to be random. And it's very likely the root cause report for the problem you're trying to solve. We then added some nice enhancements such as correlation with knowledge bases in the public internet. If someone's ever solved that problem before, we're able to find a match and pull that back into our platform. But at the heart, it was a technology that can find rare events and find the connections with other events. Yeah, and this is the theme of re-invent this year, data, the role of data, solving end-to-end complexities. One, you mentioned that. Two, I think Mike, your point about developers in the CI-CD pipeline is where DevOps is. That is what IT now is. So if you take digital transformation to its conclusion or its path and continue it, IT is DevOps. So the developers are actually doing the IT in their coding, hence the shift to autonomous IT. Now those other functions that IT used to be a department, not anymore, or they still are, but they'll go away, is security and data teams. You're starting to see the formation of new replacements to IT as a function to support the developers who are building the applications that will be the company. That's right, yeah. I mean, that's, and you agree with that statement? Yeah, I really do. And, you know, collectively independent of whether it's like traditional IT or it's DevOps or whatever it is, the enterprise as a whole needs to understand how the infrastructure is deployed, the health of that infrastructure, and more importantly, the applications that are hosted in the infrastructure, how are they doing, what's the health, and what we are seeing and what we're trying to facilitate at ScienceLogic is really change the lens of IT from being low-level compute storage and networking to looking at everything through a service's lens. Looking at the service being delivered by IT back to the business and understanding things through a service's lens. And Zebra really complements that mission that we've been on by providing, in a lot of cases, service equal-equal application. And they can provide that kind of very real-time view of service health in, you know, kind of the IT. And automation is beautiful there too because as you get into some of the scale, AJ, understanding how to do this fast is a key component. Yeah, so scale, you've pinpointed one of the dimensions that makes AI really important when it comes to troubleshooting. The humans just can't scale as fast as data, nor can they keep up with complexity of modern applications. And the third element that we feel is really important is the velocity with which people are now rolling out changes. People develop new features within hours, push them out to production. And in a world like that, the human has just no ability or time to understand what's normal, what's bad, to update their alert rules, and you need a machine or an AI technology to go help you with that. And that's basically what we're about. So this is where AIOps comes in, right? Perfectly, yeah. Yeah, you know, and John started to allude to it earlier, but having the insight on what's going on we believe is only half of the equation, right? Once you understand what's going on, you naturally want to take action to remediate it or optimize it. And we believe automation, it should not be an exercise that's left to the reader. Yeah. There's a lot of traditional platforms have done. Instead, we have a very robust no-code, low-code automation built into our platform that allows you to take action in context with what you're seeing right then and there. Yeah, essentially monitoring in term you use, observability, some use this fancy word today, is critical in all operating environments. So if we kind of holistically, hey, we're a distributed computing system, AKA cloud, you got to track stuff at scale. You got to understand what the impact is from a systems perspective. There's consequences to understanding what goes wrong. So as you look at that, what's the challenge for customers to do that? Because that seems to be the hard part as they lift and shift to the cloud, run their apps on the cloud. Now they got to go take it to the next level, which is more developer velocity, faster productivity, and secure. I mean, that seems to be the table stakes now. How are companies forming around that? Are they there yet? Are they halfway there? Where are they in the progression of one? Are they changing in, if so? Yeah, that's a great question. I mean, I think whether it's an IT use case or a security use case, you can't manage what you don't know about. So visibility, discoverability, understanding what's going on, in a lot of ways that's the really hard problem to solve. And traditionally we've approached that by like harvesting data off of all these machines and devices in the infrastructure. But as we've seen with Zebrim and with related machine learning technologies, there's multiple ways of gaining insight as to what's going on. Once you have the insight, be it an IT issue, like a service outage or security vulnerability, then you can take action. And the idea is you want to make that action as seamless as possible. But I think to answer your question, John, enterprises are still kind of getting their heads around, how can we break down all the silos that have built up over the last decade or two, internally, and get visibility across the estate that really matters? And I think that's the real challenge. And at the velocity that applications are growing, just looking at our notes here, number of applications scaling from 64 million in 2017 to 147 million in 2021, that goes to what you were talking about even with those other metrics. Earlier, 582 million by 2026 is what Morgan Stanley predicts. So not only do we need to get out of silos, we need to be able to see everything all the time all at once from the past legacy as well as we extend at scale. How are you thinking about that, Ajay? You're now with a big partner as an umbrella. What's next for you all? How are you gonna help people solve problems faster? Yeah, so one of the attractions to the ZEPRIM team about science logic, aside from the team and the culture, was the product portfolio was so complimentary. As Mike mentioned, you need visibility. You need mapping from low-level building blocks to business services. And at the end of the spectrum, once you know something's wrong, you need to be able to take action automatically. And again, science logic has a very strong product, a set of product capabilities and automated actions. What we bring to the table is the middle layer which is from visibility, understanding what went wrong, figuring out the root cause. So to us, it was really exciting to be a very nice tuck-in into this broader platform where we help complete the story. Yeah, that's exciting. Should we do the Insta challenge? I was just getting ready to do that. You go for it, John, you go ahead and make it up. All right, so we have this little tradition now. Instagram reel, short and sweet. If you were gonna see yourself on Instagram, what would be the Instagram reel of why this year's reinvent is so important and why people should pay attention to what's going on right now in the industry for your company? Well, I think partly what Ajay was saying, it's good to be back, right? So seeing just the energy and being back in 3D and mass is awesome, again, it really is. But I think this is where it's happening. We are at an inflection point of our industry and we're seeing a sea change in the way that applications and software delivered to businesses, to enterprises. And it's happening right here. This is the nexus of it. So we're thrilled to be here as a part of all this and excited about the future. All right, Ajay, your Instagram reel. Knowing what's happening in the broader economy, in the business context, it feels even more important that companies like us are working on technologies that empower the same number of people to do more because it may not be realistic to just add on more headcount given what's going on in the world. But your deliverables and your roadmaps aren't slowing down. So still the same amount of complexity, the same growth rates, but you're going to have to deal with all of that with fewer resources and be smarter about it. So the approaches we're taking feel very much of the moment given what's going on in the real world. I love it, I love it. I've got kind of a finger to the wind, potentially hardball question for you here to close it out. But given that you both have your finger really on the pulse right here, what percentage of current IT operations do you think will eventually be automated by AI and ML? Or AI ops? Well, I think a large percentage of traditional IT operations and I'm talking about network operating center type of, checking heartbeat monitors of compute storage and networking health, I think a lot of those things are going to be automated. Machine learning, just because of the scale, you can't hire enough knock engineers to scale that kind of complexity. But I think IT talents and what they're going to be focusing on is going to shift. And they're going to be focusing on different parts. And I believe a lot of IT is going to be a much more of an enabler for the business versus just managing things when they go wrong. So that's what I believe is part of the change. That's your heart, right? Ajay, what about your heart take? Knowing how error prone predictions are, I'll caveat my width. We're allowing human error here. I could be wildly wrong. But if I had to guess, in 10 years, as much as 50% of the tasks will be automated. Oh, he threw a number out there. I love it. You put a stake in the ground. You got to say the matrix. We're going to be part of the matrix and Star Trek. We can only turn back to this footage in a few years and quote you exactly when you have the Mackenzie research or the Morgan Stanley research that we've been mentioning here tonight and say that you called it accurately. So I appreciate that. Ajay, it was wonderful to have you here. Congratulations on the acquisition. Mike, thank you so much for being here on the ScienceLogic side. And congratulations to the team on 20 years. That's very exciting. John, thank you. I try, I try, thank you. You try, you succeed. And thank you to all of our fabulous viewers out there at home. Be sure and tweet us at theCUBE. Say hello, Furrier. Sav is Savvy. Let us know what you're thinking of AWS re-invent where we are live from Las Vegas all week. You're watching theCUBE, the leader in high tech coverage. My name is Savannah Peterson and we'll see you soon.