>>From around the globe. It's the cube with digital coverage of an AI ops virtual forum brought to you by Broadcom. >>Welcome to the AI ops virtual forum. Finally, some Artan extended to be talking with rich lane now, senior analyst, serving infrastructure and operations professionals at Forrester. Rich. It's great to have you today. >>Thank you for having me. I think it's going to be a really fun conversation to have today. >>It is. We're going to be setting the stage for, with Richard, for the it operations challenges and the need for AI ops. That's kind of our objective here in the next 15 minutes. So rich talk to us about some of the problems that enterprise it operations are facing now in this year, that is 2020 that are going to be continuing into the next year. >>Yeah, I mean, I think we've been on this path for a while, but certainly the last eight months has, uh, has accelerated, uh, this problem and, and brought a lot of things to light that, that people were, you know, they were going through the day to day firefighting as their goal way of life. Uh, it's just not sustainable anymore. You a highly distributed environment or in the need for digital services. And, you know, one of them has been building for a while really is in the digital age, you know, we're providing so many, uh, uh, the, the interactions with customers online. Um, we've, we've added these layers of complexity, um, to applications, to infrastructure, you know, or we're in the, in the cloud or a hybrid or multi-cloud, or do you know you name it using cloud native technologies? We're using legacy stuff. We still have mainframe out there. >>Uh, you know, the, just the, the vast amount of things we have to keep track of now and process and look at the data and signals from, it's just, it's a really untenable for, for humans to do that in silos now, uh, in, in, you know, when you add to that, you know, when companies are so heavily invested in gone on the digital transformation path, and it's accelerated so much in the last, uh, year or so that, you know, we're getting so much of our business in revenue derived from these services that they become core to the business. They're not afterthoughts anymore. It's not just about having a website presence. It's, it's about deriving core business value from the services you're providing to your, through your customers. And a lot of cases, customers you're never going to meet or see at that. So it's even more important to be vigilant. >>And on top of the quality of that service that you're giving them. And then when you think about just the staffing issues we have, there's just not enough bodies to go around it in operations anymore. Um, you know, we're not going to be able to hire, you know, like we did 10 years ago, even. Uh, so that's where we need the systems to be able to bring those operational efficiencies to bear. When we say operational efficiencies, we don't mean, you know, uh, lessening head count because we can't do that. That'd be foolish. What we mean is getting the head count. We have back to burping on and higher level things, you know, working on, uh, technology refreshes and project work that that brings better digital services to customers and get them out of doing these sort of, uh, low, uh, complexity, high volume tasks that they're spending at least 20%, if not more on our third day, each day. So I think that the more we can bring intelligence to bear and automation to take those things out of their hands, the better off we are going forward. >>And I'm sure those workers are wanting to be able to have the time to deliver more value, more strategic value to the organization, to their role. And as you're saying, you know, was the demand for digital services is spiking. It's not going to go down and as consumers, if w if we have another option and we're not satisfied, we're going to go somewhere else. So, so it's really about not just surviving this time right now, it's about how do I become a business that's going to thrive going forward and exceeding expectations that are now just growing and growing. So let's talk about AI ops as a facilitator of collaboration, across business folks, it folks developers, operations, how can it facilitate collaboration, which is even more important these days? >>Yeah. So one of the great things about it is now, you know, years ago, have I gone years, as they say, uh, we would buy a tool to fit each situation. And, you know, someone that worked in network and others who will somebody worked in infrastructure from a, you know, Linux standpoint, have their tool, somebody who's from storage would have their tool. And what we found was we would have an incident, a very high impact incident occur. Everybody would get on the phone, 24 people all be looking at their siloed tool, they're siloed pieces of data. And then we'd still have to try to like link point a to B to C together, you know, just to institutional knowledge. And, uh, there was just ended up being a lot of gaps there because we couldn't understand that a certain thing happening over here was related to an advantage over here. >>Um, now when we bring all that data into one umbrella, one data Lake, whatever we want to call it, a lot of smart analytics to that data, uh, and normalize that data in a way we can contextualize it from, you know, point a to point B all the way through the application infrastructure stack. Now, the conversation changes now, the conversation changes to here is the problem, how are we going to fix it? And we're getting there immediately versus three, four or five hours of, uh, you know, hunting and pecking and looking at things and trying to try to extrapolate what we're seeing across disparate systems. Um, and that's really valuable. And in what that does is now we can change the conversation for measuring things. And in server up time and data center, performance metrics as to how are we performing as a business? How are we overall in, in real time, how are businesses being impacted by service disruption? >>We know how much money losing per minute hour, or what have you, uh, and what that translate lights into brand damage and things along those lines, that people are very interested in that. And, you know, what is the effect of making decisions either brief from a product change side? You know, if we're, we're, we're always changing the mobile apps and we're always changing the website, but do we understand what value that brings us or what negative impact that has? We can measure that now and also sales, marketing, um, they run a campaign here's your, you know, coupon for 12% off today only, uh, what does that drive to us with user engagement? We can measure that now in real time, we don't have to wait for those answers anymore. And I think, you know, having all those data and understanding the cause and effect of things increases, it enhances these feedback loops of we're making decisions as a business, as a whole to make, bring better value to our customers. >>You know, how does that tie into ops and dev initiatives? How does everything that we do if I make a change to the underlying architectures that help move the needle forward, does that hinder things, uh, all these things factor into it. In fact, there into the customer experience, which is what we're trying to do at the end of the day, w w whether operations people like it or not, we are all in the customer experience business now. And we have to realize that and work closer than ever with our business and dev partners to make sure we're delivering the highest level of customer experience we can. >>Uh, customer experience is absolutely critical for a number of reasons. I always kind of think it's inextricably linked with employee experience, but let's talk about long-term value because as organizations and every industry has pivoted multiple times this year and will probably continue to do so for the foreseeable future, for them to be able to get immediate value that let's, let's not just stop the bleeding, but let's allow them to get a competitive advantage and be really become resilient. What are some of the, uh, applications that AI ops can deliver with respect to long-term value for an organization? >>Yeah, and I think that it's, you know, you touched upon this a very important point that there is a set of short term goals you want to achieve, but they're really going to be looking towards 12, 18 months down the road. What is it going to have done for you? And I think this helps framing out for you what's most important because it'd be different for every enterprise. Um, and it also shows the ROI of doing this because there is some, you know, change is going to be involved with things you're gonna have to do. But when you look at the, the, the longer time horizon of what it brings to your business as a whole, uh it's to me, at least it all seems, it seems like a no brainer to not do it. Um, you know, thinking about the basic things, like, you know, faster remediation of, of, uh, client impacting incidents, or maybe, maybe even predictive of sort of detection of these incidents that will affect clients. >>So now you're getting, you know, at scale, you know, it's very hard to do when you have hundreds of thousands of optics of the management that relate to each other, but now you're having letting the machines and the intelligence layer find out where that problem is. You know, it's not the red thing, it's the yellow thing. Go look at that. Um, it's reducing the amount of finger pointing and what have you like resolved between teams now, everybody's looking at the same data, the same sort of, uh, symptoms and like, Oh yeah, okay. This is telling us, you know, here's the root cause you should investigate this huge, huge thing. Um, and, and it's something we never thought we'd get to where, uh, this, this is where we smart enough to tell us these things, but this, again, this is the power of having all the data under one umbrella >>And the smart analytics. >>Um, and I think really, you know, it's a boat. Uh, if you look at where infrastructure and operations people are today, and especially, you know, eight months, nine months, whatever it is into the pandemic, uh, a lot of them are getting really burnt out with doing the same repetitive tasks over and over again. Um, just trying to keep the lights on, you know, we need, we need to extract those things for those people, uh, just because it just makes no sense to do something over and over again, the same remediation step, just we should automate those things. So getting that sort of, uh, you know, drudgery off their hands, if you will, and, and get them into, into all their important things they should be doing, you know, they're really hard to solve problems. That's where the human shine, um, and that's where, you know, having a, you know, really high level engineers, that's what they should be doing, you know, and just being able to do things I >>Think in a much faster, >>In a more efficient manner, when you think about an incident occurring, right. In, in a level, one technician picks that up and he goes and triaged that maybe run some tests. He has a script, >>Uh, or she, uh, and, >>You know, uh, they open a ticket and they enrich the ticket. They call it some log files. They can look up for the servers on it. You're in an hour and a half into an incident before anyone's even looked at it. If we could automate all of that, >>Why wouldn't we, that makes it easier for everyone. Um, >>Yeah. And I really think that's where the future is, is, is, is bringing this intelligent automation to bear, to take, knock down all the little things that consume the really, the most amount of time. When you think about it, if you aggregate it over the course of a quarter or a year, a great deal of your time is spent just doing that minutiae again, why don't we automate that? And we should. So I really think that's, that's where you get to look long-term. I think also the sense of we're going to be able to measure everything in the sense of business KPIs versus just IT-centric KPIs. That's really where we going to get to in the digital age. And I think we waited too long to do that. I think our operations models were all voted. I think, uh, you know, a lot of, a lot of the KPIs we look at today are completely outmoded. They don't really change if you think about it. When we look at the monthly reports over the course of a year, uh, so let's do something different. And now having all this data and the smart analytics, we can do something different. Absolutely. I'm glad >>That you brought up kind of looking at the impact that AI ops can make on, on minutiae and burnout. That's a really huge problem that so many of us are facing in any industry. And we know that there's some amount of this that's going to continue for a while longer. So let's get our let's leverage intelligent automation to your point, because we can to be able to allow our people to not just be more efficient, but to be making a bigger impact. And there's that mental component there that I think is absolutely critical. I do want to ask you what are some of these? So for those folks going, all right, we've got to do this. It makes sense. We see some short-term things that we need. We need short-term value. We need long-term value as you've just walked us through. What are some of the obstacles that you'd say, Hey, be on the lookout for this to wipe it out of the way. >>Yeah. I, I think there's, you know, when you think about the obstacles, I think people don't think about what are big changes for their organization, right? You know, they're, they're going to change process. They're going to change the way teams interact. They're they're going to change a lot of things, but they're all for the better. So what we're traditionally really bad in infrastructure and operations is communication, marketing, a new initiative, right? We don't go out and get our peers agreement to it where the product owner is, you know, and say, okay, this is what it gets you. This is where it changes. People just hear I'm losing something, I'm losing control over something. You're going to get rid of the tools that I have, but I love I've spent years building out perfecting, um, and that's threatening to people and understandably so because people think if I start losing tools, I start losing head count. >>And then, whereas my department at that point, um, but that's not what this is all about. Uh, this, this isn't a replacement for people. This isn't a replacement for teams. This isn't augmentation. This is getting them back to doing the things they should be doing and less of the stuff they shouldn't be doing. And frankly, it's, it's about providing better services. So when in the end, it's counterintuitive to be against it because it's gonna make it operations look better. It's gonna make us show us that we are the thought leaders in delivering digital services that we can, um, constantly be perfecting the way we're doing it. And Oh, by the way, we can help the business be better. Also at the same time. Uh, I think some of the mistakes people really don't make, uh, really do make, uh, is not looking at their processes today, trying to figure out what they're gonna look like tomorrow when we bring in advanced automation and intelligence, uh, but also being prepared for what the future state is, you know, in talking to one company, they were like, yeah, we're so excited for this. >>Uh, we, we got rid of our old 15 year old laundering system and the same day we stepped a new system. Uh, one problem we had though, was we weren't ready for the amount of incidents that had generated on day one. And it wasn't because we did anything wrong or the system was wrong or what have you. It did the right thing actually, almost too. Well, what it did is it uncovered a lot of really small incidents through advanced correlations. We didn't know we had, so there were things lying out there that were always like, huh, that's weird. That system acts strange sometimes, but we can never pin it down. We found all of those things, which is good. It goes, but it kind of made us all kind of sit back and think, and then our readership are these guys doing their job. Right? >>And then we had to go through an evolution of, you know, just explaining we were 15 years behind from a visibility standpoint to our environment, but technologies that we deployed in applications had moved ahead and modernized. So this is like a cautionary tale of falling too far behind from a sort of a monitoring and intelligence and automation standpoint. Um, so I thought that was a really good story for something like, think about as Eagle would deploy these modern systems. But I think if he really, you know, the marketing to people, so they're not threatened, I think thinking about your process and then what's, what's your day one and then look like, and then what's your six and 12 months after that looks like, I think settling all that stuff upfront just sets you up for success. >>All right. Rich, take us home here. Let's summarize. How can clients build a business case for AI ops? What do you recommend? >>Yeah. You know, I actually get that question a lot. It's usually, uh, almost always the number one, uh, question in, in, um, you know, webinars like this and conversations that, that the audience puts in. So I wouldn't be surprised, but if that was true, uh, going forward from this one, um, yeah, people are like, you know, Hey, we're all in. We want to do this. We know this is the way forward, but the guy who writes the checks, the CIO, the VP of ops is like, you know, I I've signed lots of checks over the years for tools wise is different. Um, and when I guide people to do is to sit back and, and start doing some hard math, right. Uh, one of the things that resonates with the leadership is dollars and cents. It's not percentages. So saying, you know, it's, it brings us a 63% reduction and MTTR is not going to resonate. >>Uh, Oh, even though it's a really good number, you know, uh, I think what it is, you have to put it in terms of avoid, if we could avoid that 63%. Right. You know, um, what does that mean for our, our digital services as far as revenue, right. We know that every hour system down, I think, uh, you know, typically in the market, you see is about $500,000 an hour for enterprise. We'll add that up over the course of the year. What are you losing in revenue? Add to that brand damage loss of customers, you know, uh, Forrester puts out a really big, uh, casino, um, uh, customer experience index every year that measures that if you're delivering good Udall services, bad digital services, if you could raise that up, what does that return to you in revenue? And that's a key thing. And then you just look at the, the, uh, hours of lost productivity. >>I call it, I might call it something else, but I think it's a catchy name. Meaning if a core internal system is down say, and you know, you have a customer service desk of a thousand customer service people, and they can't do that look up or fix that problem for clients for an hour. How much money does that lose you? And you multiply it out. You know, average customer service desk person makes X amount an hour times this much time. This many times it happens. Then you start seeing the real, sort of a power of AI ops for this incident avoidance, or at least lowering the impact of these incidents. And people have put out in graphs and spreadsheets and all this, and then I'm doing some research around this actually to, to, to put out something that people can use to say, the project funds itself in six to 12 months, it's paid for itself. And then after that it's returning money to the business. Why would you not do that? And when you start framing the conversation, that way, the little light bulb turn on for the people that sign the checks. For sure. >>That's great advice for folks to be thinking about. I loved how you talked about the 63% reduction in something. I think that's great. What does it impact? How does it impact the revenue for the organization? If we're avoiding costs here, how do we drive up revenue? So having that laser focus on revenue is great advice for folks in any industry, looking to build a business case for AI ops. I think you set the stage for that rich beautifully, and you were right. This was a fun conversation. Thank you for your time. Thank you. And thanks for watching >>From around the globe with digital coverage. >>Welcome back to the Broadcom AI ops, virtual forum, Lisa Martin here talking with Eastman Nasir global product management at Verizon. We spent welcome back. >>Hi. Hello. Uh, what a pleasure. >>So 2020 the year of that needs no explanation, right? The year of massive challenges and wanting to get your take on the challenges that organizations are facing this year as the demand to deliver digital products and services has never been higher. >>Yeah. So I think this is something it's so close to all the far far, right? It's, uh, it's something that's impacted the whole world equally. And I think regardless of which industry you rent, you have been impacted by this in one form or the other, and the ICT industry, the information and communication technology industry, you know, Verizon being really massive player in that whole arena. It has just been sort of struck with this massive consummation we have talked about for a long time, we have talked about these remote surgery capabilities whereby you've got patients in Kenya who are being treated by an expert sitting in London or New York, and also this whole consciousness about, you know, our carbon footprint and being environmentally conscious. This pandemic has taught us all of that and brought us to the forefront of organization priorities, right? The demand. I think that's, that's a very natural consequence of everybody sitting at home. >>And the only thing that can keep things still going is this data communication, right? But I wouldn't just say that that is, what's kind of at the heart of all of this. Just imagine if we are to realize any of these targets of the world is what leadership is setting for themselves. Hey, we have to be carbon neutral by X year as a country, as a geography, et cetera, et cetera. You know, all of these things require you to have this remote working capabilities, this remote interaction, not just between humans, but machine to machine interactions. And this there's a unique value chain, which is now getting created that you've got people who are communicating with other people or communicating with other machines, but the communication is much more. I wouldn't even use the term real time because we've used real time for voice and video, et cetera. >>We're talking low latency, microsecond decision-making that can either cut somebody's, you know, um, our trees or that could actually go and remove the tumor, that kind of stuff. So that has become a reality. Everybody's asking for it, remote learning, being an extremely massive requirement where, you know, we've had to enable these, uh, these virtual classrooms ensuring the type of connectivity, ensuring the type of type of privacy, which is just so, so critical. You can't just have everybody in a go on the internet and access a data source. You have to be concerned about the integrity and security of that data as the foremost. So I think all of these things, yes, we have not been caught off guard. We were pretty forward-looking in our plans and our evolution, but yes, it's fast track the journey that we would probably believe we would have taken in three years. It has brought that down to two quarters where we've had to execute them. >>Right. Massive acceleration. All right. So you articulated the challenges really well. And a lot of the realities that many of our viewers are facing. Let's talk now about motivations, AI ops as a tool, as a catalyst for helping organizations overcome those challenges. >>So yeah. Now on that I said, you can imagine, you know, it requires microsecond decision-making which human being on this planet can do microsecond decision-making on complex network infrastructure, which is impacting end user applications, which have multitudes of effect. You know, in real life, I use the example of a remote surgeon. Just imagine that, you know, even because of you just use your signal on the quality of that communication for that microsecond, it could be the difference between killing somebody in saving somebody's life. And it's not predictable. We talk about autonomous vehicles. Uh, we talk about this transition to electric vehicles, smart motorways, et cetera, et cetera, in federal environment, how is all of that going to work? You have so many different components coming in. You don't just have a network and security anymore. You have software defined networking. That's coming, becoming a part of that. >>You have mobile edge computing that is rented for the technologies. 5g enables we're talking augmented reality. We're talking virtual reality. All of these things require that resources and why being carbon conscious. We told them we just want to build a billion data centers on this planet, right? We, we have to make sure that resources are given on demand and the best way of resources can be given on demand and could be most efficient is that the thing is being made at million microsecond and those resources are accordingly being distributed, right? If you're relying on people, sipping their coffees, having teas, talking to somebody else, you know, just being away on holiday. I don't think we're going to be able to handle that one that we have already stepped into. Verizon's 5g has already started businesses on that transformational journey where they're talking about end user experience personalization. >>You're going to have events where people are going to go, and it's going to be three-dimensional experiences that are purely customized for you. How, how does that all happen without this intelligence sitting there and a network with all of these multiple layers? So spectrum, it doesn't just need to be intuitive. Hey, this is my private IP traffic. This is public traffic. You know, it has to not be in two, or this is an application that I have to prioritize over another task to be intuitive to the criticality and the context of those transactions. Again, that's surgeons. So be it's much more important than postman setting and playing a video game. >>I'm glad that you think that that's excellent. Let's go into some specific use cases. What are some of the examples that you gave? Let's kind of dig deeper into some of the, what you think are the lowest hanging fruit for organizations kind of pan industry to go after. >>Excellent. Brian, and I think this, this like different ways to look at the lowest hanging fruit, like for somebody like revising who is a managed services provider, you know, very comprehensive medicines, but we obviously have food timing, much lower potentially for some of our customers who want to go on that journey. Right? So for them to just go and try and harness the power of the foods might be a bit higher hanging, but for somebody like us, the immediate ones would be to reduce the number of alarms that are being generated by these overlay services. You've got your basic network, then you've got your whole software defined networking on top of that, you have your hybrid clouds, you have your edge computing coming on top of that. You know? So all of that means if there's an outage on one device on the network, I want to make this very real for everybody, right? >>It's like device and network does not stop all of those multiple applications or monitoring tools from raising and raising thousands of alarm and everyone, one capacity. If people are attending to those thousands of alarms, it's like you having a police force and there's a burglary in one time and the alarm goes off and 50 bags. How, how are you kind of make the best use of your police force? You're going to go investigate 50 bags or do you want to investigate where the problem is? So it's as real as that, I think that's the first wins where people can save so much cost, which is coming from being wasted and resources running around, trying to figure stuff out immediately. I'm tied this with network and security network and security is something which has you did even the most, you know, I mean single screens in our engineering, well, we took it to have network experts, separate people, security experts, separate people to look for different things, but there are security events that can impact the performance of a network. >>And then just drop the case on the side of et cetera, which could be falsely attributed to the metric. And then if you've got multiple parties, which are then the chapter clear stakeholders, you can imagine the blame game that goes on finding fingers, taking names, not taking responsibility that don't has all this happened. This is the only way to bring it all together to say, okay, this is what takes priority. If there's an event that has happened, what is its correlation to the other downstream systems, devices, components, and these are applications. And then subsequently, you know, like isolating it to the right cost where you can most effectively resolve that problem. Thirdly, I would say on demand, virtualized resource, virtualized resources, the heart and soul, the spirit of status that you can have them on demand. So you can automate the allocation of these resources based on customer's consumption their peaks, their cramps, all of that comes in. >>You see, Hey, typically on a Wednesday, the traffic was up significantly for this particular application, you know, going to this particular data center, you could have this automated system, uh, which is just providing those resources, you know, on demand. And so it is to have a much better commercial engagement with customers and just a much better service assurance model. And then one more thing on top of that, which is very critical is that as I was saying, giving that intelligence to the networks to start having context of the criticality of a transaction, that doesn't make sense to them. You can't have that because for that, you need to have this, you know, monkey their data. You need to have multi-cam system, which are monitoring and controlling different aspects of your overall end user application value chain to be communicating with each other. And, you know, that's the only way to sort of achieve that goal. And that only happens with AI. It's not possible >>So it was when you clearly articulated some obvious, low hanging fruit and use cases that organizations can go after. Let's talk now about some of the considerations, you talked about the importance of a network and AI ops, the approach I assume, needs to be modular support needs to be heterogeneous. Talk to us about some of those key considerations that you would recommend. >>Absolutely. So again, basically starting with the network, because if there's, if the metrics sitting at the middle of all of this is not working, then things can communicate with each other, right? And the cloud doesn't work, nothing metal. That's the hardest part of this, but that's the frequency. When you talk about machine to machine communication or IOT, it's just the biggest transformation of the span of every company is going for IOT now to drive those costs, efficiencies, and had, something's got some experience, the integrity of the topic karma, right? The security, integrity of that. How do you maintain integrity of your data beyond just a secure network components? That is true, right? That's where you're getting to the whole arena blockchain technologies, where you have to use digital signatures or barcodes that machine then, and then an intelligence system is automatically able to validate and verify the integrity of the data and the commands that are being executed by those end-user told them what I need to tell them that. >>So it's IOT machines, right? That is paramount. And if anybody is not keeping that into their equation, that in its own self is any system that is therefore maintaining the integrity of your commands and your hold that sits on those, those machines. Right? Second, you have your network. You need to have any else platform, which is able to restless all the fast network information, et cetera. And coupled with that data integrity piece, because for the management, ultimately they need to have a coherent view of the analytics, et cetera, et cetera. They need to know where the problems are again, right? So let's say if there's a problem with the integrity of the commands that are being executed by the machine, that's a much bigger problem than not being able to communicate with that machine and the best thing, because you'd rather not talk to the machine or have to do anything if it's going to start doing wrong things. >>So I think that's where it is. It's very intuitive. It's not true. You have to have subsequently if you have some kind of faith and let me use that use case self autonomous vehicles. Again, I think we're going to see in the next five years, because he's smart with the rates, et cetera, it won't separate autonomous cars. It's much more efficient, it's much more space, et cetera, et cetera. So within that equation, you're going to have systems which will be specialists in looking at aspects and transactions related to those systems. For example, in autonomous moving vehicles, brakes are much more important than the Vipers, right? So this kind of intelligence, it will be multiple systems who have to sit, N nobody has to, one person has to go in one of these systems. I think these systems should be open source enough that they, if you were able to integrate them, right, if something's sitting in the cloud, you were able to integrate for that with obviously the regard of the security and integrity of your data that has to traverse from one system to the other extremely important. >>So I'm going to borrow that integrity theme for a second, as we go into our last question, and that is this kind of take a macro look at the overall business impact that AI ops can help customers make. I'm thinking of, you know, the integrity of teams aligning business in it, which we probably can't talk about enough. We're helping organizations really effectively measure KPIs that deliver that digital experience that all of us demanding consumers expect. What's the overall impact. What would you say in summary fashion? >>So I think the overall impact is a lot of costs. That's customized and businesses gives the time to the time of enterprises. Defense was inevitable. It's something that for the first time, it will come to life. And it's something that is going to, you know, start driving cost efficiencies and consciousness and awareness within their own business, which is obviously going to have, you know, it domino kind of an effect. So one example being that, you know, you have problem isolation. I talked about network security, this multi-layers architecture, which enables this new world of 5g, um, at the heart of all of it, it has to identify the problem to the source, right? Not be bogged down by 15 different things that are going wrong. What is causing those 15 things to go wrong, right? That speed to isolation in its own sense can make millions and millions of dollars to organizations after we organize it. Next one is obviously overall impacted customer experience. Uh, 5g was given out of your customers, expecting experiences from you, even if you're not expecting to deliver them in 2021, 2022, it would have customers asking for those experience or walking away, if you do not provide those experience. So it's almost like a business can do nothing every year. They don't have to reinvest if they just want to die on the line, businesses want remain relevant. >>Businesses want to adopt the latest and greatest in technology, which enables them to, you know, have that superiority and continue it. So from that perspective that continue it, he will read that they write intelligence systems that tank rationalizing information and making decisions supervised by people, of course were previously making some of those. >>That was a great summary because you're right, you know, with how demanding consumers are. We don't get what we want quickly. We churn, right? We go somewhere else and we could find somebody that can meet those expectations. So it has been thanks for doing a great job of clarifying the impact and the value that AI ops can bring to organizations that sounds really now is we're in this even higher demand for digital products and services, which is not going away. It's probably going to only increase it's table stakes for any organization. Thank you so much for joining me today and giving us your thoughts. >>Pleasure. Thank you. We'll be right back with our next segment. >>Digital applications and services are more critical to a positive customer and employee experience than ever before. But the underlying infrastructure that supports these apps and services has become increasingly complex and expanding use of multiple clouds, mobile and microservices, along with modern and legacy infrastructure can make it difficult to pinpoint the root cause when problems occur, it can be even more difficult to determine the business impact your problems that occur and resolve them efficiently. AI ops from Broadcom can help first by providing 360 degree visibility, whether you have hybrid cloud or a cloud native AI ops from Broadcom provides a clear line of sight, including apt to infrastructure and network visibility across hybrid environments. Second, the solution gives you actionable insights by correlating an aggregating data and applying AI and machine learning to identify root causes and even predict problems before users are impacted. Third AI ops from Broadcom provides intelligent automation that identifies potential solutions when problems occur applied to the best one and learns from the effectiveness to improve response in case the problem occurs. Again, finally, the solution enables organizations to achieve digit with jelly by providing feedback loops across development and operations to allow for continuous improvements and innovation through these four capabilities. AI ops from Broadcom can help you reduce service outages, boost, operational efficiency, and effectiveness and improve customer and employee experience. To learn more about AI ops from Broadcom, go to broadcom.com/ai ops from around the globe. >>It's the cube with digital coverage of AI ops virtual forum brought to you by Broadcom. >>Welcome back to the AI ops virtual forum, Lisa Martin here with Srinivasan, Roger Rajagopal, the head of product and strategy at Broadcom. Raj, welcome here, Lisa. I'm excited for our conversation. So I wanted to dive right into a term that we hear all the time, operational excellence, right? We hear it everywhere in marketing, et cetera, but why is it so important to organizations as they head into 2021? And tell us how AI ops as a platform can help. >>Yeah. Well, thank you. First off. I wanna, uh, I want to welcome our viewers back and, uh, I'm very excited to, uh, to share, um, uh, more info on this topic. You know, uh, here's what we believe as we work with large organizations, we see all our organizations are poised to get out of the, uh, the pandemic and look for a brood for their own business and helping customers get through this tough time. So fiscal year 2021, we believe is going to be a combination of, uh, you know, resiliency and agility at the, at the same time. So operational excellence is critical because the business has become more digital, right? There are going to be three things that are going to be more sticky. Uh, you know, remote work is going to be more sticky, um, cost savings and efficiency is going to be an imperative for organizations and the continued acceleration of digital transformation of enterprises at scale is going to be in reality. So when you put all these three things together as a, as a team that is, uh, you know, that's working behind the scenes to help the businesses succeed, operational excellence is going to be, make or break for organizations, >>Right with that said, if we kind of strip it down to the key capabilities, what are those key capabilities that companies need to be looking for in an AI ops solution? >>Yeah, you know, so first and foremost, AI ops means many things to many, many folks. So let's take a moment to simply define it. The way we define AI ops is it's a system of intelligence, human augmented system that brings together full visibility across app infra and network elements that brings together disparate data sources and provides actionable intelligence and uniquely offers intelligent automation. Now, the, the analogy many folks draw is the self-driving car. I mean, we are in the world of Teslas, uh, but you know, uh, but self-driving data center is it's too far away, right? Autonomous systems are still far away. However, uh, you know, application of AI ML techniques to help deal with volume velocity, veracity of information, uh, is, is critical. So that's how we look at AI ops and some of the key capabilities that we, uh, that we, uh, that we work with our customers to help them on our own for eight years. >>Right? First one is eyes and ears. What we call full stack observability. If you do not know what is happening in your systems, uh, you know, that that serve up your business services. It's going to be pretty hard to do anything, uh, in terms of responsiveness, right? So from stack observability, the second piece is what we call actionable insights. So when you have disparate data sources, tools, sprawls data coming at you from, uh, you know, uh, from a database systems, it systems customer management systems, ticketing systems. How do you find the needle from the haystack? And how do you respond rapidly from a myriad of problems as CEO of red? The third area is what we call intelligent automation. Well, identifying the problem to act on is important, and then acting on automating that and creating, uh, a recommendation system where, uh, you know, you can be proactive about it is even more important. And finally, all of this focuses on efficiency. What about effectiveness? Effectiveness comes when you create a feedback loop, when what happens in production is related to your support systems and your developers so that they can respond rapidly. So we call that continuous feedback. So these are the four key capabilities that, uh, you know, uh, you should look for in an AI ops system. And that's what we offer as well. >>Russia, there's four key capabilities that businesses need to be looking for. I'm wondering how those help to align business. And it it's, again like operational excellence. It's something that we talk about a lot is the alignment of business. And it a lot more challenging, easier said than done, right. But I want you to explain how can AI ops help with that alignment and align it outputs to business outcomes? >>Yeah. So, you know, one of the things, uh, I'm going to say something that is, uh, that is, uh, that is simple, but, but, but this harder, but alignment is not on systems alignment is with people, right? So when people align, when organizations align, when cultures align, uh, dramatic things can happen. So in the context of AI ops VC, when, when SRE is aligned with the DevOps engineers and information architects and, uh, uh, you know, it operators, uh, you know, they enable organizations to reduce the gap between intent and outcome or output and outcome that said, uh, you know, these personas need mechanisms to help them better align, right. Help them better visualize, see the, you know, what we call single source of truth, right? So there are four key things that I want to call out. When we work with large enterprises, we find that customer journey alignment with the, you know, what we call it systems is critical. >>So how do you understand your business imperatives and your customer journey goals, whether it is car to a purchase or whether it is, uh, you know, bill shock scenarios and Swan alignment on customer journey to your it systems is one area that you can reduce the gap. The second area is how do you create a scenario where your teams can find problems before your customers do right outage scenarios and so on. So that's the second area of alignment. The third area of alignment is how can you measure business impact driven services? Right? There are several services that an organization offers versus an it system. Some services are more critical to the business than others, and these change in a dynamic environment. So how do you, how do you understand that? How do you measure that and how, how do you find the gaps there? So that's the third area of alignment that we, that we help and last but not least there are, there are things like NPS scores and others that, that help us understand alignment, but those are more long-term. But in the, in the context of, uh, you know, operating digitally, uh, you want to use customer experience and business, uh, you know, a single business outcome, uh, as a, as a key alignment factor, and then work with your systems of engagement and systems of interaction, along with your key personas to create that alignment. It's a people process technology challenge. >>So, whereas one of the things that you said there is that it's imperative for the business to find a problem before a customer does, and you talked about outages there, that's always a goal for businesses, right. To prevent those outages, how can AI ops help with that? Yeah, >>So, you know, outages, uh, talk, you know, go to resiliency of a system, right? And they also go to, uh, uh, agility of the same system, you know, if you're a customer and if you're whipping up your mobile app and it takes more than three milliseconds, uh, you know, you're probably losing that customer, right. So outages mean different things, you know, and there's an interesting website called down detector.com that actually tracks all the old pages of publicly available services, whether it's your bank or your, uh, you know, tele telecom service or a mobile service and so on and so forth. In fact, the key question around outages for, from, uh, from, uh, you know, executives are the question of, are you ready? Right? Are you ready to respond to the needs of your customers and your business? Are you ready to rapidly resolve an issue that is impacting customer experience and therefore satisfaction? >>Are you creating a digital trust system where customers can be, you know, um, uh, you know, customers can feel that their information is secure when they transact with you, all of these, getting into the notion of resiliency and outages. Now, you know, one of the things that, uh, that I, I often, uh, you know, work with customers around, you know, would that be find as the radius of impact is important when you deal with outages? What I mean by that is problems occur, right? How do you respond? How quickly do you take two seconds, two minutes, 20 minutes, two hours, 20 hours, right? To resolve the problem that radius of impact is important. That's where, you know, you have to bring a gain people, process technology together to solve that. And the key thing is you need a system of intelligence that can aid your teams, you know, look at the same set of parameters so that you can respond faster. That's the key here. >>We look at digital transformation at scale. Raj, how does AI ops help influence that? >>You know, um, I'm going to take a slightly long-winded way to answer this question. See when it comes to digital transformation at scale, the focus on business purpose and business outcome becomes extremely critical. And then the alignment of that to your digital supply chain, right, are the, are the, are the key factors that differentiate winners in the, in their digital transformation game? Really, what we have seen, uh, with, with winners is they operate very differently. Like for example, uh, you know, Nike matures, its digital business outcomes by shoes per second, right? Uh, Apple by I-phones per minute, Tesla by model threes per month, are you getting this, getting it right? I mean, you want to have a clear business outcome, which is a measure of your business, uh, in effect, I mean, ENC, right? Which, which, uh, um, my daughter use and I use very well. >>Right. Uh, you know, uh, they measure by revenue per hour, right? I mean, so these are key measures. And when you have a key business outcome measure like that, you can everything else, because you know what these measures, uh, you know, uh, for a bank, it may be deposits per month, right now, when you move money from checking account to savings account, or when you do direct deposits, those are, you know, banks need liquidity and so on and so forth. But, you know, the, the key thing is that single business outcome has a Starburst effect inside the it organization that touches a single money moment from checking a call to savings account can touch about 75 disparate systems internally. Right? So those think about it, right? I mean, all, all we're doing is moving money from checking account a savings account. Now that goats into a it production system, there are several applications. >>There is a database, there is, there are infrastructures, there are load balancers that are webs. You know, you know, the web server components, which then touches your, your middleware component, which is a queuing system, right. Which then touches your transactional system. Uh, and, uh, you know, which may be on your main frames, what we call mobile to mainframe scenario, right? And we are not done yet. Then you have a security and regulatory compliance system that you have to touch a fraud prevention system that you have to touch, right? A state department regulation that you may have to meet and on and on and on, right? This is the chat that it operations teams face. And when you have millions of customers transacting, right, suddenly this challenge cannot be managed by human beings alone. So therefore you need a system of intelligence that augments human intelligence and acts as your, you know, your, your eyes and ears in a way to, to point pinpoint where problems are. >>Right. So digital transformation at scale really requires a very well thought out AI ops system, a platform, an open extensible platform that, uh, you know, uh, that is heterogeneous in nature because there's tools, products in organizations. There is a lot of databases in systems. There are millions of, uh, uh, you know, customers and hundreds of partners and vendors, you know, making up that digital supply chain. So, you know, AI ops is at the center of an enabling an organization achieve digital op you know, transformation at scale last but not least. You need continuous feedback loop. Continuous feedback loop is the ability for a production system to inform your dev ops teams, your finance teams, your customer experience teams, your cost modeling teams about what is going on so that they can so that they can reduce the intent, come gap. >>All of this need to come together, what we call BizOps. >>That was a great example of how you talked about the Starburst effect. I actually never thought about it in that way, when you give the banking example, but what you should is the magnitude of systems. The fact that people alone really need help with that, and why intelligent automation and AI ops can be transformative and enable that scale. Raj, it's always a pleasure to talk with you. Thanks for joining me today. And we'll be right back with our next segment. Welcome back to the AI ops virtual forum. We've heard from our guests about the value of AI ops and why and how organizations are adopting AI ops platforms. But now let's see AI ops inaction and get a practical view of AI ops to deep Dante. The head of AI ops at Broadcom is now going to take you through a quick demo. >>Hello. So they've gotta head off AI ops and automation here. What I'm going to do today is talk through some of the key capabilities and differentiators of Broadcom's CII ops solution in this solution, which can be delivered on cloud or on-prem. We bring a variety of metric alarm log and applauded data from multiple sources, EPM, NetApps, and infrastructure monitoring tools to provide a single point of observability and control. Let me start where our users mostly stock key enterprises like FSI, telcos retailers, et cetera, do not manage infrastructure or applications without having a business context. At the end of the day, they offer business services governed by SLS service level objectives and SLI service level indicators are service analytics, which can scale to a few thousand services, lets our customers create and monitor the services as per their preference. They can create a hierarchy of services based on their business practice. >>For example, here, the sub services are created based on functional subsistence for certain enterprises. It could be based on location. Users can import these services from their favorite CMDB. What's important to note that not all services are born equal. If you are a modern bank, you may want to prioritize tickets coming from digital banking, for example, and this application lets you rank them as per the KPI of your choice. We can source the availability, not merely from the state of the infrastructure, whether they're running or not. But from the SLS that represent the state of the application, when it comes to triaging issues related to the service, it is important to have a complete view of the topology. The typology can show both east-west elements from mobile to mainframe or not South elements in a network flow. This is particularly relevant for a large enterprise who could be running the systems of engagement on the cloud and system of records on mainframe inside the firewall here, you can see that the issue is related to the mainframe kick server. >>You can expand to see the actual alarm, which is sourced from the mainframe operational intelligence. Similarly, clicking on network will give the hub and spoke view of the network devices, the Cisco switches and routers. I can click on the effected router and see all the details Broadcom's solution stores, the ontological model of the typology in the form of a journal graph where one can not only view the current state of the typology, but the past as well, talking of underlying data sources, the solution uses best of the pre data stores for structured and unstructured data. We have not only leveraged the power of open source, but have actively contributed back to the community. One of the key innovations is evident in our dashboarding framework because we have enhanced the open source Grafana technology to support these diverse data sources here. You can see a single dashboard representing applications to infrastructure, to mainframe again, sourcing a variety of data from these sources. >>When we talk to customers, one of the biggest challenges that they face today is related to alarms because of a proliferation of tools. They are currently drowning in an ocean of hundreds and thousands of alarms. This increases the Elmont support cost to tens of dollars per ticket, and also affects LTO efficiency leading to an average of five to six hours of meantime to resolution here is where we have the state of the art innovation utilizing the power of machine learning and ontology to arrive at the root cause we not only clusterize alarms based on text, but employ the technique of 41st. We look at the topology then at the time window duplicate text based on NLP. And lastly learn from continuous training of the model to deduce what we call situations. This is an example of a situation. As you can see, we provide a time-based evidence of how things unfolded and arrive at a root cause. >>Lastly, the solution provides a three 60 degree closed loop remediation either through a ticketing system or by direct invocation of automation actions instead of firing hard-coded automation runbooks for certain conditions, the tool leverage is machine learning to rank automation actions based on past heuristics. That's why we call it intelligent automation to summarize AI ops from Broadcom helps you achieve operational excellence through full stack observability, coupled with AIML that applies across modern hybrid cloud environments, as well as legacy ones uniquely. It ties these insights with intelligent automation to improve customer experience. Thank you for watching from around the globe. It's the cube with digital coverage of AI ops virtual forum brought to you by Broadcom. >>Welcome to our final segment today. So we've discussed today. The value that AI ops will bring to organizations in 2021, we'll discuss that through three different perspectives. And so now we want to bring those perspectives together and see if we can get a consensus on where AI ops needs to go for folks to be successful with it in the future. So bringing back some folks Richland is back with us. Senior analysts, serving infrastructure and operations professionals at Forrester smartness here is also back in global product management at Verizon and Srinivasan, Reggie Gopaul head of product and strategy at Broadcom guys. Great to have you back. So let's jump in and rich, we're going to, we're going to start with you, but we are going to get all three of you, a chance to answer the questions. So we've talked about why organizations should adopt AI ops, but what happens if they choose not to what challenges would they face? Basically what's the cost of organizations doing nothing >>Good question, because I think in operations for a number of years, we've kind of stand stood, Pat, where we are, where we're afraid change things sometimes, or we just don't think about a tooling as often. The last thing to change because we're spending so much time doing project work and modernization and fighting fires on a daily basis. >>Problem is going to get worse. If we do nothing, >>You know, we're building new architectures like containers and microservices, which means more things to mind and keep running. Um, we're building highly distributed systems. We're moving more and more into this hybrid world, a multi-cloud world, uh, it's become over-complicate and I'll give a short anecdote. I think, eliminate this. Um, when I go to conferences and give speeches, it's all infrastructure operations people. And I say, you know, how many people have three X, five X, you know, uh, things to monitor them. They had, you know, three years ago, two years ago, and everyone's saying how many people have hired more staff in that time period, zero hands go up. That's the gap we have to fill. And we have to fill that through better automation, more intelligent systems. It's the only way we're going to be able to fill back out. >>What's your perspective, uh, if organizations choose not to adopt AI ops. Yeah. So I'll do that. Yeah. So I think it's, I would just relate it to a couple of things that probably everybody >>Tired off lately and everybody can relate to. And this would resonate that we have 5g, which is all set to transform the world. As we know it, I don't have a lot of communication with these smart cities, smart communities, IOT, which is going to make us pivotal to the success of businesses. And as you've seen with this call with, you know, transformation of the world, that there's a, there's a much bigger cost consciousness out there. People are trying to become much more, forward-looking much more sustainable. And I think at the heart of all of this, that the necessity that you have intelligent systems, which are bastardizing more than enough information that previously could've been overlooked because if you don't measure engagement, not going right. People not being on the same page of this using two examples or hundreds of things, you know, that play a part in things, but not coming together in the best possible way. So I think it has an absolute necessity to drive those cost efficiencies rather than, you know, left right and center laying off people who are like 10 Mattel to your business and have a great tribal knowledge of your business. So to speak, you can drive these efficiencies through automating a lot of those tasks that previously were being very manually intensive or resource intensive. And you could allocate those resources towards doing much better things, which let's be very honest going into 20, 21 after what we've seen with 2020, it's going to be mandate treat. >>And so Raj, I saw you shaking your head there when he was mom was sharing his thoughts. What are your thoughts about that sounds like you agree. Yeah. I mean, uh, you know, uh, to put things in perspective, right? I mean we're firmly in the digital economy, right? Digital economy, according to the Bureau of economic analysis is 9% of the U S GDP. Just, you know, think about it in, in, in, in, in the context of the GDP, right? It's only ranked lower, slightly lower than manufacturing, which is at 11.3% GDP and slightly about finance and insurance, which is about seven and a half percent GDP. So the digital economy is firmly in our lives, right. And as Huisman was talking about it, you know, software eats the world and digital, operational excellence is critical for customers, uh, to, uh, you know, to, uh, to drive profitability and growth, uh, in the digital economy. >>It's almost, you know, the key is digital at scale. So when, uh, when rich talks about some of the challenges and when Huseman highlights 5g as an example, those are the things that, that, that come to mind. So to me, what is the cost or perils of doing nothing? You know, uh, it's not an option. I think, you know, more often than not, uh, you know, C-level execs are asking head of it and they are key influencers, a single question, are you ready? Are you ready in the context of addressing spikes in networks because of the pandemic scenario, are you ready in the context of automating away toil? Are you ready to respond rapidly to the needs of the digital business? I think AI ops is critical. >>That's a great point. Roger, where does stick with you? So we got kind of consensus there, as you said, wrapping it up. This is basically a, not an option. This is a must to go forward for organizations to be successful. So let's talk about some quick wins, or as you talked about, you know, organizations and sea levels asking, are you ready? What are some quick wins that that organizations can achieve when they're adopting AI? >>You know, um, immediate value. I think I would start with a question. How often do your customers find problems in your digital experience before you do think about that? Right. You know, if you, if you, you know, there's an interesting web, uh, website, um, uh, you know, down detector.com, right? I think, uh, in, in Europe there is an equal amount of that as well. It ha you know, people post their digital services that are down, whether it's a bank that, uh, you know, customers are trying to move money from checking account, the savings account and the digital services are down and so on and so forth. So some and many times customers tend to find problems before it operations teams do. So a quick win is to be proactive and immediate value is visibility. If you do not know what is happening in your complex systems that make up your digital supply chain, it's going to be hard to be responsive. So I would start there >>Visibility this same question over to you from Verizon's perspective, quick wins. >>Yeah. So I think first of all, there's a need to ingest this multi-care spectrum data, which I don't think is humanly possible. You don't have people having expertise, you know, all the seven layers of the OSI model and then across network and security and at the application level. So I think you need systems which are now able to get that data. It shouldn't just be wasted reports that you're paying for on a monthly basis. It's about time that you started making the most of those in the form of identifying what are the efficiencies within your ecosystem. First of all, what are the things, you know, which could be better utilized subsequently you have the >>Opportunity to reduce the noise of a trouble tickets handling. It sounds pretty trivial, but >>An average you can imagine every trouble tickets has the cost in dollars, right? >>So, and there's so many tickets and there's art >>That get created on a network and across an end user application value, >>We're talking thousands, you know, across and end user >>Application value chain could be million in >>A year. So, and so many of those are not really, >>He, you know, a cause of concern because the problem is something. >>So I think that whole triage is an immediate cost saving and the bigger your network, the bigger >>There's a cost of things, whether you're a provider, whether you're, you know, the end customer at the end of the day, not having to deal with problems, which nobody can resolve, which are not meant to be dealt with. There's so many of those situations, right, where service has just been adopted, >>Which is just coordinate quality, et cetera, et cetera. So many reasons. So those are the, >>So there's some of the immediate cost saving them. They are really, really significant. >>Secondly, I would say Raj mentioned something about, you know, the user, >>Your application value chain, and an understanding of that, especially with this hybrid cloud environment, >>Et cetera, et cetera, right? The time it takes to identify a problem in an end user application value chain across the seven layers that I mentioned with the OSI reference model across network and security and the application environment. It's something that >>In its own self has massive cost to business, >>Right? That could be >>No sale transactions that could be obstructed because of this. There could be, and I'm going to use a really interesting example. >>We talk about IOT. The integrity of the IOT machine is exciting. >>Family is pivotal in this new world that we're stepping into. >>You could be running commands, >>Super efficient. He has, everything is being told to the machine really fast with sending yeah. >>Everything there. What if it's hacked? And if that's okay, >>Robotic arm starts to involve the things you don't want it to do. >>So there's so much of that. That becomes a part of this naturally. And I believe, yes, this is not just like from a cost >>standpoint, but anything going wrong with that code base, et cetera, et cetera. These are massive costs to the business in the form of the revenue. They have lost the perception in the market as a result, the fed, >>You know, all that stuff. So >>These are a couple of very immediate problems, but then you also have the whole player virtualized resources where you can automate the allocation, you know, the quantification of an orchestration of those virtualized resources, rather than a person having to, you know, see something and then say, Oh yeah, I need to increase capacity over here, because then it's going to have this particular application. You have systems doing this stuff and to, you know, Roger's point your customer should not be identifying your problems before you, because this digital is where it's all about perception. >>Absolutely. We definitely don't want the customers finding it before. So rich, let's wrap this particular question up with you from that senior analyst perspective, how can companies use make big impact quickly with AI ops? Yeah, >>Yeah, I think, you know, and it was been really summed up some really great use cases there. I think with the, uh, you know, one of the biggest struggles we've always had in operations is isn't, you know, the mean time to resolve. We're pretty good at resolving the things. We just have to find the thing we have to resolve. That's always been the problem and using these advanced analytics and machine learning algorithms now across all machine and application data, our tendency is humans is to look at the console and say, what's flashing red. That must be what we have to fix, but it could be something that's yellow, somewhere else, six services away. And we have made things so complicated. And I think this is what it was when I was saying that we can't get there anymore on our own. We need help to get there in all of this stuff that the outline. >>So, so well builds up to a higher level thing of what is the customer experience about what is the customer journey? And we've struggled for years in the digital world and measuring that a day-to-day thing. We know an online retail. If you're having a bad experience at one retailer, you just want your thing. You're going to go to another retailer, brand loyalty. Isn't one of like it, wasn't a brick and mortal world where you had a department store near you. So you were loyal to that because it was in your neighborhood, um, online that doesn't exist anymore. So we need to be able to understand the customer from that first moment, they touch a digital service all the way from their, their journey through that digital service, the lowest layer, whether it be a database or the network, what have you, and then back to them again, and we're not understanding, is that a good experience? >>We gave them. How does that compare to last week's experience? What should we be doing to improve that next week? Uh, and I think companies are starting and then the pandemic certainly, you know, push this timeline. If you listened to the, the, the CEO of Microsoft, he's like, you know, 10 years of digital transformation written down. And the first several months of this, um, in banks and in financial institutions, I talked to insurance companies, aren't slowing down. They're trying to speed up. In fact, what they've discovered is that they're, you know, obviously when we were on lockdown or what have you, they use of digital servers is spiked very high. What they've learned is they're never going to go back down. They're never going to return to pretend endemic levels. So now they're stuck with this new reality. Well, how do we service those customers and how do we make sure we keep them loyal to our brand? >>Uh, so, you know, they're looking for modernization opportunities. A lot of that that's things have been exposed. And I think Raj touched upon this very early in the conversation is visibility gaps. Now that we're on the outside, looking in at the data center, we know we architect things in a very way. Uh, we better ways of making these correlations across the Sparrow technologies to understand where the problems lies. We can give better services to our customers. And I think that's really what we're going to see a lot of the innovation and the people really clamoring for these new ways of doing things that starting, you know, now, I mean, I've seen it in customers, but I think really the push through the end of this year to next year when, you know, economy and things like that straightened out a little bit more, I think it really, people are gonna take a hard look of where they are and is, you know, AI ops the way forward for them. And I think they'll find it. The answer is yes, for sure. >>So we've, we've come to a consensus that, of what the parallels are of organizations, basically the cost of doing nothing. You guys have given some great advice on where some of those quick wins are. Let's talk about something Raj touched on earlier is organizations, are they really ready for truly automated AI? Raj, I want to start with you readiness factor. What are your thoughts? >>Uh, you know, uh, I think so, you know, we place our, her lives on automated systems all the time, right? In our, in our day-to-day lives, in the, in the digital world. I think, uh, you know, our, uh, at least the customers that I talk to our customers are, uh, are, uh, you know, uh, have a sophisticated systems. Like for example, advanced automation is a reality. If you look at social media, AI and ML and automation are used to automate away, uh, misinformation, right? If you look at financial institutions, AI and ML are used to automate away a fraud, right? So I want to ask our customers why can't we automate await oil in it, operation systems, right? And that's where our customers are. Then the, you know, uh, I'm a glass half full, uh, cleanup person, right? Uh, this pandemic has been harder on many of our customers, but I think what we have learned from our customers is they've Rose to the occasion. >>They've used digital as a key needs, right? At scale. That's what we see with, you know, when, when Huseman and his team talk about, uh, you know, network operational intelligence, right. That's what it means to us. So I think they are ready, the intersection of customer experience it and OT, operational technology is ripe for automation. Uh, and, uh, you know, I, I wanna, I wanna sort of give a shout out to three key personas in this mix. It's about people, right? One is the SRE persona, you know, site, reliability engineer. The other is the information security persona. And the third one is the it operator automation engineer persona. These folks in organizations are building a system of intelligence that can respond rapidly to the needs of their digital business. We at Broadcom, we are in the business of helping them construct a system of intelligence that will create a human augmented solution for them. Right. So when I see, when I interact with large enterprise customers, I think they, they, you know, they, they want to achieve what I would call advanced automation and AI ML solutions. And that's squarely, very I ops is, you know, is going as it, you know, when I talk to rich and what, everything that rich says, you know, that's where it's going and that's what we want to help our customers to. So, which about your perspective of organizations being ready for truly automated AI? >>I think, you know, the conversation has shifted a lot in the last, in, in pre pandemic. Uh, I'd say at the end of last year, we're, you know, two years ago, people I'd go to conferences and people come up and ask me like, this is all smoke and mirrors, right? These systems can't do this because it is such a leap forward for them, for where they are today. Right. We we've sort of, you know, in software and other systems, we iterate and we move forward slowly. So it's not a big shock. And this is for a lot of organizations that big, big leap forward where they're, they're running their operations teams today. Um, but now they've come around and say, you know what? We want to do this. We want all the automations. We want my staff not doing the low complexity, repetitive tasks over and over again. >>Um, you know, and we have a lot of those kinds of legacy systems. We're not going to rebuild. Um, but they need certain care and feeding. So why are we having operations? People do those tasks? Why aren't we automating those out? I think the other piece is, and I'll, I'll, I'll send this out to any of the operations teams that are thinking about going down this path is that you have to understand that the operations models that we're operating under in, in INO and have been for the last 25 years are super outdated and they're fundamentally broken for the digital age. We have to start thinking about different ways of doing things and how do we do that? Well, it's, it's people, organization, people are going to work together differently in an AI ops world, um, for the better. Um, but you know, there's going to be the, the age of the 40 person bridge call thing. >>Troubleshooting is going away. It's going to be three, four, five focused engineers that need to be there for that particular incident. Um, a lot of process mailer process we have in our level, one level, two engineering. What have you running of tickets, gathering of artifacts, uh, during an incident is going to be automated. That's a good thing. We should be doing those, those things by hand anymore. So I'd say that the, to people's like start thinking about what this means to your organization. Start thinking about the great things we can do by automating things away from people, having to do them over and over again. And what that means for them, getting them matched to what they want to be doing is high level engineering tasks. They want to be doing monitorization, working with new tools and technologies. Um, these are all good things that help the organization perform better as a whole great advice and great kind of some of the thoughts that you shared rich for what the audience needs to be on the lookout. For one, I want to go over to you, give me your thoughts on what the audience that should be on the lookout for, or put on your agendas in the next 12 months. >>So there's like a couple of ways to answer that question. One thing would be in the form of, you know, what are some of the things they have to be concerned about in terms of implementing this solution or harnessing its power. The other one could be, you know, what are the perhaps advantages they should look to see? So if I was to talk about the first one, let's say that, what are some of the things I have to watch out for like possible pitfalls that everybody has data, right? So yeah, there's one strategy we say, okay, you've got the data, let's see what we can do with them. But then there's the exact opposite side, which has to be considered when you're doing that analysis. What are the use cases that you're looking to drive? Right. But then use cases you have to understand, are you taking a reactive use case approach? >>Are you taking active use cases, right? Or, yeah, that's a very, very important concentration. Then you have to be very cognizant of where does this data that you have, where does it reside? What are the systems and where does it need to go to in order for this AI function to happen and subsequently if there needs to be any backward communication with all of that data in a process manner. So I think these are some of the very critical points because you can have an AI solution, which is sitting in a customer data center. It could be in a managed services provider data center, like, right, right. It could be in a cloud data center, like an AWS or something, or you could have hybrid views, et cetera, all of that stuff. So you have to be very mindful of where you're going to get the data from is going to go to what are the use cases you're trying to get out to do a bit of backward forward. >>Okay, we've got this data thing and I think it's a journey. Nobody can come in and say, Hey, you've built this fantastic thing. It's like Terminator two. I think it's a journey where we built starting with the network. My personal focus always comes down to the network and with 5g so much, so much more right with 5g, you're talking low latency communication. That's like the true power of 5g, right? It's low latency, it's ultra high bandwidth, but what's the point of that low latency. If then subsequently the actions that need to be taken to prevent any problems in application, IOT applications, remote surgeries, uh, self driving vehicles, et cetera, et cetera. What if that's where people are sitting and sipping their coffees and trying to take action that needs to be in low latency as well. Right? So these are, I think some of the fundamental things that you have to know your data, your use cases, that location, where it needs to be exchanged, what are the parameters around that for extending that data? >>And I think from that point at one word, it's all about realizing, you know, sense of business outcomes. Unless AI comes in as a digital labor that shows you, I have, I have reduced your this amount of time and that's a result of big problems or identified problems for anything. Or I have saved you this much resource in a month, in a year or whatever timeline that people want to see it. So I think those are some of the initial starting points, and then it all starts coming together. But the key is it's not one system that can do everything. You have to have a way where, you know, you can share data once you've caught all of that data into one system. Maybe you can send it to another system at make more, take more advantage, right? That system might be an AI and IOT system, which is just looking at all of your street and make it sure that Hey parents. So it's still off just to be more carbon neutral and all that great stuff, et cetera, et cetera, >>Stuff for the audience to can cigarette rush, take us time from here. What are some of the takeaways that you think the audience really needs to be laser focused on as we move forward into the next year? You know, one thing that, uh, I think a key takeaway is, um, uh, you know, as we embark on 2021, closing the gap between intent and outcome and outputs and outcome will become critical, is critical. Uh, you know, especially for, uh, you know, uh, digital transformation at scale for organizations context in the, you know, for customer experience becomes even more critical as who Swan Huseman was talking, uh, you know, being network network aware network availability is, is a necessary condition, but not sufficient condition anymore. Right? The what, what, what customers have to go towards is going from network availability to network agility with high security, uh, what we call app aware networks, right? How do you differentiate between a trade, a million dollar trade that's happening between, uh, you know, London and New York, uh, uh, versus a YouTube video training that an employee is going through? Worse is a YouTube video that millions of customers are, are >>Watching, right? Three different context, three different customer scenarios, right? That is going to be critical. And last but not least feedback loop, uh, you know, responsiveness is all about feedback loop. You cannot predict everything, but you can respond to things faster. I think these are sort of the three, three things that, uh, that, uh, you know, customers aren't going to have to have to really think about. And that's also where I believe AI ops, by the way, AI ops and I I'm. Yeah. You know, one of the points that was smart and shout out to what he was saying was heterogeneity is key, right? There is no homogeneous tool in the world that can solve problems. So you want an open extensible system of intelligence that, that can harness data from disparate data sources provide that visualization, the actionable insight and the human augmented recommendation systems that are so needed for, uh, you know, it operators to be successful. I think that's where it's going. >>Amazing. You guys just provided so much content context recommendations for the audience. I think we accomplished our goal on this. I'll call it power panel of not only getting to a consensus of what, where AI ops needs to go in the future, but great recommendations for what businesses in any industry need to be on the lookout for rich Huisman Raj, thank you for joining me today. We want to thank you for watching. This was such a rich session. You probably want to watch it again. Thanks for your time. Thanks so much for attending and participating in the AI OBS virtual forum. We really appreciate your time and we hope you really clearly understand the value that AI ops platforms can deliver to many types of organizations. I'm Lisa Martin, and I want to thank our speakers today for joining. We have rich lane from Forrester who's fund here from Verizon and Raj from Broadcom. Thanks everyone. Stay safe..

>>From around the globe. It's the cube with digital coverage of an AI ops virtual forum brought to you by Broadcom. >>Welcome to this preview of Broadcom's AI ops virtual forum on your host, Lisa Martin. And joining me to give you a sneak peek of this event. That's on December 3rd is streaming of Boston Rajagopal or Raj, the head of AI ops at Broadcom. Raj. This has meant it's coming up in a couple of weeks. Excited, >>Good to be here. I am excited, Lisa, uh, you know, um, customers are poised for growth, uh, in 2021. And, uh, they, uh, we believe they all, they will also come out of the pandemic to grow their business and serve, uh, their customers. Uh, you know, well, they have two key challenges. How do you grow at the same time, operate with efficiency, right? These two challenges are decision-makers are struggling with every day at scale. That is why they do digital transformation at scale. And our key influencers like it, operators and SRE personas are helping our decision-makers in our customers to drive the efficiency. They are trying to, uh, focus on converting outputs to outcomes. That's what AI ops is all about. And you're going to hear from us. >>Yeah. And we've got a panel of experts here. Rich lane, senior research analyst for Forrester is going to be joining us as well as nastier, the global product management at Verizon. And of course, Raj, you're going to be hearing some of the latest trends for AI ops. Why now is the time Raj, what are some of the key takeaways that you think those key influencers and those decision makers are going to walk away from this event? >>So the, you know, our decision makers and key influencers have a single question in mind when they deal with enterprise large enterprise scenarios, the questions that they get asked by their skill level execs are, are you ready? Are you ready? When remote work is the norm, are you ready when you have to optimize your investments? And are you ready when you have to accelerate your transformation at scale to operate as a digital enterprise, all of this requires them to think and act differently from people process technology. And how do you bring all of this together? Under the ages of what we call AI ops is what they're going to learn about. >>Another thing too, is you're going to hear the latest industry trends on AI ops from Raj and the panel of experts that mentioned a minute ago, how organizations like yours are finding value from AI ops and something that Raj talked about a minute ago is understanding why now is the time to be ready for AI ops. So Raj and I look forward to you joining us along with our other panelists, December 3rd, register for the Broadcom AI ops virtual forum today.

>>from around the globe. It's the Cube with digital coverage of AI ops Virtual Forum Brought to you by Broadcom Welcome back to the AI Ops Virtual Forum. Lisa Martin here with Srinivasan Rajagopal, the head of products and strategy at Broadcom Raj Welcome. >>Good to be here, Lisa. >>I'm excited for our conversation, so I wanted to dive right into a term that we hear all the time. Operational excellence, right? We hear it everywhere in marketing, etcetera. But why is it so important to organizations as they head into 2021 tell us how ai ops as a platform can help? >>Yeah. Thank you. First off, I wanna I wanna welcome our viewers back and I'm very excited Toe share more info on this topic. You know, here's what we believe. As we work with large organizations, we see all our organizations are poised toe get out off the pandemic and look for growth for their own business and helping customers get through this tough time. So fiscal year 2021 we believe, is going to be a combination off, you know, resiliency and agility at the at the same time. So operational excellence is critical because the business has become mawr digital, right? There are gonna be three things that are gonna be more sticky. You know, remote work is gonna be more sticky. Um, cost savings and efficiency is going to be an imperative for organizations. And the continued acceleration of digital transformation off enterprises at scale is going to be in reality. So when you put all these three things together as a team, that is, you know, that's working behind the scenes toe help the businesses succeed. Operational excellence is going to be make or break for organizations. >>Russia with that said, if we kind of strip it down to the key capabilities, what are those key capabilities that companies need to be looking for in an AI ops solution? >>Yeah, you know. So first foremost AI ops means many things to many, many folks. So let's take a moment to simply define it. The way we defined AI ops is it's a system off intelligence human augmented system that brings together full visibility across app, infra and network elements that brings together despite of data sources on provides actionable intelligence and uniquely offers intelligent automation. Now the technology many folks draw is the self driving car, right? I mean, we are in the world of Tesla's, but, you know, but self driving data center is is too far away, right? Autonomous systems are still far away. However, you know, application off the I M l techniques toe help deal with volume velocity, veracity of information. Eyes is critical. So that's how we look at AI ops and some of the key capabilities that we that we that we work with our customers to help them around 48 years. Right? First one is eyes and years. What we call full stack, observe ability. If you do not know what is happening in your systems, you know that that serve up your business services, it's gonna be pretty hard to do anything in terms of responsiveness, right? So from stack of their ability, the second piece is what we call actionable insights. So when you have disparaged data sources, tool sprawls, data coming at you from, you know from database systems, I T systems, customer management systems, ticketing systems, how do you find the needle from the haystack? And how do you respond rapidly from a myriad off problems? A sea off read The third area is what we call intelligent automation. Well, Identifying the problem toe Act on is important and then acting on. Automating that and creating a recommendation system where you know you could be proactive about that is even more important. And finally, all of this focuses on efficiency. What about effectiveness? Effectiveness comes when you create a feedback loop when what happens in production is related to your support systems and your developers so that they can respond rapidly. So we call that continuous feedback. So these are the four key capabilities that you know you should look for in an AI ops system. And that's what we offer us. >>Alright, Russia. There's four key capabilities that businesses need to be looking for. I'm wondering how those help to align business and i t. It's again like operational excellence. It's something that we talk about a lot is the alignment of business and I t a lot more challenging. Is your something done right? But I want you to explain how can a iob help with that alignment and align? I t outputs to business outcomes. >>So you know, one of the things I'm going to say something that this, that is that is simple. But it's harder. Alignment is not on systems. Alignment is with people, right? So when people align when organizations aligned, when cultures align, dramatic things can happen. So in the context off AI ops, we see when when saris aligned with the develops engineers and information architects. And, uh, you know, I t operators, you know, they enable organizations to reduce the gap between intent and outcome or output an outcome that said, you know, these personas need mechanisms toe help them better align, right, help them Better visual. I see the you know what we call single source of truth, right? So there are four key things that I wanna call out when we work with large enterprises. We find that customer journey alignment with the you know what we call I T systems is critical. So how do you understand your business imperatives and your customer journey goals? Whether it is card toe purchase or whether it is, you know, Bill shock scenarios and swan alignment on customer journey to your I T systems is one area that you can reduce the gap. The second area is how do you create a scenario where your teams can find problems before your customers do right out. It's scenarios and so on. So that's the second area off alignment. The third area off alignment is how can you measure business impact driven services right? There are several services that an organization off course as the 19 system. Some services are more critical to the business. Well, then, others and thes change in a dynamic environment. So how do you How do you understand that? How do you measure that? And how? How do you find the gaps there? So that that's the 3rd 80 off alignment that we that we help. And last but not least, there are. There are things like NPS scores and others that that help us understand alignment. But those are more long term. But in the in the context off, you know, operating digitally. You want to use customer experience and, you know single business outcome as as a key alignment factor, and then work with your systems of engagement and systems of interaction, along with your key personas to create that alignment. It's a people process technology challenge, actually. >>So where is one of the things that you said there is that it's imperative for the business toe. Find a problem before a customer does. And you talked about outages there. That's always a goal for businesses, right to prevent those outages. How can Ai ops help with that? >>Yeah, so, you know, out they just talk, you know, go to resiliency off a system, right? And they also goto have, you know, agility off the same system. You know, if you are a customer and if you're ripping up your mobile happened, it takes more than you know, three milliseconds. You know, you're probably losing that customer, right? So I would just mean different things, you know? And there's an interesting website called don't detector dot com that actually tracks all the outages of publicly available services, whether it's your bank or your, you know, telecom service or mobile service and so on and so forth. In fact, the key question around outages for from from you know, executives are the question of Are you ready? Right? Are you ready to respond to the needs off your customers and your business? Are you ready toe rapidly to solve an issue that is impacting customer experience and therefore satisfaction. Are you creating a digital trust system where customers can be, You know, you know, customers can feel that their information is secure when they transact with you. All of these getting toe the notion of resiliency and outages. Now, you know, one of the things that I often you know work with customers around, you know, that we find is the radius off. Impact is important when you deal with outages. What I mean by that is problems occur, right? How do you respond? How quickly do you take? Two seconds? Two minutes, 20 minutes. Two hours, 20 hours. Right To resolve that problem. That radius of impact is important. That's where you know you have to bring again. People process technology together to solve that. And the key thing is, you need a system of intelligence that can aid you your teams, you know, look at the same set of parameters so that you can respond faster. That's the key here. >>But as we look at digital transformation at scale, Raj, how does a apps help influence that? >>You know, I'm gonna take a slightly long winded way to answer this question. See, when it comes to digital transformation at scale, the focus on business purpose and business outcome becomes extremely critical. And then the alignment off that to your digital supply chain right are the are the are the key factors that differentiate vintners in the in their digital transformation game. Really? What we have seen with with winners is they operate very differently. Like, for example, you know, 19 assures its digital business outcomes by shoes per second, right apple buy iPhones per per minute. Tesla by model threes per month. Are you getting getting it right? I mean, you wanna have, ah, clear business outcome, which is a measure off your business. In effect, I mean, easy right, which which my daughter use. And I use very well, right? You know, they measured by revenue per hour, right? I mean, so these are key measures, and when you have a key business outcome measure like that, you can align everything else because you know what these measures you know, for a bank, it may be deposits per month. Right now, when you move money from checking account to savings account or when you do direct deposits, those are you know, banks need liquidity and so on and so forth. But, you know, the key thing is that single business outcome has a starburst effect inside the I T. Organization that touches a single money movement from checking account to savings account can touch about 75 disparage systems internally. Right? So those think about right. I mean, all we're doing is moving money from checking accounts savings account. Now that goats in tow, a IittIe production system, there are several applications. There is a database there is there are infrastructures, their load balancers, that our webs, you know, the Web server components, which then touches your your middleware component, which is a queuing system right, which then touches your transactional system on. Do you know which may be on your mainframes what we call mobile toe mainframe scenario, right? And we're not done yet. Then you have a security and regulatory compliance system that you have to touch a fraud prevention system that you have to touch right, a State Department regulation that you may have to meet and on and on and on, right? This is the challenge that I t operation teams phase. And when you have millions of customers transacting right? Certainly this challenge cannot be, you know, managed by, you know, human beings alone. So therefore, you need a system off intelligence that augments human intelligence and acts as you, you know, your your eyes and ears in of a toe point pinpoint. Their problems are right. So digital transformation at scale really requires a well thought out ai ops system a platform and open extensible platform that you know, that is heterogeneous in nature because their stools problems in organizations. There is, uh, you know, a lot of data bases in systems. There are million's off, you know, customers and hundreds off partners and vendors, you know, making up that digital supply chain. So, you know, AI ops is at the center off, enabling an organization achieved digital up, you know, transformation at scale. Last but not least, you need continuous feedback loop. Continuous feedback loop is the ability for a production system toe. Inform your develops teams your finance teams, your customer experience teams your cost Modeling teams about what is going on say that they can so that they can reduce the intent outcome gap. All of this need to come together. What we call biz obs for ideal abs. >>That was a great example of how you talked about the Starburst effect. Actually never thought about it in that way. When you give the banking example but what you should is the magnitude of systems, the fact that people alone really need help with that and why intelligent automation and air ops could be transformative and enable that scale. Raj, it's always a pleasure to talk with you. Thanks for joining me today. Yeah, >>great to be here >>and we'll be right back with our next segment.

>>From around the globe with digital coverage brought to you by Broadcom. >>So our final segment today, so we've discussed today, the value that AI ops will bring to organizations in 2021, we'll discuss that through three different perspectives. And so now we want to bring those perspectives together and see if we can get a consensus on where AI ops needs to go for folks to be successful with it in the future. So bringing back some folks Richland is back with us. Senior analysts, serving infrastructure and operations professionals at Forester with smartness here is also back global product management at Verizon and Srinivasan, Reggie Gopaul head of product and strategy at Broadcom guys. Great to have you back. So let's jump in and Richard, we're going to, we're going to start with you, but we are going to get all three of you, a chance to answer the questions. So rich, we've talked about why organizations should adopt AI ops, but what happens if they choose not to what challenges would they face? Basically what's the cost of organizations doing nothing. >>Yeah. So it's a really good question because I think in operations for a number of reviews, we've kind of stand, uh, stood Pat, where we are, where we're afraid change things sometimes. Or we just don't think about a tooling is often the last thing to change because we're spending so much time doing project work and modernization and fighting fires on a daily basis. Uh, that problem is going to get worse if we do nothing. Um, you know, we're building new architectures like containers and microservices, which means more things to mind and keep running. Um, we're building highly distributed systems where you got moving more and more into this hybrid world, the multicloud world, uh, it's become over-complicate and I'll give a short anecdote. I think, eliminate this. Um, when I go to conferences and give speeches, it's all infrastructure operations people. And I say, you know, how many people have three X, five X, you know, uh, things to monitor them. They had, you know, three years ago, two years ago, and everyone's hand goes up, say how many people have hired more staff in that time period. Zero hands go up. That's the gap we have to fill. And we have to fill that through better automation, more intelligent systems. It's the only way we're going to be able to feel back out. >>What's your perspective, uh, if organizations choose not to adopt AI ops. >>Yeah. >>That's pretty good. So I'll do that. >>Yeah. So I think it said, I say it's related to a couple of things that probably everybody tired off lately and everybody can relate to. And this would resonate that we'd have 5g, which is old set to transform the one that we know it, of communication with these smart cities, smart communities, IOT, which is going to become pivotal to the success of businesses. And as we seen with this, COVID, you know, transformation of the world that there's a, there's a much bigger cost consciousness out there. People are trying to become much more, forward-looking much more sustainable. And I think at the heart of all of this, that the necessity that you have intelligent systems, which are bastardizing more than enough information that previous equipment overlooked, because if you don't measure engagement, not going right. People love being on the same page of this using two examples for hundreds of things that play a part in things not coming together in the best possible way. So I think it has an absolute necessity to grind those cost efficiencies rather than, you know, left right and center laying off people who are like pit Mattel to your business and have a great tribal knowledge of your business. So to speak, you can drive these efficiencies through automating a lot of those tasks that previously were being very manually intensive or resource intensive. And you could allocate those resources towards doing much better things, which let's be very honest going into 20, 21, after what we've seen with 2020, it's going to be mandate >>Shaking your head there when you, his mom was sharing his thoughts. What are your thoughts about this sounds like you agree. Yeah. I mean, uh, you know, uh, to put things in perspective, right? I mean, we are firmly in the digital economy, right? Digital economy, according to the Bureau of economic analysis is 9% of the us GDP. Just, you know, think about it in, in, in, in, in the context of the GDP, right? It's only ranked lower, slightly lower than manufacturing, which is at 11.3% GDP and slightly about finance and insurance, which is about seven and a half percent GDP. So G the digital economy is firmly in our lives, right? And so someone was talking about it, you know, software eats the world and digital, operational excellence is critical for customers, uh, to, uh, you know, to, uh, to drive profitability and growth, uh, in the digital economy. >>It's almost, you know, the key is digital at scale. So when, uh, when rich talks about some of the challenges and when newsman highlights 5g, as an example, those are the things that, that, that come to mind. So to me, what is the cost or perils of doing nothing? You know, uh, it's not an option. I think, you know, more often than not, uh, you know, C-level execs are asking their head of it and they are key influencers, a single question, are you ready? Are you ready in the context of addressing spikes in networks because of, uh, the pandemic scenario, are you ready in the context of automating away toil? Are you ready to respond rapidly to the needs of the digital business? I think AI ops is critical. >>That's a great point, Roger, where gonna stick with you. So we got kind of consensus there, as you said, wrapping it up. This is basically a, not an option. This is a must to go forward for organizations to be successful. So let's talk about some quick wins. So as you talked about, you know, organizations and C-levels asking, are you ready? What are some quick wins that that organizations can achieve when they're adopting AI? >>You know, um, immediate value. I think I would start with a question. How often do your customers find problems in your digital experience before you think about that? Right. You know, if you, if you, you know, there's an interesting web, uh, website, um, uh, you know, down detector.com, right? I think, uh, in, in Europe, there is an equal amount of that as well. It ha you know, people post their digital services that are down, whether it's a bank that, uh, you know, customers are trying to move money from checking account, the savings account and the digital services are down and so on and so forth. So some and many times customers tend to find problems before it operation teams do. So. A quick win is to be proactive and immediate value is visibility. If you do not know what is happening in your complex systems that make up your digital supply chain, it's going to be hard to be responsive. So I would start there >>Vice visibility. There's some question over to you from Verizon's perspective, quick wins. >>Yeah. So I think first of all, there's a need to ingest this multi-layered monetize spectrum data, which I don't think is humanly possible. You don't have people having expertise, you know, all seven layers of the OSI model and then across network and security and at the application of it. So I think you need systems which are now able to get that data. It shouldn't just be wasted reports that you're paying for on a monthly basis. It's about time that you started making the most of those in the form of identifying what are the efficiencies within your ecosystem. First of all, what are the things, you know, which could be better utilized subsequently you have the opportunity to reduce the noise of a troubled tickets handle. It sounds pretty trivial, but as an average, you can imagine every shop is tickets has the cost in dollars, right? >>So, and there's so many tickets and there's desserts that get on a network and across an end-user application value chain, we're talking thousands, you know, across and end user application value chain could be million in a year. So, and so many of those are not really, you know, cause of concern because the problem is somewhere else. So I think that whole triage is an immediate cost saving and the bigger your network, the bigger the cost of whether you're a provider, whether you're, you know, the end customer at the end of the day, not having to deal with problems, which nobody can resolve, which are not meant to be dealt with. If so many of those situations, right, where service has just been adopted, which is coordinate quality, et cetera, et cetera. So many reasons. So those are the, those are some of the immediate cost savings. >>They are really, really significant. Secondly, I would say Raj mentioned something about, you know, the end user application value chain and an understanding of that, especially with this hybrid cloud environment, et cetera, et cetera, right? The time it takes to identify a problem in an end-user application value chain across the seven layers that I mentioned with the OSI reference model across network and security and the application environment, it's something that in its own self has a massive cost to business, right? They could be point of sale transactions that could be obstructed because of this. There could be, and I'm going to use a very interesting example. When we talk IOT, the integrity of the IOT machine is extremely pivotal in this new world that we're stepping into. You could be running commands, which are super efficient. He has, everything is being told to the machine really fast. >>We're sending everything there. What if it's hacked? And if that robotic arm starts to involve the things you don't want it to do. So there's so much of that. That becomes a part of this naturally. And I believe, yes, this is not just like from a cost saving standpoint, but anything going wrong with that code base, et cetera, et cetera. These are massive costs to the business in the form of the revenue. They have lost the perception in the market as a result, the fed, you know, all that stuff. So these are a couple of very immediate funds, but then you also have the whole player virtualized resources where you can automate the allocation, you know, the quantification of an orchestration of those virtualized resources, rather than a person having to, you know, see something and then say, Oh yeah, I need to increase capacity over here, because then it's going to have this particular application. You have systems doing this stuff to, you know, Roger's point your customer should not be identifying your problems before you, because this digital where it's all about perception. >>Absolutely. We definitely don't want the customers finding it before. So rich, let's wrap this particular question up with you from that analyst perspective, how can companies use make big impact quickly with AI? >>Yeah, I think, you know, and it has been really summed up some really great use cases there. I think with the, uh, you know, one of the biggest struggles we've always had in operations is isn't, you know, the mean time to resolve. We're pretty good at resolving the things. We just have to find the thing we have to resolve. That's always been the problem and using these advanced analytics and machine learning algorithms now across all machine and application data, our tendency as humans is to look at the console and say, what's flashing red. That must be what we have to fix, but it could be something that's yellow, somewhere else, six services away. And we have made things so complicated. And I think this is what it was. One was saying that we can't get there anymore on our own. We need help to get there in all of this stuff that the outline. >>So, so well builds up to a higher level thing of what is the customer experience about what is the customer journey? And we've struggled for years in the digital world and measuring that a day-to-day thing. We know an online retail. If you're having a bad experience at one retailer, you just want your thing. You're going to go to another retailer, brand loyalty. Isn't one of the light. It wasn't the brick and mortal world where you had a department store near you. So you were loyal to that cause it was in your neighborhood, um, online that doesn't exist anymore. So we need to be able to understand the customer from that first moment, they touch a digital service all the way from their, their journey through that digital service, the lowest layer, whether it be a database or the network, what have you, and then back to them again, and we not understand, is that a good experience? >>We gave them. How does that compare to last week's experience? What should we be doing to improve that next week? And I think companies are starting and then the pandemic, certainly you push this timeline. If you listen to the, the, the CEO of Microsoft, he's like, you know, 10 years of digital transformation written down. And the first several months of this, um, in banks and in financial institutions, I talked to insurance companies, aren't slowing. Now they're trying to speed up. In fact, what they've discovered is that there, obviously when we were on lockdown or what have you, the use of digital services spiked very high. What they've learned is they're never going to go back down. They're never going to return to pretend levels. So now they're stuck with this new reality. Well, how do we service those customers and how do we make sure we keep them loyal to our brand? >>Uh, so, you know, they're looking for modernization opportunities. A lot of that, that things have been exposed. And I think Raj touched upon this very early in the conversation is visibility gaps. Now that we're on the outside, looking in at the data center, we know we architect things in a very specific way. Uh, we better ways of making these correlations across the Sparrow technologies to understand where the problems lies. We can give better services to our customers. And I think that's really what we're going to see a lot of the, the innovation and the people really for these new ways of doing things starting, you know, w now, I mean, I think I've seen it in customers, but I think really the push through the end of this year to next year when, you know, economy and things like that, straighten out a little bit more. I think it really, people are going to take a hard look of where they are is, you know, AI ops the way forward for them. And I think they'll find it. The answer is yes, for sure. >>So we've, we've come to a consensus that, of what the parallels are of organizations, basically the cost of doing nothing. You guys have given some great advice on where some of those quick wins are. Let's talk about something Raj touched on earlier, is organizations, are they really ready for truly automated AI? Raj, I want to start with you readiness factor. What are your thoughts? >>Uh, you know, uh, I think so, you know, we place our, her lives on automated systems all the time, right? In our, in our day-to-day lives, in the, in the digital world. I think, uh, you know, our, uh, at least the customers that I talked to our customers are, uh, are, uh, you know, uh, have a sophisticated systems, like for example, advanced automation is a reality. If you look at social media, AI and ML and automation are used to automate away, uh, misinformation, right? If you look at financial institutions, AI and ML are used to automate away a fraud, right? So I want to ask our customers why can't we automate await oil in it, operation systems, right? And that's where our customers are. Then, you know, uh, I'm a glass half full, uh, clinical person, right? Uh, this pandemic has been harder on many of our customers, but I think what we have learned from our customers is they've Rose to the occasion. >>They've used digital as a key moons, right? At scale. That's what we see with, you know, when, when Huseman and his team talk about, uh, you know, network operational intelligence, right. That's what it means to us. So I think they are ready, the intersection of customer experience it and OT, operational technology is ripe for automation. Uh, and, uh, you know, I, I wanna, I wanna sort of give a shout out to three key personas in, in this mix. It's somewhat right. One is the SRE persona, you know, site, reliability engineer. The other is the information security persona. And the third one is the it operator automation engineer persona. These folks in organizations are building a system of intelligence that can respond rapidly to the needs of their digital business. We at Broadcom, we are in the business of helping them construct a system of intelligence that will create a human augmented solution for them. Right. So when I see, when I interact with large enterprise customers, I think they, they, you know, they, they want to achieve what I would call advanced automation and AI ML solutions. And that's squarely, very I ops is, you know, is going as an it, you know, when I talked to rich and what, everything that rich says, you know, that's where it's going. And that's what we want to help our customers to. >>So rich, talk to us about your perspective of organizations being ready for truly automated AI. >>I think, you know, the conversation has shifted a lot in the last, in, in pre pandemic. Uh, I'd say at the end of last year, we're, you know, two years ago, people I'd go to conferences and people come up and ask me like, this is all smoke and mirrors, right? These systems can't do this because it is such a leap forward for them, for where they are today. Right. We we've sort of, you know, in software and other systems, we iterate and we move forward slowly. So it's not a big shock. And this is for a lot of organizations that big, big leap forward in the way that they're running their operations teams today. Um, but now they've come around and say, you know what? We want to do this. We want all the automations. We want my staff not doing the low complexity, repetitive tasks over and over again. >>Um, you know, and we have a lot of those kinds of legacy systems. We're not going to rebuild. Um, but they need certain care and feeding. So why are we having operations? People do those tasks? Why aren't we automating those out? I think the other piece is, and I'll, I'll, I'll send this out to any of the operations teams that are thinking about going down this path is that you have to understand that the operations models that we're operating under in INO and have been for the last 25 years are super outdated and they're fundamentally broken for the digital age. We have to start thinking about different ways of doing things and how do we do that? Well, it's, it's people, organization, people are going to work together differently in an AI ops world, um, for the better, um, but you know, there's going to be the, the age of the 40 person bridge call thing. >>Troubleshooting is going away. It's going to be three, four, five focused engineers that need to be there for that particular incident. Um, a lot of process mailer process we have for now level one level, two engineering. What have you running of tickets, gathering of artifacts, uh, during an incident is going to be automated. That's a good thing. We shouldn't be doing those, those things by hand anymore. So I'd say that the, to people's like start thinking about what this means to your organization. Start thinking about the great things we can do by automating things away from people, having to do them over and over again. And what that means for them, getting them matched to what they want to be doing is high level engineering tasks. They want to be doing monitorization, working with new tools and technologies. Um, these are all good things that help the organization perform better as a whole >>Great advice and great kind of some of the thoughts that you shared rich for what the audience needs to be on the, for going on. I want to go over to you, give me your thoughts on what the audience should be on the lookout for, or put on your agendas in the next 12 months. >>So there's like a couple of ways to answer that question. One thing would be in the form of, you know, what are some of the things they have to be concerned about in terms of implementing this solution or harnessing its power. The other one could be, you know, what are the perhaps advantages they should look to see? So if I was to talk about the first one, let's say that, what are some of the things you have to watch out for like possible pitfalls that everybody has data, right? So yeah, that's one strategy, we'd say, okay, you've got the data, let's see what we can do with them. But then there's the exact opposite side, which has to be considered when you're doing that analysis that, Hey, what are the use cases that you're looking to drive, right? But then use cases you have to understand, are you taking a reactive use case approach? >>Are you taking quite active use cases, right? Or that that's a very, very important consideration. Then you have to be very cognizant of where does this data that you have vision, it reside, what are the systems and where does it need to go to in order for this AI function to happen and subsequently if there needs to be any, you know, backward communication with all of that data in a process better. So I think these are some of the very critical points because you can have an AI solution, which is sitting in a customer data center. It could be in a managed services provider data center, like, right, right. It could be in a cloud data center, like an AWS or something, or you could have hybrid scenarios, et cetera, all of that stuff. So you have to be very mindful of where you're going to get the data from is going to go to what are the use cases you're trying to, you have to do a bit of backward forward. >>Okay. We've got this data cases and I think it's the judgment. Nobody can come in and say, Hey, you've built this fantastic thing. It's like Terminator two. I think it's a journey where we built starting with the network. My personal focus always comes down to the network and with 5g so much, so much more right with 5g, you're talking low latency communication. That's like the two power of 5g, right? It's low latency, it's ultra high bandwidth, but what's the point of that low latency. If then subsequently the actions that need to be taken to prevent any problems in critical applications, IOT applications, remote surgeries, uh, test driving vehicles, et cetera, et cetera. What if that's where people are sitting and sipping their coffees and trying to take action that needs to be in low latency as well. Right? So these are, I think some of the fundamental things that you have to know your data, your use cases and location, where it needs to be exchanged, what are the parameters around that for extending that data? >>And I think from that point onward, it's all about realizing, you know, in terms of business outcomes, unless AI comes in as a digital labor, that shows you, I have, I have reduced your, this amount of, you know, time, and that's a result of big problems or identified problems for anything. Or I have saved you this much resource right in a month, in a year, or whatever, the timeline that people want to see it. So I think those are some of the initial starting points, and then it all starts coming together. But the key is it's not one system that can do everything. You have to have a way where, you know, you can share data once you've got all of that data into one system, maybe you can send it to another system and make more, take more advantage, right? That system might be an AI and IOT system, which is just looking at all of your streetlights and making sure that Hey, parent switched off just to be more carbon neutral and all that great stuff, et cetera, et cetera >>For the audience, you can take her Raj, take us time from here. What are some of the takeaways that you think the audience really needs to be laser focused on as we move forward into the next year? You know, one thing that, uh, I think a key takeaway is, um, uh, you know, as we embark on 2021, closing the gap between intent and outcome and outputs and outcome will become critical, is critical. Uh, you know, especially for, uh, uh, you know, uh, digital transformation at scale for organizations context in the, you know, for customer experience becomes even more critical as Swan Huseman was talking, uh, you know, being network network aware network availability is, is a necessary condition, but not sufficient condition anymore. Right? The what, what, what customers have to go towards is going from network availability to network agility with high security, uh, what we call app aware networks, right? >>How do you differentiate between a trade, a million dollar trade that's happening between, uh, you know, London and New York, uh, versus a YouTube video training that an employee is going through? Worse is a YouTube video that millions of customers are, are watching, right? Three different context, three different customer scenarios, right? That is going to be critical. And last but not least feedback loop, uh, you know, responsiveness is all about feedback loop. You cannot predict everything, but you can respond to things faster. I think these are sort of the three, uh, three things that, uh, that, uh, you know, customers are going to have to, uh, have to really think about. And that's also where I believe AI ops, by the way, AI ops and I I'm. Yeah. You know, one of the points that was smart, shout out to what he was saying was heterogeneity is key, right? There is no homogeneous tool in the world that can solve problems. So you want an open extensible system of intelligence that, that can harness data from disparate data sources provide that visualization, the actionable insight and the human augmented recommendation systems that are so needed for, uh, you know, it operators to be successful. I think that's where it's going. >>Amazing. You guys just provided so much content context recommendations for the audience. I think we accomplished our goal on this. I'll call it power panel of not only getting to a consensus of what, where AI ops needs to go in the future, but great recommendations for what businesses in any industry need to be on the lookout for rich Huisman Raj, thank you for joining me today. >>Pleasure. Thank you. Thank you. >>We want to thank you for watching. This was such a rich session. You probably want to watch it again. Thanks for your time.

>>from around the globe. It's the Cube with digital coverage of AI ops Virtual Forum Brought to you by Broadcom Welcome back to the Broadcom AI Ops Virtual Forum Lisa Martin here talking with Usman Naseer Global Product Management at Verizon we spend Welcome back. >>Uh huh. Hello, Good >>to see you. So 2020 The year of that needs no explanation. With the year of massive challenges, I wanted to get your take on the challenges that organizations are facing this year as the demand to deliver digital products and services has never been higher. >>Yeah, I e I think this is something is so close to all the part part right? It's something that's impacted the whole world equally. And I think regardless off which industry you win, you have been impacted by this in one form or the other and the i c t industry, the information and communication technology industry. You know, Verizon being really massive player in that whole arena, it has just been sort of struck with this massive confirmation that we have talked about for a long time. We have talked about these remote surgery capabilities whereby you got patients in Kenya were being treated by experts sitting in London or New York and also this whole consciousness about, you know, our carbon footprint and being environmentally conscious. This pandemic has taught a school of that and brought this to the forefront off organizational priority, right? The demand. I think that Zaveri natural consequence of everybody sitting at home. And the only thing that can keep things still going is the data communication, Right? But I would just say that that is what kind of at the heart of all of this. Just imagine if we are to realize any of these targets that the world is world leadership is setting for themselves. Hey, we have >>to be carbon >>neutral by Xia as a country as a geography, etcetera etcetera. You know, all of these things require you to have this remote working capability this remote interaction, not just between human but machine to machine interaction. And this is a unique value chain which is now getting created that you've got people we're communicating with other people or were communicating with other machines. But the communication is much more. I won't even use the term really time because we've used real time for voice and video, etcetera. We're talking low latency microsecond to see and making that can either cut somebody's, you know, um, our trees or that could actually go and remove the tumor, that kind of stuff. So that has become a reality. Everybody's asking for it. Remote learning, being an extremely massive requirement where, you know, we've had to enable these thes virtual classrooms ensuring the type of connectivity, ensuring the type of type of privacy which is just so, so critical. You can't just have everybody you know, Go on the internet and access the data source. You have to be. I'm sorry about the integrity and security of >>that. They've >>had the foremost. So I think all of these things, Yes. We have not been caught off guard. We were should be pretty forward looking in our, you know, plans in our evolution. But yes, it does this fast track a journey that we would probably the least we would have taken in three years. It has brought that down to two quarters where we had to execute them. >>Right? Massive acceleration. All right, so you articulated the challenges really well and a lot of the realities that many of our viewers air facing. Let's talk now about motivations ai ops as a tool as a catalyst for helping organizations overcome those challenges. >>So, yeah, now all that I said you can imagine, you know, it requires microsecond the sea and making which human being on this planet can do microsecond the sea and making on complex network infrastructure, which is impacting, and user applications which have multitudes off effect. You know, in real life, I used the example of a remote surgeon. Just imagine, if you know, even because you just lose your signal on the quality of that communication for that microsecond, it could be the difference between killing somebody in saving somebody's life. Is that particular? We talk about autonomous vehicles way talk about the transition to electric vehicles, smart motorways, etcetera, etcetera in federal environment. How is all of that going to work? You have so many different components coming in. You don't just have a natural can security anymore. You have software defined networking that's coming becoming a part of this. You have mobile edge computing that is rented for the technologies. Five g enables we're talking augmented reality. We're talking virtual reality all of these things require that resource is. And while we carbon conscious, we don't just wanna build a billionaire, a terrorist on the planet, right? We we have to make sure that resource is air given on demand and the best way of re sources can be given on demand and could be most efficient. Is that we're making is being made at million microsecond. And those resource is our accordingly being distribute. Right? If you're 10 flying on, people sipping their coffee is having teeth talking to somebody else. You know, just being away on holiday. I don't think we're gonna be able to handle that world that we have already stepped into. Risen's five g has already started businesses on the transformational journey where they're talking about end user experience, personalization. You're gonna have, you know, events where people are going to go. And it's going to be three dimensional experiences that are purely customized for you. How How does that all happen without this intelligence having their and a network with all of these multiple layers assaults spectrum, it doesn't just need to be intuitive. Hey, this is my private I p traffic. This is public traffic. You know it has to now be into or this is an application that to privatize over another has to be intuitive to the criticality in the context, off those transactions again that surgeons surgery is much more important than husband sitting and playing a video game. >>Yeah, I'm glad that you think that that's excellent. Let's go into some specific use cases. What are in some of the examples that you gave? Let's kind of dig deeper into some of that. What you think are the lowest hanging fruit for organizations, kind of pan industry to go after here. >>Excellent, right? And I think this just like different ways to look at the lowest timing food. Like for somebody like Verizon, who is the managed services provider, you know, very comprehensive medicines. But we obviously have food timing much lower than potentially for some of our customers who want to go on that journey, right? So for them to just >>go and try and >>harness the power of help, the food's might be a bit higher hanging. But for somebody like God, the immediate ones would be to reduce the number off alarms that are being generated by these overlays services. You've got your basic network. Then you've got your software defined networking. On top of that, you have your hybrid clouds. You have your edge computing coming on top of that, you know? So ALOF this means if there is an outrage on one device on the network, gonna make this very real for everybody, right? It's right out. I'm not divisive. Network does not stop all of those multiple applications for monitoring tools from raising havoc and raising thousands off alarms and everyone capacity. If people are attending to those thousands off alarms, it's like you having a police force. And there's a burglary in one bank and the alarm goes off in $50. How you gonna make the best use of your police force? You're gonna go investigate 50 banks? You wanna investigate one where the problem is. So it's as realize that and I think that's the first wind where people can save so much cost, which is currently being wasted. And resource is running around primary figure stuff up immediately. Anti this with network and security network and security is something which has eluded even the most. You know, amazing off brings in or engineering. Well, we took it. We have network expert, separate people. Security experts separate people to look for different things. But there are security events that can impact the performance of the network and then use your application, cetera, etcetera, which could be falsely attributed to the network. And then if you've got multiple parties, which are then which have to clear stakeholders, you can imagine the blame game that goes on pointing fingers, taking names, not taking responsibility. That is how all this happened. This is the only way to bring it all together to say Okay, this is what takes priority. If there's an event that has happened, what is its correlation to the other downstream systems, devices, components and user applications. And it subsequently, you know, like isolating into the right cause where you can most effectively resolve that problem. Certainly, I would say on demand virtualized resource virtualized resource is the heart and soul of the spirit of status that you can have them on them up so you can automate the allocation of these. Resource is based on, you know, customers consumption, their peaks, their crimes. All of that comes in. You see Hey, typically on a Wednesday, their traffic goes up significantly from this particular application. You know, going to this particular data center, you could have this automated this AI ops, which is just providing those resource, is, you know, on demand and tell us to have a much better commercial engagement with customers and just a much better service assurance model. And then one more thing on top of that, which is very critical, is that, as I was saying, giving that intelligence to the network to start having context of the criticality of a transaction that doesn't exist to it. You can't have that because for that you need to have this, you know, multi layer data. You need to have multiple system which are monitoring and controlling different aspects of your overall and user application value chain to be communicating with each other. And, you know, that's that's the only way to sort of achieve that goal. And that only happens with AI off. It's not possible with them. You can paradise Comdex. >>So Guzman, you clearly articulated some obvious low hanging for use cases that organizations can go after. Let's talk now about some of the considerations you talked about the importance of the network in AI ops. The approach, I assume, needs to be modular support needs to be heterogeneous. Talk to us about some of those key considerations that you would recommend >>absolutely. So again, basically starting with the network. Because if there is, if the network sitting at the middle of all of this is not working, then things from communicate with each other, right? And the cloud doesn't work. Nothing. None of this person has hit the hardest all of this. But then subsequently, when you talk about machine to machine communication or i o T. Which is the biggest transformation to spend, every company is going priority now to drive those class efficiencies enhancements. We've got some experience. The integrity off the tab becomes paramount, right? The security integrity of that. How do you maintain integrity off your detail beyond just the secured network components that Trevor right? That's where you get into the whole arena Blockchain technology where you have these digital signatures or barcodes that machine then and then an intelligent system is automatically able to validate and verify the integrity of the data and the commands that are being executed by those and you determine. But I think the terminal. So I o. T machines, right, that is paramount. And if anybody is not keeping that into their equation, that in its own self, is any eye off system that is therefore maintaining the integrity off your commands and your quote that sits on those those machines Right. Second, you have your network. You need to have any off platform, which is able to rationalize all the fat network information, etcetera. And couple that with that. The integrity peace. Because for the management, ultimately, they need to have a co haven't view off the analytics, etcetera, etcetera. They need to. They need to know where the problems are again, right? So let's see if there's a problem with the integrity off the commands that are being executed by a machine. That's a much bigger problems than not being able to communicate with that machine. And the first thing because you'd rather not talk to the machine or haven't do anything if it's going to start doing the wrong thing, So I think that's where it's just very intuitive. It's natural. You have to have subsequently if you have some kind of say and let me use that use case Off Autonomous comes again. I think we're going to see in the next five years it's much water rates, etcetera. It will set for autonomous because it's much more efficient. It's much more space, etcetera, etcetera. So whether that equation you're gonna have systems which will be specialist in looking at aspects and Trump's actions related to those systems, for example, an autonomous moving vehicle's brakes are much more important than the Vipers, Right? So this kind of intelligence, there will be multiple systems who have to sit and nobody has to. One person has to go and on these systems, I think these systems should be open source enough that you are able to integrate them, right? If something sitting in the cloud you were able to integrate for that with obviously the regard off the security and integrity off their data, that has two covers from one system to the extremely. >>So I'm gonna borrow that integrity theme for a second as we go into our last question. And that is this kind of take a macro. Look at the overall business impact that AI ops can help customers make. I'm thinking of, you know, the integrity of teams aligning business and I t. Which we probably can't talk about enough. We're helping organizations really effectively measure KP eyes that deliver that digital experience that all of us demanding consumers expect. What's the overall impact? What would you say in separation? >>So I think the overall impact is a lot. Of course, that customers and businesses give me term got prior to the term enterprises defense was inevitable. There's something that for the first time will come to light. And it's something that is going to, you know, start driving cost efficiencies and consciousness and awareness within their own business, which is obviously going to have, you know, abdominal kind of an effect. So what example being that, you know, you have a problem? Isolation? I talked about network security, this multilayered architectural which enables this new world of five g um, at the heart of all of it. It is to identify the problem to the source, right? Not be bogged down by 15 different things that are going wrong. What is causing those 15 things to go wrong, right that speed to isolation and its own self can make millions and millions off dollars to organizations every organization. Next one is obviously overall impacted customer experience. The five g waas. You can have your customers expecting experiences from you, even if you're not expecting to deliver them in 2021 2022. You'll have customers asking for those experiences or walking away if you do not provide those experiences. So for it's almost like a business can do nothing. Every year they don't have to reinvest if they just want to die on the wine. Businesses want to remain relevant. Businesses want to adopt the latest and greatest in technology, which enables them to, you know, have that superiority and continue it. So from that perspective that continue ity, we're ready that there are intelligence system sitting, rationalizing information and making this in supervised by people, of course, who were previously making some of those here. >>That was a great summary because you're right, you know, with how demanding consumers are. We don't get what we want. Quickly we turn right, we go somewhere else, and we could find somebody that can meet those expectations. So it was spent Thanks for doing a great job of clarifying the impact and the value that AI ops can bring to organizations. That sounds really now is we're in this even higher demand for digital products and services, which is not going away. It's probably going to only increase. It's table stakes for any organization. Thank you so much for joining me today and giving us your thoughts. >>Pleasure. Thank you. >>We'll be right back with our next segment.

>>from around the globe. It's the Cube with digital coverage of AI ops. Virtual forum Brought to you by Broadcom >>Welcome today, I Office Virtual Forum Finally So Martin, excited to be talking with Rich Lane now senior analyst serving infrastructure and operations professionals at Forrester Rich, it's great to have you today. >>Hey, thank you for having me. I think it's gonna be a really fun conversation at today. >>It is. We're gonna be setting the stage for with Richard for the I T operations challenges and the need for a I ops. That's kind of our objective here in the next 15 minutes. So which talk to us about some of the problems that enterprise I T operations are facing now, in this year, that is 2020 that are gonna be continuing into the next year. >>Yeah. I mean, I think we've been on this path for a while. It's certainly that the last eight months has has accelerated this problem and and brought a lot of things toe light that people are, you know, they were going through the day to day firefighting as their goal way of life. It's just not sustainable anymore. New highly distributed environment or in the need for digital services. And, you know, one of them has been building for a while, really, is in the digital age. You know, we're providing so many of the interactions with customers online. We've added these layers of complexity, um, toe applications to infrastructure. You know, we're in the in the cloud where hybrid were multi cloud. You know, you name it using cloud native technologies, reason legacy stuff. We still have mainframe out there. Uh, you know, just the vast amount of things we have to keep track of now in process and look at the data and signals from It's just it's really untenable for humans to do that in silos now, Andi. And when you add to that, you know, when cos air so heavily invested in going on the digital transformation path, and it's accelerated so much the last year so that, you know, we're getting so much for our business in revenue drive from the services that they become core to the business. They're not afterthoughts anymore. It's not just about having a website presence, it's it's about to arrive in core business value from the services you're providing to York through your customers and a lot of cases customers you're never gonna meet or see at that. So it is even more important to be vigilant on top of the quality of that service that you're giving them. And then when you think about just the staffing issues we have, there's just not enough bodies to go around in operations anymore. Um, you know, we're not gonna be able to hire, you know, like we did 10 years ago. Even eso That's where we need the systems to be able to bring those operational efficiencies to bear. When we say operational insufficiencies, we don't mean, you know, lessening headcount because we can't do the other be foolish. What we mean is getting the headcount we have back to broking on higher level things, you know, working on technology refreshes and project work that that brings better digital services to customers and get them out of doing these sort of low complexity, high volume task that they're spending a tely east 20% if not more on of their day each day. So I think the more we could bring intelligence to bear on automation to take those things out of their hands, the better off we are going forward. >>And I'm sure those workers are wanting to be able to have the time to deliver more value, more strategic value to the organization, to their role. And as you're saying you know, is the demand for digital services this spiking, it's not going to go down. And its consumers, If we have another option on, we're not satisfied, we're gonna go somewhere else. So So it's really about not just surviving this time right now. It's about how do I become a business distance Gonna thrive going forward and exceeding expectations that are now just growing and growing. So let's talk about AI ops as a facilitator. Collaboration across business folks. I t folks, developers, operations. How can it facilitate collaboration, which is even more important these days? >>Yeah, So one of the great things about it is now, you know, years ago, bygone years, as they say, we would buy a tool to fit each situation and, you know, somebody that worked in networking out their school. Somebody infrastructure from a, you know, Linux standpoint have their tools. Somebody is from stores would have their tool. What we found Waas, we would have an incident overy high impacting incident occur. Everybody would get on the phone 2030 people. I'll be looking at their siloed tool. They're silent pieces of data and then we would still have to try a like link point A to B to C together, you know, just to institutional knowledge. And there was just ended up being a lot of gaps there because we couldn't understand that a certain thing happening over here was related to an event over here. Now, when we bring all that data under one umbrella one Data Lake where we wanna call it a lot of smart analytics to that data on normalize that data in a way we can contextualize it from, you know, point a to point B all the way through the application infrastructure stack. Now the conversation changes. Now the conversation changes to here is the problem. How are we going to fix it? We're getting there immediately versus 345 hours of hunting and pecking and looking at things and trying toe trying to extrapolate what we're seeing across the spirit systems. Andi, that's really valuable. And what that does is now we can change the conversation for measuring things in corrupt time and data center performance metrics is to How >>are we >>performing as a business? How are we overall in in real time? How is a business be impacted by hey, service disruption. We know how much money we're losing per minute hour. What have you on what that translates lights into brand damage and things along those lines that people are very interested in that. And you know, what is the effect of making decisions either from a product change side, You know, if we're always changing the mobile app So we're always changing the website. But do we understand what value that brings us or what negative impact that has? We could measure that now And also sales marketing, Um, they run a campaign. Here's your, you know, coupon for 12% off today only. What does that drive to us with user engagement? We can measure that now in real time. We don't know. Wait for those answers anymore, E, I think you know having all this data and understand the cause and effect of things increases and enhances thes feedback loops of we're making decisions as a business as a whole to make bring better value to our customers. You know? How does that tie into Office and Dev initiatives? How does everything that we do if I make a change, the underlying architectures that help move the needle forward is that hinder things? All these things factor into it in factor into customer experience, which is what we're trying to do with the end of the day, whether operations people like it or not. We are all in the customer experience business now, and we have to realize that and work closer than ever with our business and Dev partners to make sure we're delivering the highest level of customer experience we can >>now. Customer experience is absolutely critical for a number of reasons, always kind of think it's it's inextricably linked with employee experience. But let's talk about long term value because as organizations and every industry have pivoted multiple times this year and will probably continue to do so for the foreseeable future, for them to be able to get immediate value that let's let's not just stop the bleeding, but let's allow them to get, you know, a competitive advantage and we really become resilient. What are some of the applications that AI ops can deliver with respect to long term value for an organization? >>Yeah, I think that it's, you know, and you touched upon this very important point that there is a set of short term goals you want to achieve. But they're really gonna be looking towards 12, 18 months down the road. What is it gonna have done for you? And I think this helps framing out for you. What's most important? Because it be different for every enterprise. Um, and it also shows the arrow I of doing this because there is some, you know, change is gonna be involved in things you're gonna have to do. But when you look at the longer time horizon, what it brings to your business is the whole, uh to me, at least it all seems it seems like a no brainer to not do it. Um, you're thinking about the basic things, like, you know, faster re mediation of client impacting incidents, or maybe maybe even predictive, sort of detection of these incidents that will affect clients. So now you're getting, you know, at scale, you know, it's very hard to do when you have hundreds of thousands of objects on the management that relate to each other. But now you're having letting the machines and intelligence layer find out where that problem is. You know, it's it's not the red thing, it's the yellow thing. Go look at that. Um, it's reducing the amount of finger pointing. And what have you like? It was on between teens. Now everybody's looking at the same day to the same sort of symptoms and like, Oh, yeah, okay, this is telling us, you know, here's the root cause you should investigate this huge, huge thing on. Does something we never thought we'd get. Thio, where decisions. We smart enough to tell us these things, But this again, this is the power of having all the data under one umbrella and smart analytics. Andi, I think, really, You know, it's about if you look at where infrastructure in operations people are today and especially, you know, eight months and nine months, whatever it is into the pandemic. Ah, lot of them getting really burnt out with doing the same repetitive tasks over and over again. Just trying to keep the lights on, you know, we need we need to extract those things for those people just because it just makes no sense to do something over and over again. The same remediation step. Just we should automate those things. So getting that sort of, you know, drudgery off their hands, if you will, and get them into into other important things they should be doing, you know, they're really hard to solve problems. That's where the human shine on. And that's where you know, having ah, you know, really high level engineers. That's what they should be doing, you know, and just being able to do things I think in a much faster, in more efficient manner. When you think about an incident occurring right in a level, one technician picks that up and he goes in triage that maybe run some tests. He has a script or she, uh and you know, they over a ticket. They enrich the ticket, they call some lock files, they go look up for the service on you're in an hour and a half into an incident before anyone's even looked at it. If we could automate all of that, why wouldn't we? That makes it easier for everyone on guy. And I really think that's where the future is. Is bringing this intelligent automation to bear to take knocked down all the little things that consume the really the most amount of time when you think about it? If you aggregated over the course of, like, a quarter or year Ah, great deal of your time is spent just doing that menu Sha again. Why don't we automate that? We should So I really think that's that's where you gonna look long term, I think also the sense of we're going to be able to measure everything in the sense of business. KP eyes versus just I t Central KP eyes. That's really where we gonna get to in a digital age. And I think we waited too long to do that. I think our operations models were all voted. I think, uh, you know, a lot of ah, a lot of the KPs we look at today are completely outmoded. They don't really change if you think about it. We look at the monthly reports over the course of a year s, so let's do something different. And now, having all this data and a smart analytics. We can do something different. >>Absolutely. I'm glad that you brought up kind of looking at the impact that AI ops can make on on minutia and burn up. That's a really huge problem that so many of us are facing in any industry, and we know that there's some amount of this that's going to continue for a while longer. So let's get our let's let leverage intelligent automation present your point because we can to be able to allow our people to not just be more efficient but to be making a bigger impact. And there's that mental component there that I think is absolutely critical. I do want to ask you, what are some of these? So for those folks going all right, we've We've got to do this. It makes sense. We see some short term things that we need. We need short term value. We need long term value, as you've just walked us through. What are some of the obstacles that you to hate be on the lookout for this toe? Wipe it out of >>the way? Yeah, I think there's, You know, when you think about the obstacles I think people don't think about what are big. Changes this for their organization, right? You know, they're they're going to change process. They're gonna change the way teams interact there. They're going to change a lot of things, but the off of the better. So what were traditionally really bad in infrastructure operations is communication marketing a new initiative, right? We don't go out and get our peers agreement to it over the product owner is, you know, and say Okay, this is what gets you. This is what it changes. People just here. I'm losing something. I'm losing control over something. You're going to get rid of the tools that I have, but I love I've spent years building out perfecting Andi That's threatening to people, and understandably so, because people think if I start losing tools, I start losing head count. Then where's my department at that point? But that's not what this is all about. This this isn't a replacement for people. This is a replacement for teams. This is an augmentation. This is getting them back to doing the things they should be doing in less of the stuff they shouldn't be doing. And frankly, it's a it's about providing better services. So when they in the end it's counterintuitive, be against it because it's gonna make I t operations look better. He's gonna make a show us that we are the thought leaders and delivering digital services that we can constantly being perfected the way we're doing it. And, oh, by the way, we can help the business be better. Also, at the same time, I think some of the mistakes people really don't make really do make, uh is not looking at their processes today, trying to figure out what they're gonna look like tomorrow when we bring in advanced automation and intelligence, but also being prepared, prepared for what the future state is, you know, in talking toe one company they were like, Yeah, we're so excited for this. We we got rid of our 15 year old monitoring system on the same day we step the new system. One problem we had So it waas We weren't ready for the amount of incidents that had generated on day one, and it wasn't because we did anything wrong or the system was wrong or what have you? It did the right thing actually almost too well. What it did is it uncovered a lot of really small incidents through advanced correlations. We didn't know we had. So there was things lying out there. They're always like how that's where the system acts strange sometimes that we could never pin it down. We found all those things, which is good, because but it kind of made us all kind of sit back and think. And then our readership, these guys doing their job right Then we had to go through evolution of just explaining. We were 15 years behind from invisibility standpoint into our environment. But technologies that we deployed applications had moved ahead, modernized. So this is like a cautionary tale of falling too far behind from a sort of monitoring and intelligence and automation standpoint. Eso I thought that was a really good story for something like think about as you go to deploy these modern systems. But I think if you really you know the marketing to people, so they're not threatened, I think thinking about your process and then what's what's your day one and then look like And then what's your six and 12 months after that looks like I think settling all that stuff up front just sets you up for success. >>Alright, Rich. Take us home here. Let's summarize. How can clients build a business case for AI ops? What do you recommend? >>Yeah, you know, I actually get that question a lot. It's usually almost always the number one question and you know, webinars like this and conversations that that the audience puts in. So I wouldn't be surprised if that was true going forward from this one. Um, yeah. People are like, you know, Hey, we're all in. We want to do this. We know this is the way forward. But the guy who writes the checks, the CEO, the VP of ops is like, you know, I've signed lots of checks over the years for tools wise is different on, but I got people to do is to sit back and start doing some hard math, right? One of the things that that resonates with the leadership is dollars and cents. It's not percentages. So saying, you know, it's brings us a 63% reduction and empty TR is not going to resonate. Oh, even though it's a really good number, you know. I think what it is you have to put it in terms of of if we could avoid that 63% right? You know, what does that mean for our digital services as faras revenue? Right. We know that every our system down, I think, you know, typically in the market, you see, is about $500,000 an hour for enterprise. We'll add that up over the course of the year. What are you losing in revenue? Add to that brand damage loss of customers. You know, Forrester puts out a really big casino, uh, customer experience in next every year. That measures that if you're delivering digital services, bad digital services, if you could raise that up, what is that return to you in revenue on? That's a key thing. And then you just look at the hours of lost productivity. I call it I call it something else. I think it's catching name meaning if if a core internal system is down, say, you know, you have ah customer service desk of 1000 customer service people and they can't do that, look up or fix that problem for clients for an hour. How much money. Does that lose you and you multiply it? Oh, you know, average customer service desk. You know, person makes X amount in our times, this amount of time, this many times it happens. Then you start seeing the rial sort of power of a layoffs for this, this incident avoidance or be least lowering the impact of these incidents. And people have put out in graphs and spreadsheets and all this. And then I'm doing some research around this, actually to toe put out something that people can use to say The project funds itself in 6 to 12 months. It's paid for itself. And then after that, it's returning money to the business. Why would you not do that? And we start framing of the conversation that way, little lightbulbs turned on for the people who signed the checks For sure. >>That's great advice for folks to be thinking about. I love how you talked about 63% reduction in something you think that's great. What is it? Impact. How does it impact the revenue for the organization? If we're avoiding costs here, how do we drive up revenue? So having that laser focus on revenue is great advice for folks in any industry looking to build a business case for AI ops. I think you set the stage for that rich beautifully. And you are right. This was a fun conversation. Thank you for your time. Thank you. And thanks for watching.

>>from around the globe. It's the Cube with digital coverage of AI ops. Virtual Forum Brought to You by Broadcom >>Welcome to this preview of Broadcom's AI Ops Virtual Forum on your host Lisa Martin, and joining me to give you a sneak peek of this event that's on December 3rd is Srinivasan, Rajagopal or Raj, the head of a I Ops at Broadcom Raj, this event is coming up in a couple of weeks. Excited. >>Good to be here. I am excited, Lisa. You know, um, customers are poised for growth in 2021 and, uh, they are we believe they all. They will also come out off the pandemic toe, grow their business and serve their customers, you know? Well, they have to key challenges. How do you grow at the same time, operate with efficiency, right. These two challenges our decision makers are struggling with every day at scale. That is why they do digital transformation at scale. And our key influencers like I t operators and SRE personas are helping our decision makers in our customers to drive the efficiency they are trying toe focus on converting outputs to outcomes. That's what the eye ops is all about and you're gonna hear it from us. >>Yeah, We've got a panel of experts here. Rich Lane, senior research analyst for Forrester, is going to be joining us as well as Guzman nastier the global product management at Verizon. And, of course, Raj, you're gonna be hearing some of the latest trends for AI ops. Why, now is the time, Raj, What are some of the key takeaways that you think those key influencers and those decision makers are gonna walk away from this event empowered with >>So the You know, our decision makers and a key influencers have a single question in mind when they deal with enterprise large enterprise scenarios, the questions that they get asked by their C level execs are Are you ready? Are you ready when remote work is the norm? Are you ready when you have to optimize your investments and are you ready when you have to accelerate your transformation at scale toe operate as a digital enterprise? All of this requires them to think and act differently from people process technology. And how do you bring all of this together under the ages off what we call a I ops is what they're gonna learn about. >>Another thing, too, is you're going to hear the latest industry trends on AI ops from Raj and the panel of experts that we mentioned a minute ago. How organizations like yours are finding value from a I ops and something that Raj talked about a minute ago is understanding why Now is the time to be ready for I also Raj and I look forward to you joining us along with our other Panelists. December 3rd register for the Broadcom AI Ops Virtual form today.

>>Well, welcome everybody to our last session of the day. I want to introduce you to Sean O'Meara. Orfield Cto. Hey, Sean. >>Hey, Nick. Good afternoon. It's been a crazy day. It has. It's been a busy run up to today in a busy day with a lot of great things going on. You know, we've heard from Adrian on his strategy this morning. The great way the Marantz is moving forward. We announced our new product line. You know, we spoke about the new doctor Enterprise Container Cloud line, New future for Mirant. Us. We had a great lineup of customers share their story. We introduced lanes following on the lanes launch a couple of weeks ago. Andi, we're introducing new great projects like our mosque project. New way to deliver open stack going into the future on then in parallel sel. This We ran a great tutorial tracker teachers you all about how to use these new products, and hopefully you'll go and everyone had opportunity to go and look through guys. Yeah. What's next? What is next? Yeah, lots going on. A lot of new things that we're thinking about for the future. Obviously, a lot of work to do on what we have right now. A lot of great things coming. But, you know, we've had this opportunity to talk about all these cool things that are coming down the road. And everybody these days seems to be talking about topics like edge computing or hybrid cloud. Or, you know, hyper scale data centers, even things like disaster recovery is a service. Andi, you know, we talk a lot about things like hyper converged, but frankly, it's boring. It's one thing a little. Good morning. Uh, you know, you and I have been talking about these topics for a while now, and I think it's about time when we spoke about some of the cool things that we're thinking about for the future, not necessarily looking out for the road map, but ideas for the future. Things that may could have an impact on the way we do business going to. So today we're gonna talk a little bit about things like pervasive computing. A nick, what is pervasive computing. >>Well, basically pervasive computing is when everything that you interact with, for the most part, is computerized. So in some ways, we're already there in that You know your phone is a computer. Your refrigerator may have a computer in it. Um, your smart watch your car has a computer in it. And the the most obvious sign of that is this whole Internet of things where, you know your vacuum is, uh is connected to your phone and all of that. And so pervasive computing is this, uh is this sense of you don't even really think about it. You just kind of assume that everything is computerized. >>So how is that different from ubiquitous computing? >>Oh, God. You hit, You hit my hot button. Okay, so if you look, there are a lot of places that will say that pervasive computing and ubiquitous computing are the same thing, but not the same thing. Don't use them interchangeably. They're not the same thing. You big. What is computing is where you can do your computing virtually anywhere. So, for example, you know, I've got, uh I've got a document. I started it on my laptop. I can then go and finish it sitting on the beach on my phone. Or, you know, I can go and do it in a coffee shop or a library. or wherever. So the idea of ubiquitous computing is similar in that, yes, there's computing everywhere, but it's more about your data being universally accessible. So essentially it is cloud computing. That is what this whole ubiquitous computing thing is about. >>Okay on that then differs from pervasive computing in the fact that pervasive is the devices that we have all around us versus the access to those devices. >>Exactly. It's it's really it's more about the data. So ubiquitous computing is more about My data is stored in some central place, and I could hit it from anywhere. There is a device, whereas pervasive computing is there is a device almost everywhere. Okay, so yeah, >>So why Why do we as Moran takes care about the vice of computing? >>Well, pervasive computing brings up a whole lot of new issues, and it's coming up really fast. I mean, you last night I was watching, you know, commercial where you know, somebody a woman's coming out and starting her car with her phone. Um, which sounds really cool. Um, but you know what they say Anything that you can access, you know, with your computer is hackable. So, you know, there are security issues that need to be considered when it comes to all of this, but that's that's the downside. But there's just this huge upside on pervasive computing that it's so exciting when you think about this. I mean, think about a world where remember I said your refrigerator might be attached to the network. Well, what if you could rent out space on your refrigerator to somebody someplace else in a secure way? Of course. You know what? If you could define your personal network as all of these devices that you own and it doesn't matter where your workloads run or, you know, you could define all of this stuff in such a way that the connectivity between objects is really huge. Um, so you know, I mean, you look at things like, you know, I f t t you know, it's like get a notification when the International space station passes over your house. Okay? I don't know why I would need that. Um, but it's the kind of thing that people >>would have a nine year old. You can run him outside and show Z. Oh, >>there you go. There you go. So I mean, that kind of level of connectivity between objects is really really it gives us this new level off. Uh, this new level of functionality that we would never even considered even 10 years ago. Um, it also extends the life of objects that we already have. So, you know, maybe you've got that, uh, that computerized vacuum cleaner, and you don't like the way that it you don't like the pattern that uses in your house. So you re program it or, uh Or I watched. I watched a guy decide that he didn't want to buy multiple vacuums for his house. So he programmed his programa will act Hume to fly between floors. It was actually pretty funny. Um, I it's some people just have too much time. >>It's driving the whole world of programmable at all levels. Really? Like the projects coming out of the car industry of creating a programmable car would fit into that category. Then, I >>suppose absolutely, absolutely needs developer tool kits. Um, that make it possible for anybody to re program these devices that you never would have thought of reprogramming before. So it's important. So do >>we want to talk about the questions. We would love people to give us some feedback on at this stage. >>I would love to talk about these questions. So what we did is we put together, uh, we put together a place for you to answer questions. If you're not watching this live. If you're watching this live, please go ahead. Drop your ideas in the chat. We would love to discuss them, you know. Do you want to see more of this? Or does it? Conversely, Does it scare you, Sean? You What? >>What do you >>think about these questions? >>Well, I mean, for me, the idea of the connected world at one level, the engineering me loves the idea. Another level. It comes to these questions of privacy. Vegas questions off. How do I control this going into the future? What prevents somebody from taking over my flying vacuum cleaner? I'm using it, you know? So it's an interesting question. I think there's a lot of cool, cool ideas. Yeah, and a lot of work to be done. I really want to hear other people's ideas as well and see how we can take this into the future. >>Definitely, definitely. I mean, look I mean, we're joking about it, but, you know, when somebody hacks into your grandmother's insulin pump, maybe not so funny. >>Yeah, a very real risk. >>A very real risk. A very real risk. But yeah, I mean, we'd love Thio. We'd love to hear how you'd like to see this used. So that's that's my That's kind of what I've been thinking about thes days. Um, but, you know, Sean, uh, now, you I know you are really concerned about this whole issue of developers and how they feel about infrastructure. So I would love to hear what you've got to say on that. >>Yeah, I'd like to sex, but a bit about that. You know, we we've done a lot of work over the last few years looking at how developing our history has been very focused on operations, but without big drive towards supporting developers providing better infrastructure for developers. One of the interesting things that keeps coming up to the four on Do you know, the way the world is changing is that big question is, do developers actually give a damn about infrastructure in any way, shape or form? Um, you know, ultimately more and more development languages and tools abstract that underlying infrastructure. What communities does is basically abstract. The infrastructure away, Um, mawr and more options. They're coming to market, which you can quite literally creating application without out of a writing a line of code. Um, so this morning, way Dio, we're doing it all the time, sometimes without even realizing it on. I think the definition of what a developer is is also changing to a certain extent. So you know the big question, which I have on which I'd like to understand Maureen, from talking to low developers is due. Developers care about infra What is it that you expect from infrastructure? What do they want going into the future? How are they going to interact with that infrastructure? I My personal opinion is that they don't really care about infrastructure, that they're going to find more ways to completely abstract away from that. And they just want to focus on delivering applications faster and getting value to market. But I might be wrong, and I'd really like to hear people's impact ideas and thoughts on that >>on. And that's exactly and that's why we're asking this question. Developers out there. Do you care? Or do you just want the whole thing completely abstracted away from you >>on? If you do care why, If you don't, what would you like to see? Another. It's a couple of questions to ask, but really like to hear those opinions on bond. You know, Do you just want the operations guys to live with it? You never want to hear about it again, just fine. It's actually good to say that we'll work it out. >>Yes, and that there's nothing. There's nothing wrong with pushing that up stack >>pretty much what we're trying to do here. >>Well, it is what we're trying to do. But at the same time, we want to do what's good for developers. And if you developers or like No, don't don't do that. Well, we want to know because, you know, we don't wanna work away here and some ivory tower and wind up with something that's not good for >>you after school. So cool. So, yeah, there are some other interesting things we're talking about. >>I know, I know. This is This is one of my favorites. This is one of my favorites. >>Zoo this? Yes. While >>we're on the subject of not getting involved with the infrastructure. Go ahead, Sean. Tell us about it. >>Thing is a pet topic of mine and something that that we've spoken about a lot. And thanks something that we we have spent many nights talking about. The idea is AI ops using artificial intelligence to drive operations within our infrastructure. And so a lot of people ask me, You know why? Um, essentially, What the hell is a I out on? I have answered this question many times, and it does often seem that we all take this AI ops thing for granted or look at it in a different way. To me, it is essentially, it's it's automation on steroids. That's what it boils down Thio. It's using intelligence systems that to replace the human cerebellum. I mean, let's just be blunt about this. We're trying to replace humans. Onda reason for that is we humans less meat sacks are airplane. We make mistakes all the time and compared to computers were incredibly slow. Um, you know, that's really the simplest point with the scale of modern infrastructure that we're dealing with the sheer volume. I mean, we've gone from, you know, thousands to tens of thousands of the EMS to now hundreds and thousands of containers spread across multiple time zones. Multiple places. We need to come up with better ways of managing this on the old fashioned stick through mechanism of automation. It's just too limited for that. Right >>when we say we want to replace meat sacks, we mean in a good way. >>We mean in a good way. I know it's a bit of a harsh way of putting it. Um, ultimately, humans have ability for creativity that machines just don't have. But machines can do other things, and they could do analysis of data a lot faster than we can. Quite often, we have to present that data to humans to have invalidate that information. But, you know, one of the options for us is to use artificial intelligence, quantified data, um, correlated, you know, look for root cause and then provide that information to us in such a way that we can make valid decisions based on that information a lot faster than we could otherwise, >>right? So what are the what are the implications? What are the practical implications of doing this so >>practically we can analyze massive amounts of data a lot faster than a single human. Could we even just a normal type system that's searching? We We have the tools to learn by looking at data and have machines do it a lot faster than we can. We can take action faster based on that data, because we get the data foster. We can take action and much more complex action that involves maybe many different layers of tasking much, much faster. Um, on we could start to do maintenance operations and maintenance tasks without having to wait for human beings to wake up or get to an office. But more importantly, we could start making tasks happen very complex tasks in a very specific orders, with much less potential for error. And those are the kinds of areas we're looking at. >>That's that's true. So how do you kind of see this moving forward? I mean, obviously, we're not gonna go from nothing to Skynet, and hopefully we never get to Skynet. Well, >>depends if you are in control of Skynet or not. Ultimately, Dionysus little computer. Um, practically speaking, we have a few things Thio hoops to jump through our suppose before we can look at where else is going to be really effective on the first one is a trust issue. We have to learn to trust it. And to do that, we have to put in a position where it can learn and start providing us that data analysis on that inference and then having humans validated. That's practically the very first step. No, it's a trust issue. You know, we've seen been watching sci fi for the last 30 years. Class on. Do you know the computers take over? Well, ultimately, is that real or not? Um, if we look at how we gonna get there? Probably midterm. Adaptive maintenance, maybe infrastructure orchestration. Smart allocation of resource is across cloud services. Well, >>we can talk for a minute About what that would would actually look like. So, I mean, we could talk about, you know, abs, midterms. I mean, in a practical sense, how would that actually work? >>Yeah, Okay. It's a great question. So, practically speaking, the first thing we're gonna do is we're going to start to collect all this data. We're gonna find all this data. I mean, the modern computer systems that we have infrastructure systems. We are producing many hundreds of gigabytes, sometimes terabytes of logging data every day. The majority of it gives far 13. I mean, we throw the majority of their logging information away or if it's not thrown away, it's stored some way for security purposes and never analyzed. So let's start by taking their data and actually analyzing it. To do that, we have laid and correlated, >>so we >>gotta put it all together. We've got a match it and we've got to start building patents. We're going to start looking for the patterns. This is where I is particularly good at starting to help us. Bold patterns start to look for those patterns. Initially, humans will have to do some training. Um, once we have that patent, once we've got that working, we can now start having the AI systems start to do some affairs. E, here's the recalls. So we the system can tell us based on the data based on the patterns we've been learning. We know from the past debt. If those three network links get full bad example, we're gonna have a failure in Region X, right. So start telling us while those network links of filling up tell us before they fall rather than after their full always they're falling up as we see trending information now seems like a simple I could do trending information with just normal monitoring systems. But if I can start to correlate that with greater users in, you know, Beijing Office versus Users in California office filling up those links and different times of the day, I can now start to make much more clever decisions, which is a human on its own, to try and correlate that information, which is be insane once you've done that way to go to the next stage, which is not to have the system act do actions for us. Based on that information right now, we're starting to get close to the scan it. Speaking of this doesn't have to be a big, complex pile of change. Smart ai solution. I have data on that AI solution is talking to my existing automation solutions to action. That change. That's how I see this moving forward, >>right? So essentially you, instead of saying, you know, deploy this too. Uh, this workload to AWS, you would say deploy this. Yeah, And then the system would look and go. Okay, It's this kind of workload. At this time of day at this size, it's gonna interact with this and this and this. And so it's gonna be best off in this region of this cloud provider on then. Uh, you know, two days from now, when the prices drop, we're gonna put it over there, >>even taking a different different. Spoken exactly that it could be. The Beijing office is coming online. Let's move the majority of the workload to a cloud that's closer to them. Reducing the network bandwidth. Yeah, and inference. Andi Also reducing the impact on international lines as Beijing winds down for the day, I can just move the majority of the workload into California on board Europe. In between, it's very simple examples, but have humans do that would be very complex and very time consuming >>exactly. And end. Just having humans notice those patterns would be difficult. But once you have the system noticing those patterns, then the humans could start to think, How can I take advantage of this, you know, So as you are talking about much longer term in the actual applicant patients themselves. So you know, everything can be optimized that way so >>everybody may optimized way can optimize down to the way we even potentially write applications in the future. Humans were still deciding the base logic. Humans were still deciding the creative components of that. Right as we as we build things, we can start to optimize them, breaking down into smaller and smaller units that are much more specific. But the complexity goes up. When we do that right. I want to use AI and AI solutions to start to manage that complexity across multiple spaces. Multiple time zones, etcetera. >>Exactly. Exactly. So. So that's the question, you know. What do you guys think? You know, we really want to know >>on Dhere again. You know, we mentioned this around the beginning, but do you think you could trust in a iob sedition? What would it take for you to trust in our absolution? And where do you practically see it being used in the short term? >>Yeah, that's that's the big question is where do you see it being used? Where would you like it to be used, you know? Is there something that you don't think would be possible, but you would like to see it, you know. But the main thing is, on a practical matter, what would you like to see? >>Let me ask. The question is like a different way. Do you have a problem that we could solve within a isolation today? E, They're really well >>right. A re a world problem. And And assuming that, you know, we are not gonna, you know, take over the world. >>Yeah. Important. My evil plan is to take over the world with >>man. I'm so sorry. First >>had to let that draw. >>I did. I did. I'm so sorry. Okay, Alright. So that's so That's a I ops. And we like I said, if you're watching this live, throw in the chat. We want to hear your ideas. If your, um if you're doing this, if you're watching this on the replay, go to the survey because we way, we really want to hear your ideas and your opinions. All right, So moving right along. All right. What the heck are you know, kernels? >>Uh, lovely questions. So, you know, the whole world is talking about containers today way we're talking about containers today. But containers like VMS or just one way to handle compute Andi. They're more and more ideas that are out there today, and people have been trying different ways off, shrinking the size of the compute environment. COMPUTER Paxil Another cool way of looking at this and saying That's been around for a little while. But it's getting your attraction to learn to sing called unique kernels, and what they are is they're basically highly optimized. Execute a bles that include the operating system, Um, there on OS settle libraries, um, and some very simplified application code all mixed into a very, very tiny package. Easiest way to describe them. They're super simplified. And I were talking about in the eye ops discussion this idea off taking everything into smaller and smaller individual functions but creating a certain level of complexity. Well, if we look at uni kernels, those are those smaller and smaller bundles and functions. They interact directly with the hardware or through a hyper visor. Um, so actually, no overhead. I mean the overhead If you just look at what a modern you clinics operating system is made up of these days, there are so many different parts and components. Even just the colonel has got anything from, you know, 5 to 7 different parts to it. Plus, of course, drivers and a boot loader. Then we look at the system libraries that set on top of that, you know? And then they're demons and utilities and shells and scream components and, you know, additional colonel stacks that go on top of that for hyper visors. What we're trying to say is, what, This text of space, I'm >>getting tired. Just listening, >>Thio. I'm tired talking about it. You know that the unique colonel, really, it just takes over their complexity. It puts the application the OS on the basic libraries necessary. That application in tow, one really tiny package. Um, yeah. Give you an idea what we're talking about here. We're talking about memory footprints or time package footprints in the kilobytes. You know, a small container is considered 100 make plus, we're talking kilobytes. We're talking memory utilization in the kilobyte two megabytes space because there's no no fact, no fluff, no unnecessary components. And then only the CPU that it needs. >>So Bill Gates was right 6. 40 k is all anybody will ever need >>Potentially. Yeah, right. E, there was there was an IBM CEO who said even less at some point. So we'll see >>how that go. What goes around comes around. >>But one of the really interesting things about this small size, which is really critical, is how fast they can boot. Yeah, we're talking boot times measured in 30 seconds. Wow, We're talking the ability to spin up specific functions only when you need them. Now, if we look at the knock on effect of that, we're looking at power saving. Who knew? Run the app when I need it because there's no Leighton. See to start it up. The app is tiny so I can pack a lot mawr into a lot less space game power seconds. But when I start looking at where you were talking about earlier, which the basic compute idea in the world all of a sudden that tiny little arm chips it in my raspberry pi that's running my fridge, My raspberry pi equivalent that's running my fridge no longer has a fact operating system around it. I can run tens thousands, potentially off these very tiny specific devices when I need them. Wow, I'm kind of excited about it. I'm excited by the idea. You >>can hear that >>I'm a hardware geek from from many, many moons ago on DSO. I kind of like the idea of being able to better utilize along this very low powered hardware that we have lying around and really take it into the future. Well, that's good. Yeah. So I'm not going to kill, not going to kill containers. But it is a parallel technology that I'm very interested in >>that that is true. Now what does it I mean in terms of, like, attack surface. That means it's got a much smaller attack surface, though, right? >>Yeah. Great. Great point. I mean, there's no there's no fluff. There's no extra components in the system. Therefore, the attack surface is very, very small. Um, you know, and because they're so small and can be distributed much, much faster and much more easily updating and upgrading them as much easier way can we can upgrade a 60 k b file across a GPRS connection on which I certainly can't do with 600 make, uh, four gig VM 600 made container. You know, just unrealistic. Um, e >>I was just going to say so. So now these. You know, kernels, they're they're so small. And they have on Lee what they absolutely need. Now, how do you access the hardware? >>So the hardware is accessed via hyper visor. So you have to have some kind of hyper visor running on top of the hard way. But because Because we need very little from their type adviser, we don't actually need to interact with that very much. It could be a very cut down operating system. Very, very simplified operating system. We're also not trying to run another layer on top of that. We're not We're not ending up with multiple potential VMS or something underneath it were completely removed. That layer, um, the the drivers, the necessary drivers are built into that particular colonel device. >>Oh, okay. That makes sense. >>Tiny footprint easily distributed, um, and once again, very specialized, >>right? Right. Well, that makes sense. Okay. So, yeah, I mean, I guess so. These these individual stacks, you know, comparing virtual machines to containers to unit colonels, there just a completely different architecture. But I can see how that would How That would work where you have the hi perverse. A little hyper buys are on top of rented teeth. OK, so moving right along certain. Where do we see these being used? >>Um, it's early days, although there are some very good practical applications out there. There's a big, big ecosystem of people trying different ways for this I o ts off the obvious immediate place. I i o t s a quick, easy place for something very specialized. Um, what's interesting to me? And you mentioned this earlier. You know, we're talking about medical devices. We're talking about potentially disposable medical devices. Now, if I can keep those devices to run on really low power very, very cheap, um, CPUs and all of a sudden I've got a device that is available to a lot more people. I don't need a massive, powerful CPU. I just need saying that runs a very specific function really fast, A very small scale. I could do well disposable devices. I can build medical devices that are so small we can potentially swallow them and other areas which are really interesting. And I spoke a little bit about it, but it's energy efficiency. Where We need to be very, very energy efficient. No. And that can also impact on massively scalable systems where I want to deal with tens of thousands of potential transactions from users going into a system. I can spin them up only when I need them. I don't need to keep them running all the time again. It comes back to that low latency on then. Anyway, that an incredibly fast food time is valuable. Um, a car, you know, Think about it. If if my if my electric car is constantly draining that battery when it's parked in the garage and I'm traveling or if it takes 20 minutes from my car to boot up its clinics. Colonel, when I wanted, I'm going to get very irritated. Well, >>that and if you have a specific function, you know, like, identify that thing, Yeah, it would be good if you haven't smashed into it before. Identified it as a baby carriage e dark today. Yes. >>So, Nick, you know, these is all really interesting topics. Um, yeah. We spoke about air ops. We spoke about the impact is gonna have on humans. Um, all of these changes to the world that we're living in from computer systems, the impact it's having on our lives biggest. An interesting question about the ethics of all of this >>ethics of all of this. Yes, because let's be let's be realistic. There are actual riel concerns when it comes to privacy, when it comes to how corporations operate, when it comes to how governments operate. Um, there are areas of the world's where, how all of this has has moved, it's absolutely I'll be honest, absolutely terrifying the economic disparity. Um, but when you really come right down to it, um, it's all about the human control over the technology because all of these ethical issues are are in our hands. Okay, we could joke about Sky Net. We can joke about things like that, but this is one place that technology can't help us. We have to do this. We have to be aware of what's going on. We have to be aware. Are they using facial recognition? Uh, you know, when you go to X y Z, are they using recidivism algorithms in sentencing? And how is that? How is that going? Is it? Are those algorithms fair? Certain groups get longer sentences because historical data, uh, is skewed. Be educated. Know how this works? Don't be afraid of any of this. None of this is, uh, none of this is rocket science. Really? Come right down to it. I mean, it's it's not simple, but you can learn this. You can do it. >>Ask good questions. Be interested to be part of the part of the discussion. Not just a passive bystander. >>Exactly. Don't just complain about what you think is going on. Learn about what is actually going on and be active, where you see something that needs to be fixed. So that's what that's what we can do about it. We need to be aware that there's an issue or potential issues, and we need to step in and fix it. So that z myself box, I'll step down zone >>important topic. And it's one that we all can have influence on on bits one. Those who are us who are actually involved in building these systems for the future. We can help make sure that the rules are there. That's right. Systems are built correctly on that. We have open dialogues and discussions around these points and topics and on going away, was she? I think we're coming to the end of the time on hopefully we've kept everybody interested in some of the things that we think are cool for the future. And we're putting our efforts into E O. But I think we need to wrap this up now. So, Nick, great chatting to you is always >>always, always a pleasure, Sean. >>It's been an amazing week. Um, been amazing. Couple of weeks, everybody leading up to this event on bond. No, thank you, everybody for listening to us. Please go and download and try. Dr. Enterprise, Uh, the container card is available. Will post the links here to better understand what we've been doing. Go and have a look through the tutorial track. You'll hear my voice. I'm sure you'll hear next voice and make other people's voices through those tutorials. Hopefully, we keep you all interested and then going download and try lens, Please. Finally, we want your feedback. We're interested to hear what you think would be the great ideas. Good, Bad. Otherwise let us know what you think about products. We are striving to make them better all the time. >>Absolutely. And we want your involvement. Was it all right? Thank you all. Bye bye. Yeah,

(bright upbeat music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCUBE. We've got a great conversation featuring Arthur AI. I'm your host. I'm excited to have Adam Wenchel who's the Co-Founder and CEO. Thanks for joining us today, appreciate it. >> Yeah, thanks for having me on, John, looking forward to the conversation. >> I got to say, it's been an exciting world in AI or artificial intelligence. Just an explosion of interest kind of in the mainstream with the language models, which people don't really get, but they're seeing the benefits of some of the hype around OpenAI. Which kind of wakes everyone up to, "Oh, I get it now." And then of course the pessimism comes in, all the skeptics are out there. But this breakthrough in generative AI field is just awesome, it's really a shift, it's a wave. We've been calling it probably the biggest inflection point, then the others combined of what this can do from a surge standpoint, applications. I mean, all aspects of what we used to know is the computing industry, software industry, hardware, is completely going to get turbo. So we're totally obviously bullish on this thing. So, this is really interesting. So my first question is, I got to ask you, what's you guys taking? 'Cause you've been doing this, you're in it, and now all of a sudden you're at the beach where the big waves are. What's the explosion of interest is there? What are you seeing right now? >> Yeah, I mean, it's amazing, so for starters, I've been in AI for over 20 years and just seeing this amount of excitement and the growth, and like you said, the inflection point we've hit in the last six months has just been amazing. And, you know, what we're seeing is like people are getting applications into production using LLMs. I mean, really all this excitement just started a few months ago, with ChatGPT and other breakthroughs and the amount of activity and the amount of new systems that we're seeing hitting production already so soon after that is just unlike anything we've ever seen. So it's pretty awesome. And, you know, these language models are just, they could be applied in so many different business contexts and that it's just the amount of value that's being created is again, like unprecedented compared to anything. >> Adam, you know, you've been in this for a while, so it's an interesting point you're bringing up, and this is a good point. I was talking with my friend John Markoff, former New York Times journalist and he was talking about, there's been a lot of work been done on ethics. So there's been, it's not like it's new. It's like been, there's a lot of stuff that's been baking over many, many years and, you know, decades. So now everyone wakes up in the season, so I think that is a key point I want to get into some of your observations. But before we get into it, I want you to explain for the folks watching, just so we can kind of get a definition on the record. What's an LLM, what's a foundational model and what's generative ai? Can you just quickly explain the three things there? >> Yeah, absolutely. So an LLM or a large language model, it's just a large, they would imply a large language model that's been trained on a huge amount of data typically pulled from the internet. And it's a general purpose language model that can be built on top for all sorts of different things, that includes traditional NLP tasks like document classification and sentiment understanding. But the thing that's gotten people really excited is it's used for generative tasks. So, you know, asking it to summarize documents or asking it to answer questions. And these aren't new techniques, they've been around for a while, but what's changed is just this new class of models that's based on new architectures. They're just so much more capable that they've gone from sort of science projects to something that's actually incredibly useful in the real world. And there's a number of companies that are making them accessible to everyone so that you can build on top of them. So that's the other big thing is, this kind of access to these models that can power generative tasks has been democratized in the last few months and it's just opening up all these new possibilities. And then the third one you mentioned foundation models is sort of a broader term for the category that includes LLMs, but it's not just language models that are included. So we've actually seen this for a while in the computer vision world. So people have been building on top of computer vision models, pre-trained computer vision models for a while for image classification, object detection, that's something we've had customers doing for three or four years already. And so, you know, like you said, there are antecedents to like, everything that's happened, it's not entirely new, but it does feel like a step change. >> Yeah, I did ask ChatGPT to give me a riveting introduction to you and it gave me an interesting read. If we have time, I'll read it. It's kind of, it's fun, you get a kick out of it. "Ladies and gentlemen, today we're a privileged "to have Adam Wenchel, Founder of Arthur who's going to talk "about the exciting world of artificial intelligence." And then it goes on with some really riveting sentences. So if we have time, I'll share that, it's kind of funny. It was good. >> Okay. >> So anyway, this is what people see and this is why I think it's exciting 'cause I think people are going to start refactoring what they do. And I've been saying this on theCUBE now for about a couple months is that, you know, there's a scene in "Moneyball" where Billy Beane sits down with the Red Sox owner and the Red Sox owner says, "If people aren't rebuilding their teams on your model, "they're going to be dinosaurs." And it reminds me of what's happening right now. And I think everyone that I talk to in the business sphere is looking at this and they're connecting the dots and just saying, if we don't rebuild our business with this new wave, they're going to be out of business because there's so much efficiency, there's so much automation, not like DevOps automation, but like the generative tasks that will free up the intellect of people. Like just the simple things like do an intro or do this for me, write some code, write a countermeasure to a hack. I mean, this is kind of what people are doing. And you mentioned computer vision, again, another huge field where 5G things are coming on, it's going to accelerate. What do you say to people when they kind of are leaning towards that, I need to rethink my business? >> Yeah, it's 100% accurate and what's been amazing to watch the last few months is the speed at which, and the urgency that companies like Microsoft and Google or others are actually racing to, to do that rethinking of their business. And you know, those teams, those companies which are large and haven't always been the fastest moving companies are working around the clock. And the pace at which they're rolling out LLMs across their suite of products is just phenomenal to watch. And it's not just the big, the large tech companies as well, I mean, we're seeing the number of startups, like we get, every week a couple of new startups get in touch with us for help with their LLMs and you know, there's just a huge amount of venture capital flowing into it right now because everyone realizes the opportunities for transforming like legal and healthcare and content creation in all these different areas is just wide open. And so there's a massive gold rush going on right now, which is amazing. >> And the cloud scale, obviously horizontal scalability of the cloud brings us to another level. We've been seeing data infrastructure since the Hadoop days where big data was coined. Now you're seeing this kind of take fruit, now you have vertical specialization where data shines, large language models all of a set up perfectly for kind of this piece. And you know, as you mentioned, you've been doing it for a long time. Let's take a step back and I want to get into how you started the company, what drove you to start it? Because you know, as an entrepreneur you're probably saw this opportunity before other people like, "Hey, this is finally it, it's here." Can you share the origination story of what you guys came up with, how you started it, what was the motivation and take us through that origination story. >> Yeah, absolutely. So as I mentioned, I've been doing AI for many years. I started my career at DARPA, but it wasn't really until 2015, 2016, my previous company was acquired by Capital One. Then I started working there and shortly after I joined, I was asked to start their AI team and scale it up. And for the first time I was actually doing it, had production models that we were working with, that was at scale, right? And so there was hundreds of millions of dollars of business revenue and certainly a big group of customers who were impacted by the way these models acted. And so it got me hyper-aware of these issues of when you get models into production, it, you know. So I think people who are earlier in the AI maturity look at that as a finish line, but it's really just the beginning and there's this constant drive to make them better, make sure they're not degrading, make sure you can explain what they're doing, if they're impacting people, making sure they're not biased. And so at that time, there really weren't any tools to exist to do this, there wasn't open source, there wasn't anything. And so after a few years there, I really started talking to other people in the industry and there was a really clear theme that this needed to be addressed. And so, I joined with my Co-Founder John Dickerson, who was on the faculty in University of Maryland and he'd been doing a lot of research in these areas. And so we ended up joining up together and starting Arthur. >> Awesome. Well, let's get into what you guys do. Can you explain the value proposition? What are people using you for now? Where's the action? What's the customers look like? What do prospects look like? Obviously you mentioned production, this has been the theme. It's not like people woke up one day and said, "Hey, I'm going to put stuff into production." This has kind of been happening. There's been companies that have been doing this at scale and then yet there's a whole follower model coming on mainstream enterprise and businesses. So there's kind of the early adopters are there now in production. What do you guys do? I mean, 'cause I think about just driving the car off the lot is not, you got to manage operations. I mean, that's a big thing. So what do you guys do? Talk about the value proposition and how you guys make money? >> Yeah, so what we do is, listen, when you go to validate ahead of deploying these models in production, starts at that point, right? So you want to make sure that if you're going to be upgrading a model, if you're going to replacing one that's currently in production, that you've proven that it's going to perform well, that it's going to be perform ethically and that you can explain what it's doing. And then when you launch it into production, traditionally data scientists would spend 25, 30% of their time just manually checking in on their model day-to-day babysitting as we call it, just to make sure that the data hasn't drifted, the model performance hasn't degraded, that a programmer did make a change in an upstream data system. You know, there's all sorts of reasons why the world changes and that can have a real adverse effect on these models. And so what we do is bring the same kind of automation that you have for other kinds of, let's say infrastructure monitoring, application monitoring, we bring that to your AI systems. And that way if there ever is an issue, it's not like weeks or months till you find it and you find it before it has an effect on your P&L and your balance sheet, which is too often before they had tools like Arthur, that was the way they were detected. >> You know, I was talking to Swami at Amazon who I've known for a long time for 13 years and been on theCUBE multiple times and you know, I watched Amazon try to pick up that sting with stage maker about six years ago and so much has happened since then. And he and I were talking about this wave, and I kind of brought up this analogy to how when cloud started, it was, Hey, I don't need a data center. 'Cause when I did my startup that time when Amazon, one of my startups at that time, my choice was put a box in the colo, get all the configuration before I could write over the line of code. So the cloud became the benefit for that and you can stand up stuff quickly and then it grew from there. Here it's kind of the same dynamic, you don't want to have to provision a large language model or do all this heavy lifting. So that seeing companies coming out there saying, you can get started faster, there's like a new way to get it going. So it's kind of like the same vibe of limiting that heavy lifting. >> Absolutely. >> How do you look at that because this seems to be a wave that's going to be coming in and how do you guys help companies who are going to move quickly and start developing? >> Yeah, so I think in the race to this kind of gold rush mentality, race to get these models into production, there's starting to see more sort of examples and evidence that there are a lot of risks that go along with it. Either your model says things, your system says things that are just wrong, you know, whether it's hallucination or just making things up, there's lots of examples. If you go on Twitter and the news, you can read about those, as well as sort of times when there could be toxic content coming out of things like that. And so there's a lot of risks there that you need to think about and be thoughtful about when you're deploying these systems. But you know, you need to balance that with the business imperative of getting these things into production and really transforming your business. And so that's where we help people, we say go ahead, put them in production, but just make sure you have the right guardrails in place so that you can do it in a smart way that's going to reflect well on you and your company. >> Let's frame the challenge for the companies now that you have, obviously there's the people who doing large scale production and then you have companies maybe like as small as us who have large linguistic databases or transcripts for example, right? So what are customers doing and why are they deploying AI right now? And is it a speed game, is it a cost game? Why have some companies been able to deploy AI at such faster rates than others? And what's a best practice to onboard new customers? >> Yeah, absolutely. So I mean, we're seeing across a bunch of different verticals, there are leaders who have really kind of started to solve this puzzle about getting AI models into production quickly and being able to iterate on them quickly. And I think those are the ones that realize that imperative that you mentioned earlier about how transformational this technology is. And you know, a lot of times, even like the CEOs or the boards are very personally kind of driving this sense of urgency around it. And so, you know, that creates a lot of movement, right? And so those companies have put in place really smart infrastructure and rails so that people can, data scientists aren't encumbered by having to like hunt down data, get access to it. They're not encumbered by having to stand up new platforms every time they want to deploy an AI system, but that stuff is already in place. There's a really nice ecosystem of products out there, including Arthur, that you can tap into. Compared to five or six years ago when I was building at a top 10 US bank, at that point you really had to build almost everything yourself and that's not the case now. And so it's really nice to have things like, you know, you mentioned AWS SageMaker and a whole host of other tools that can really accelerate things. >> What's your profile customer? Is it someone who already has a team or can people who are learning just dial into the service? What's the persona? What's the pitch, if you will, how do you align with that customer value proposition? Do people have to be built out with a team and in play or is it pre-production or can you start with people who are just getting going? >> Yeah, people do start using it pre-production for validation, but I think a lot of our customers do have a team going and they're starting to put, either close to putting something into production or about to, it's everything from large enterprises that have really sort of complicated, they have dozens of models running all over doing all sorts of use cases to tech startups that are very focused on a single problem, but that's like the lifeblood of the company and so they need to guarantee that it works well. And you know, we make it really easy to get started, especially if you're using one of the common model development platforms, you can just kind of turn key, get going and make sure that you have a nice feedback loop. So then when your models are out there, it's pointing out, areas where it's performing well, areas where it's performing less well, giving you that feedback so that you can make improvements, whether it's in training data or futurization work or algorithm selection. There's a number of, you know, depending on the symptoms, there's a number of things you can do to increase performance over time and we help guide people on that journey. >> So Adam, I have to ask, since you have such a great customer base and they're smart and they got teams and you're on the front end, I mean, early adopters is kind of an overused word, but they're killing it. They're putting stuff in the production's, not like it's a test, it's not like it's early. So as the next wave comes of fast followers, how do you see that coming online? What's your vision for that? How do you see companies that are like just waking up out of the frozen, you know, freeze of like old IT to like, okay, they got cloud, but they're not yet there. What do you see in the market? I see you're in the front end now with the top people really nailing AI and working hard. What's the- >> Yeah, I think a lot of these tools are becoming, or every year they get easier, more accessible, easier to use. And so, you know, even for that kind of like, as the market broadens, it takes less and less of a lift to put these systems in place. And the thing is, every business is unique, they have their own kind of data and so you can use these foundation models which have just been trained on generic data. They're a great starting point, a great accelerant, but then, in most cases you're either going to want to create a model or fine tune a model using data that's really kind of comes from your particular customers, the people you serve and so that it really reflects that and takes that into account. And so I do think that these, like the size of that market is expanding and its broadening as these tools just become easier to use and also the knowledge about how to build these systems becomes more widespread. >> Talk about your customer base you have now, what's the makeup, what size are they? Give a taste a little bit of a customer base you got there, what's they look like? I'll say Capital One, we know very well while you were at there, they were large scale, lot of data from fraud detection to all kinds of cool stuff. What do your customers now look like? >> Yeah, so we have a variety, but I would say one area we're really strong, we have several of the top 10 US banks, that's not surprising, that's a strength for us, but we also have Fortune 100 customers in healthcare, in manufacturing, in retail, in semiconductor and electronics. So what we find is like in any sort of these major verticals, there's typically, you know, one, two, three kind of companies that are really leading the charge and are the ones that, you know, in our opinion, those are the ones that for the next multiple decades are going to be the leaders, the ones that really kind of lead the charge on this AI transformation. And so we're very fortunate to be working with some of those. And then we have a number of startups as well who we love working with just because they're really pushing the boundaries technologically and so they provide great feedback and make sure that we're continuing to innovate and staying abreast of everything that's going on. >> You know, these early markups, even when the hyperscalers were coming online, they had to build everything themselves. That's the new, they're like the alphas out there building it. This is going to be a big wave again as that fast follower comes in. And so when you look at the scale, what advice would you give folks out there right now who want to tee it up and what's your secret sauce that will help them get there? >> Yeah, I think that the secret to teeing it up is just dive in and start like the, I think these are, there's not really a secret. I think it's amazing how accessible these are. I mean, there's all sorts of ways to access LLMs either via either API access or downloadable in some cases. And so, you know, go ahead and get started. And then our secret sauce really is the way that we provide that performance analysis of what's going on, right? So we can tell you in a very actionable way, like, hey, here's where your model is doing good things, here's where it's doing bad things. Here's something you want to take a look at, here's some potential remedies for it. We can help guide you through that. And that way when you're putting it out there, A, you're avoiding a lot of the common pitfalls that people see and B, you're able to really kind of make it better in a much faster way with that tight feedback loop. >> It's interesting, we've been kind of riffing on this supercloud idea because it was just different name than multicloud and you see apps like Snowflake built on top of AWS without even spending any CapEx, you just ride that cloud wave. This next AI, super AI wave is coming. I don't want to call AIOps because I think there's a different distinction. If you, MLOps and AIOps seem a little bit old, almost a few years back, how do you view that because everyone's is like, "Is this AIOps?" And like, "No, not kind of, but not really." How would you, you know, when someone says, just shoots off the hip, "Hey Adam, aren't you doing AIOps?" Do you say, yes we are, do you say, yes, but we do differently because it's doesn't seem like it's the same old AIOps. What's your- >> Yeah, it's a good question. AIOps has been a term that was co-opted for other things and MLOps also has people have used it for different meanings. So I like the term just AI infrastructure, I think it kind of like describes it really well and succinctly. >> But you guys are doing the ops. I mean that's the kind of ironic thing, it's like the next level, it's like NextGen ops, but it's not, you don't want to be put in that bucket. >> Yeah, no, it's very operationally focused platform that we have, I mean, it fires alerts, people can action off them. If you're familiar with like the way people run security operations centers or network operations centers, we do that for data science, right? So think of it as a DSOC, a Data Science Operations Center where all your models, you might have hundreds of models running across your organization, you may have five, but as problems are detected, alerts can be fired and you can actually work the case, make sure they're resolved, escalate them as necessary. And so there is a very strong operational aspect to it, you're right. >> You know, one of the things I think is interesting is, is that, if you don't mind commenting on it, is that the aspect of scale is huge and it feels like that was made up and now you have scale and production. What's your reaction to that when people say, how does scale impact this? >> Yeah, scale is huge for some of, you know, I think, I think look, the highest leverage business areas to apply these to, are generally going to be the ones at the biggest scale, right? And I think that's one of the advantages we have. Several of us come from enterprise backgrounds and we're used to doing things enterprise grade at scale and so, you know, we're seeing more and more companies, I think they started out deploying AI and sort of, you know, important but not necessarily like the crown jewel area of their business, but now they're deploying AI right in the heart of things and yeah, the scale that some of our companies are operating at is pretty impressive. >> John: Well, super exciting, great to have you on and congratulations. I got a final question for you, just random. What are you most excited about right now? Because I mean, you got to be pretty pumped right now with the way the world is going and again, I think this is just the beginning. What's your personal view? How do you feel right now? >> Yeah, the thing I'm really excited about for the next couple years now, you touched on it a little bit earlier, but is a sort of convergence of AI and AI systems with sort of turning into AI native businesses. And so, as you sort of do more, get good further along this transformation curve with AI, it turns out that like the better the performance of your AI systems, the better the performance of your business. Because these models are really starting to underpin all these key areas that cumulatively drive your P&L. And so one of the things that we work a lot with our customers is to do is just understand, you know, take these really esoteric data science notions and performance and tie them to all their business KPIs so that way you really are, it's kind of like the operating system for running your AI native business. And we're starting to see more and more companies get farther along that maturity curve and starting to think that way, which is really exciting. >> I love the AI native. I haven't heard any startup yet say AI first, although we kind of use the term, but I guarantee that's going to come in all the pitch decks, we're an AI first company, it's going to be great run. Adam, congratulations on your success to you and the team. Hey, if we do a few more interviews, we'll get the linguistics down. We can have bots just interact with you directly and ask you, have an interview directly. >> That sounds good, I'm going to go hang out on the beach, right? So, sounds good. >> Thanks for coming on, really appreciate the conversation. Super exciting, really important area and you guys doing great work. Thanks for coming on. >> Adam: Yeah, thanks John. >> Again, this is Cube Conversation. I'm John Furrier here in Palo Alto, AI going next gen. This is legit, this is going to a whole nother level that's going to open up huge opportunities for startups, that's going to use opportunities for investors and the value to the users and the experience will come in, in ways I think no one will ever see. So keep an eye out for more coverage on siliconangle.com and theCUBE.net, thanks for watching. (bright upbeat music)

>> theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (ambient music) >> Lisa: Hey everyone, welcome back to Barcelona, Spain. It's theCUBE Live at MWC 23. Lisa Martin with Dave Vellante. This is day two of four days of cube coverage but you know that, because you've already been watching yesterday and today. We're going to have a great conversation next with EnterpriseWeb and Red Hat. We've had great conversations the last day and a half about the Telco industry, the challenges, the opportunities. We're going to unpack that from this lens. Please welcome Dave Duggal, founder and CEO of EnterpriseWeb and Azhar Sayeed is here, Senior Director Solution Architecture at Red Hat. >> Guys, it's great to have you on the program. >> Yes. >> Thank you Lisa, >> Great being here with you. >> Dave let's go ahead and start with you. Give the audience an overview of EnterpriseWeb. What kind of business is it? What's the business model? What do you guys do? >> Okay so, EnterpriseWeb is reinventing middleware, right? So the historic middleware was to build vertically integrated stacks, right? And those stacks are now such becoming the rate limiters for interoperability for so the end-to-end solutions that everybody's looking for, right? Red Hat's talking about the unified platform. You guys are talking about Supercloud, EnterpriseWeb addresses that we've built middleware based on serverless architecture, so lightweight, low latency, high performance middleware. And we're working with the world's biggest, we sell through channels and we work through partners like Red Hat Intel, Fortnet, Keysight, Tech Mahindra. So working with some of the biggest players that have recognized the value of our innovation, to deliver transformation to the Telecom industry. >> So what are you guys doing together? Is this, is this an OpenShift play? >> Is it? >> Yeah. >> Yeah, so we've got two projects right her on the floor at MWC throughout the various partners, where EnterpriseWeb is actually providing an application layer, sorry application middleware over Red Hat's, OpenShift and we're essentially generating operators so Red Hat operators, so that all our vendors, and, sorry vendors that we onboard into our catalog can be deployed easily through the OpenShift platform. And we allow those, those vendors to be flexibly composed into network services. So the real challenge for operators historically is that they, they have challenges onboarding the vendors. It takes a long time. Each one of them is a snowflake. They, you know, even though there's standards they don't all observe or follow the same standards. So we make it easier using models, right? For, in a model driven process to on boards or streamline that onboarding process, compose functions into services deploy those services seamlessly through Red Hat's OpenShift, and then manage the, the lifecycle, like the quality of service and the SLAs for those services. >> So Red Hat obviously has pretty prominent Telco business has for a while. Red Hat OpenStack actually is is pretty popular within the Telco business. People thought, "Oh, OpenStack, that's dead." Actually, no, it's actually doing quite well. We see it all over the place where for whatever reason people want to build their own cloud. And, and so, so what's happening in the industry because you have the traditional Telcos we heard in the keynotes that kind of typical narrative about, you know, we can't let the over the top vendors do this again. We're, we're going to be Apifi everything, we're going to monetize this time around, not just with connectivity but the, but the fact is they really don't have a developer community. >> Yes. >> Yet anyway. >> Then you have these disruptors over here that are saying "Yeah, we're going to enable ISVs." How do you see it? What's the landscape look like? Help us understand, you know, what the horses on the track are doing. >> Sure. I think what has happened, Dave, is that the conversation has moved a little bit from where they were just looking at IS infrastructure service with virtual machines and OpenStack, as you mentioned, to how do we move up the value chain and look at different applications. And therein comes the rub, right? You have applications with different requirements, IT network that have various different requirements that are there. So as you start to build those cloud platform, as you start to modernize those set of applications, you then start to look at microservices and how you build them. You need the ability to orchestrate them. So some of those problem statements have moved from not just refactoring those applications, but actually now to how do you reliably deploy, manage in a multicloud multi cluster way. So this conversation around Supercloud or this conversation around multicloud is very >> You could say Supercloud. That's okay >> (Dave Duggal and Azhar laughs) >> It's absolutely very real though. The reason why it's very real is, if you look at transformations around Telco, there are two things that are happening. One, Telco IT, they're looking at partnerships with hybrid cloud, I mean with public cloud players to build a hybrid environment. They're also building their own Telco Cloud environment for their network functions. Now, in both of those spaces, they end up operating two to three different environments themselves. Now how do you create a level of abstraction across those? How do you manage that particular infrastructure? And then how do you orchestrate all of those different workloads? Those are the type of problems that they're actually beginning to solve. So they've moved on from really just putting that virtualizing their application, putting it on OpenStack to now really seriously looking at "How do I build a service?" "How do I leverage the catalog that's available both in my private and public and build an overall service process?" >> And by the way what you just described as hybrid cloud and multicloud is, you know Supercloud is what multicloud should have been. And what, what it originally became is "I run on this cloud and I run on this cloud" and "I run on this cloud and I have a hybrid." And, and Supercloud is meant to create a common experience across those clouds. >> Dave Duggal: Right? >> Thanks to, you know, Supercloud middleware. >> Yeah. >> Right? And, and so that's what you guys do. >> Yeah, exactly. Exactly. Dave, I mean, even the name EnterpriseWeb, you know we started from looking from the application layer down. If you look at it, the last 10 years we've looked from the infrastructure up, right? And now everybody's looking northbound saying "You know what, actually, if I look from the infrastructure up the only thing I'll ever build is silos, right?" And those silos get in the way of the interoperability and the agility the businesses want. So we take the perspective as high level abstractions, common tools, so that if I'm a CXO, I can look down on my environments, right? When I'm really not, I honestly, if I'm an, if I'm a CEO I don't really care or CXO, I don't really care so much about my infrastructure to be honest. I care about my applications and their behavior. I care about my SLAs and my quality of service, right? Those are the things I care about. So I really want an EnterpriseWeb, right? Something that helps me connect all my distributed applications all across all of the environments. So I can have one place a consistency layer that speaks a common language. We know that there's a lot of heterogeneity down all those layers and a lot of complexity down those layers. But the business doesn't care. They don't want to care, right? They want to actually take their applications deploy them where they're the most performant where they're getting the best cost, right? The lowest and maybe sustainability concerns, all those. They want to address those problems, meet their SLAs meet their quality service. And you know what, if it's running on Amazon, great. If it's running on Google Cloud platform, great. If it, you know, we're doing one project right here that we're demonstrating here is with with Amazon Tech Mahindra and OpenShift, where we took a disaggregated 5G core, right? So this is like sort of latest telecom, you know net networking software, right? We're deploying pulling elements of that network across core, across Amazon EKS, OpenShift on Red Hat ROSA, as well as just OpenShift for cloud. And we, through a single pane of deployment and management, we deployed the elements of the 5G core across them and then connected them in an end-to-end process. That's Telco Supercloud. >> Dave Vellante: So that's an O-RAN deployment. >> Yeah that's >> So, the big advantage of that, pardon me, Dave but the big advantage of that is the customer really doesn't care where the components are being served from for them. It's a 5G capability. It happens to sit in different locations. And that's, it's, it's about how do you abstract and how do you manage all those different workloads in a cohesive way? And that's exactly what EnterpriseWeb is bringing to the table. And what we do is we abstract the underlying infrastructure which is the cloud layer. So if, because AWS operating environment is different then private cloud operating environment then Azure environment, you have the networking is set up is different in each one of them. If there is a way you can abstract all of that and present it in a common operating model it becomes a lot easier than for anybody to be able to consume. >> And what a lot of customers tell me is the way they deal with multicloud complexity is they go with mono cloud, right? And so they'll lose out on some of the best services >> Absolutely >> If best of, so that's not >> that's not ideal, but at the end of the day, agree, developers don't want to muck with all the plumbing >> Dave Duggal: Yep. >> They want to write code. >> Azhar: Correct. >> So like I come back to are the traditional Telcos leaning in on a way that they're going to enable ISVs and developers to write on top of those platforms? Or are there sort of new entrance and disruptors? And I know, I know the answer is both >> Dave Duggal: Yep. >> but I feel as though the Telcos still haven't, traditional Telcos haven't tuned in to that developer affinity, but you guys sell to them. >> What, what are you seeing? >> Yeah, so >> What we have seen is there are Telcos fall into several categories there. If you look at the most mature ones, you know they are very eager to move up the value chain. There are some smaller very nimble ones that have actually doing, they're actually doing something really interesting. For example, they've provided sandbox environments to developers to say "Go develop your applications to the sandbox environment." We'll use that to build an net service with you. I can give you some interesting examples across the globe that, where that is happening, right? In AsiaPac, particularly in Australia, ANZ region. There are a couple of providers who have who have done this, but in, in, in a very interesting way. But the challenges to them, why it's not completely open or public yet is primarily because they haven't figured out how to exactly monetize that. And, and that's the reason why. So in the absence of that, what will happen is they they have to rely on the ISV ecosystem to be able to build those capabilities which they can then bring it on as part of the catalog. But in Latin America, I was talking to one of the providers and they said, "Well look we have a public cloud, we have our own public cloud, right?" What we want do is use that to offer localized services not just bring everything in from the top >> But, but we heard from Ericson's CEO they're basically going to monetize it by what I call "gouge", the developers >> (Azhar laughs) >> access to the network telemetry as opposed to saying, "Hey, here's an open platform development on top of it and it will maybe create something like an app store and we'll take a piece of the action." >> So ours, >> to be is a better model. >> Yeah. So that's perfect. Our second project that we're showing here is with Intel, right? So Intel came to us cause they are a reputation for doing advanced automation solutions. They gave us carte blanche in their labs. So this is Intel Network Builders they said pick your partners. And we went with the Red Hat, Fort Net, Keysite this company KX doing AIML. But to address your DevX, here's Intel explicitly wants to get closer to the developers by exposing their APIs, open APIs over their infrastructure. Just like Red Hat has APIs, right? And so they can expose them northbound to developers so developers can leverage and tune their applications, right? But the challenge there is what Intel is doing at the low level network infrastructure, right? Is fundamentally complex, right? What you want is an abstraction layer where develop and this gets to, to your point Dave where you just said like "The developers just want to get their job done." or really they want to focus on the business logic and accelerate that service delivery, right? So the idea here is an EnterpriseWeb they can literally declaratively compose their services, express their intent. "I want this to run optimized for low latency. I want this to run optimized for energy consumption." Right? And that's all they say, right? That's a very high level statement. And then the run time translates it between all the elements that are participating in that service to realize the developer's intent, right? No hands, right? Zero touch, right? So that's now a movement in telecom. So you're right, it's taking a while because these are pretty fundamental shifts, right? But it's intent based networking, right? So it's almost two parts, right? One is you have to have the open APIs, right? So that the infrastructure has to expose its capabilities. Then you need abstractions over the top that make it simple for developers to take, you know, make use of them. >> See, one of the demonstrations we are doing is around AIOps. And I've had literally here on this floor, two conversations around what I call as network as a platform. Although it sounds like a cliche term, that's exactly what Dave was describing in terms of exposing APIs from the infrastructure and utilizing them. So once you get that data, then now you can do analytics and do machine learning to be able to build models and figure out how you can orchestrate better how you can monetize better, how can how you can utilize better, right? So all of those things become important. It's just not about internal optimization but it's also about how do you expose it to third party ecosystem to translate that into better delivery mechanisms or IOT capability and so on. >> But if they're going to charge me for every API call in the network I'm going to go broke (team laughs) >> And I'm going to get really pissed. I mean, I feel like, I'm just running down, Oracle. IBM tried it. Oracle, okay, they got Java, but they don't they don't have developer jobs. VMware, okay? They got Aria. EMC used to have a thing called code. IBM had to buy Red Hat to get to the developer community. (Lisa laughs) >> So I feel like the telcos don't today have those developer shops. So, so they have to partner. [Azhar] Yes. >> With guys like you and then be more open and and let a zillion flowers bloom or else they're going to get disrupted in a big way and they're going to it's going to be a repeat of the over, over the top in, in in a different model that I can't predict. >> Yeah. >> Absolutely true. I mean, look, they cannot be in the connectivity business. Telcos cannot be just in the connectivity business. It's, I think so, you know, >> Dave Vellante: You had a fry a frozen hand (Dave Daggul laughs) >> off that, you know. >> Well, you know, think about they almost have to go become over the top on themselves, right? That's what the cloud guys are doing, right? >> Yeah. >> They're riding over their backbone that by taking a creating a high level abstraction, they in turn abstract away the infrastructure underneath them, right? And that's really the end game >> Right? >> Dave Vellante: Yeah. >> Is because now, >> they're over the top it's their network, it's their infrastructure, right? They don't want to become bid pipes. >> Yep. >> Now you, they can take OpenShift, run that in any cloud. >> Yep. >> Right? >> You can run that in hybrid cloud, enterprise web can do the application layer configuration and management. And together we're running, you know, OSI layers one through seven, east to west, north to south. We're running across the the RAN, the core and the transport. And that is telco super cloud, my friend. >> Yeah. Well, >> (Dave Duggal laughs) >> I'm dominating the conversation cause I love talking super cloud. >> I knew you would. >> So speaking of super superpowers, when you're in customer or prospective customer conversations with providers and they've got, obviously they're they're in this transformative state right now. How, what do you describe as the superpower between Red Hat and EnterpriseWeb in terms of really helping these Telcos transforms. But at the end of the day, the connectivity's there the end user gets what they want, which is I want this to work wherever I am. >> Yeah, yeah. That's a great question, Lisa. So I think the way you could look at it is most software has, has been evolved to be specialized, right? So in Telcos' no different, right? We have this in the enterprise, right? All these specialized stacks, all these components that they wire together in the, in you think of Telco as a sort of a super set of enterprise problems, right? They have all those problems like magnified manyfold, right? And so you have specialized, let's say orchestrators and other tools for every Telco domain for every Telco layer. Now you have a zoo of orchestrators, right? None of them were designed to work together, right? They all speak a specific language, let's say quote unquote for doing a specific purpose. But everything that's interesting in the 21st century is across layers and across domains, right? If a siloed static application, those are dead, right? Nobody's doing those anymore. Even developers don't do those developers are doing composition today. They're not doing, nobody wants to hear about a 6 million lines of code, right? They want to hear, "How did you take these five things and bring 'em together for productive use?" >> Lisa: Right. How did you deliver faster for my enterprise? How did you save me money? How did you create business value? And that's what we're doing together. >> I mean, just to add on to Dave, I was talking to one of the providers, they have more than 30,000 nodes in their infrastructure. When I say no to your servers running, you know, Kubernetes,running open stack, running different components. If try managing that in one single entity, if you will. Not possible. You got to fragment, you got to segment in some way. Now the question is, if you are not exposing that particular infrastructure and the appropriate KPIs and appropriate things, you will not be able to efficiently utilize that across the board. So you need almost a construct that creates like a manager of managers, a hierarchical structure, which would allow you to be more intelligent in terms of how you place those, how you manage that. And so when you ask the question about what's the secret sauce between the two, well this is exactly where EnterpriseWeb brings in that capability to analyze information, be more intelligent about it. And what we do is provide an abstraction of the cloud layer so that they can, you know, then do the right job in terms of making sure that it's appropriate and it's consistent. >> Consistency is key. Guys, thank you so much. It's been a pleasure really digging through EnterpriseWeb. >> Thank you. >> What you're doing >> with Red Hat. How you're helping the organization transform and Supercloud, we can't forget Supercloud. (Dave Vellante laughs) >> Fight Supercloud. Guys, thank you so much for your time. >> Thank you so much Lisa. >> Thank you. >> Thank you guys. >> Very nice. >> Lisa: We really appreciate it. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live tech coverage coming to you live from MWC 23. We'll be back after a short break.

(bright upbeat music) >> Hello, everyone. Welcome to SiliconANGLE News breaking story here. Amazon Web Services expanding their relationship with Hugging Face, breaking news here on SiliconANGLE. I'm John Furrier, SiliconANGLE reporter, founder, and also co-host of theCUBE. And I have with me, Swami, from Amazon Web Services, vice president of database, analytics, machine learning with AWS. Swami, great to have you on for this breaking news segment on AWS's big news. Thanks for coming on and taking the time. >> Hey, John, pleasure to be here. >> You know- >> Looking forward to it. >> We've had many conversations on theCUBE over the years, we've watched Amazon really move fast into the large data modeling, SageMaker became a very smashing success, obviously you've been on this for a while. Now with ChatGPT OpenAI, a lot of buzz going mainstream, takes it from behind the curtain inside the ropes, if you will, in the industry to a mainstream. And so this is a big moment, I think, in the industry, I want to get your perspective, because your news with Hugging Face, I think is another tell sign that we're about to tip over into a new accelerated growth around making AI now application aware, application centric, more programmable, more API access. What's the big news about, with AWS Hugging Face, you know, what's going on with this announcement? >> Yeah. First of all, they're very excited to announce our expanded collaboration with Hugging Face, because with this partnership, our goal, as you all know, I mean, Hugging Face, I consider them like the GitHub for machine learning. And with this partnership, Hugging Face and AWS, we'll be able to democratize AI for a broad range of developers, not just specific deep AI startups. And now with this, we can accelerate the training, fine tuning and deployment of these large language models, and vision models from Hugging Face in the cloud. And the broader context, when you step back and see what customer problem we are trying to solve with this announcement, essentially if you see these foundational models, are used to now create like a huge number of applications, suggest like tech summarization, question answering, or search image generation, creative, other things. And these are all stuff we are seeing in the likes of these ChatGPT style applications. But there is a broad range of enterprise use cases that we don't even talk about. And it's because these kind of transformative, generative AI capabilities and models are not available to, I mean, millions of developers. And because either training these elements from scratch can be very expensive or time consuming and need deep expertise, or more importantly, they don't need these generic models, they need them to be fine tuned for the specific use cases. And one of the biggest complaints we hear is that these models, when they try to use it for real production use cases, they are incredibly expensive to train and incredibly expensive to run inference on, to use it at a production scale. So, and unlike web search style applications, where the margins can be really huge, here in production use cases and enterprises, you want efficiency at scale. That's where Hugging Face and AWS share our mission. And by integrating with Trainium and Inferentia, we're able to handle the cost efficient training and inference at scale, I'll deep dive on it. And by teaming up on the SageMaker front, now the time it takes to build these models and fine tune them is also coming down. So that's what makes this partnership very unique as well. So I'm very excited. >> I want to get into the time savings and the cost savings as well on the training and inference, it's a huge issue, but before we get into that, just how long have you guys been working with Hugging Face? I know there's a previous relationship, this is an expansion of that relationship, can you comment on what's different about what's happened before and then now? >> Yeah. So, Hugging Face, we have had a great relationship in the past few years as well, where they have actually made their models available to run on AWS, you know, fashion. Even in fact, their Bloom Project was something many of our customers even used. Bloom Project, for context, is their open source project which builds a GPT-3 style model. And now with this expanded collaboration, now Hugging Face selected AWS for that next generation office generative AI model, building on their highly successful Bloom Project as well. And the nice thing is, now, by direct integration with Trainium and Inferentia, where you get cost savings in a really significant way, now, for instance, Trn1 can provide up to 50% cost to train savings, and Inferentia can deliver up to 60% better costs, and four x more higher throughput than (indistinct). Now, these models, especially as they train that next generation generative AI models, it is going to be, not only more accessible to all the developers, who use it in open, so it'll be a lot cheaper as well. And that's what makes this moment really exciting, because we can't democratize AI unless we make it broadly accessible and cost efficient and easy to program and use as well. >> Yeah. >> So very exciting. >> I'll get into the SageMaker and CodeWhisperer angle in a second, but you hit on some good points there. One, accessibility, which is, I call the democratization, which is getting this in the hands of developers, and/or AI to develop, we'll get into that in a second. So, access to coding and Git reasoning is a whole nother wave. But the three things I know you've been working on, I want to put in the buckets here and comment, one, I know you've, over the years, been working on saving time to train, that's a big point, you mentioned some of those stats, also cost, 'cause now cost is an equation on, you know, bundling whether you're uncoupling with hardware and software, that's a big issue. Where do I find the GPUs? Where's the horsepower cost? And then also sustainability. You've mentioned that in the past, is there a sustainability angle here? Can you talk about those three things, time, cost, and sustainability? >> Certainly. So if you look at it from the AWS perspective, we have been supporting customers doing machine learning for the past years. Just for broader context, Amazon has been doing ML the past two decades right from the early days of ML powered recommendation to actually also supporting all kinds of generative AI applications. If you look at even generative AI application within Amazon, Amazon search, when you go search for a product and so forth, we have a team called MFi within Amazon search that helps bring these large language models into creating highly accurate search results. And these are created with models, really large models with tens of billions of parameters, scales to thousands of training jobs every month and trained on large model of hardware. And this is an example of a really good large language foundation model application running at production scale, and also, of course, Alexa, which uses a large generator model as well. And they actually even had a research paper that showed that they are more, and do better in accuracy than other systems like GPT-3 and whatnot. So, and we also touched on things like CodeWhisperer, which uses generative AI to improve developer productivity, but in a responsible manner, because 40% of some of the studies show 40% of this generated code had serious security flaws in it. This is where we didn't just do generative AI, we combined with automated reasoning capabilities, which is a very, very useful technique to identify these issues and couple them so that it produces highly secure code as well. Now, all these learnings taught us few things, and which is what you put in these three buckets. And yeah, like more than 100,000 customers using ML and AI services, including leading startups in the generative AI space, like stability AI, AI21 Labs, or Hugging Face, or even Alexa, for that matter. They care about, I put them in three dimension, one is around cost, which we touched on with Trainium and Inferentia, where we actually, the Trainium, you provide to 50% better cost savings, but the other aspect is, Trainium is a lot more power efficient as well compared to traditional one. And Inferentia is also better in terms of throughput, when it comes to what it is capable of. Like it is able to deliver up to three x higher compute performance and four x higher throughput, compared to it's previous generation, and it is extremely cost efficient and power efficient as well. >> Well. >> Now, the second element that really is important is in a day, developers deeply value the time it takes to build these models, and they don't want to build models from scratch. And this is where SageMaker, which is, even going to Kaggle uses, this is what it is, number one, enterprise ML platform. What it did to traditional machine learning, where tens of thousands of customers use StageMaker today, including the ones I mentioned, is that what used to take like months to build these models have dropped down to now a matter of days, if not less. Now, a generative AI, the cost of building these models, if you look at the landscape, the model parameter size had jumped by more than thousand X in the past three years, thousand x. And that means the training is like a really big distributed systems problem. How do you actually scale these model training? How do you actually ensure that you utilize these efficiently? Because these machines are very expensive, let alone they consume a lot of power. So, this is where SageMaker capability to build, automatically train, tune, and deploy models really concern this, especially with this distributor training infrastructure, and those are some of the reasons why some of the leading generative AI startups are actually leveraging it, because they do not want a giant infrastructure team, which is constantly tuning and fine tuning, and keeping these clusters alive. >> It sounds like a lot like what startups are doing with the cloud early days, no data center, you move to the cloud. So, this is the trend we're seeing, right? You guys are making it easier for developers with Hugging Face, I get that. I love that GitHub for machine learning, large language models are complex and expensive to build, but not anymore, you got Trainium and Inferentia, developers can get faster time to value, but then you got the transformers data sets, token libraries, all that optimized for generator. This is a perfect storm for startups. Jon Turow, a former AWS person, who used to work, I think for you, is now a VC at Madrona Venture, he and I were talking about the generator AI landscape, it's exploding with startups. Every alpha entrepreneur out there is seeing this as the next frontier, that's the 20 mile stairs, next 10 years is going to be huge. What is the big thing that's happened? 'Cause some people were saying, the founder of Yquem said, "Oh, the start ups won't be real, because they don't all have AI experience." John Markoff, former New York Times writer told me that, AI, there's so much work done, this is going to explode, accelerate really fast, because it's almost like it's been waiting for this moment. What's your reaction? >> I actually think there is going to be an explosion of startups, not because they need to be AI startups, but now finally AI is really accessible or going to be accessible, so that they can create remarkable applications, either for enterprises or for disrupting actually how customer service is being done or how creative tools are being built. And I mean, this is going to change in many ways. When we think about generative AI, we always like to think of how it generates like school homework or arts or music or whatnot, but when you look at it on the practical side, generative AI is being actually used across various industries. I'll give an example of like Autodesk. Autodesk is a customer who runs an AWS and SageMaker. They already have an offering that enables generated design, where designers can generate many structural designs for products, whereby you give a specific set of constraints and they actually can generate a structure accordingly. And we see similar kind of trend across various industries, where it can be around creative media editing or various others. I have the strong sense that literally, in the next few years, just like now, conventional machine learning is embedded in every application, every mobile app that we see, it is pervasive, and we don't even think twice about it, same way, like almost all apps are built on cloud. Generative AI is going to be part of every startup, and they are going to create remarkable experiences without needing actually, these deep generative AI scientists. But you won't get that until you actually make these models accessible. And I also don't think one model is going to rule the world, then you want these developers to have access to broad range of models. Just like, go back to the early days of deep learning. Everybody thought it is going to be one framework that will rule the world, and it has been changing, from Caffe to TensorFlow to PyTorch to various other things. And I have a suspicion, we had to enable developers where they are, so. >> You know, Dave Vellante and I have been riffing on this concept called super cloud, and a lot of people have co-opted to be multicloud, but we really were getting at this whole next layer on top of say, AWS. You guys are the most comprehensive cloud, you guys are a super cloud, and even Adam and I are talking about ISVs evolving to ecosystem partners. I mean, your top customers have ecosystems building on top of it. This feels like a whole nother AWS. How are you guys leveraging the history of AWS, which by the way, had the same trajectory, startups came in, they didn't want to provision a data center, the heavy lifting, all the things that have made Amazon successful culturally. And day one thinking is, provide the heavy lifting, undifferentiated heavy lifting, and make it faster for developers to program code. AI's got the same thing. How are you guys taking this to the next level, because now, this is an opportunity for the competition to change the game and take it over? This is, I'm sure, a conversation, you guys have a lot of things going on in AWS that makes you unique. What's the internal and external positioning around how you take it to the next level? >> I mean, so I agree with you that generative AI has a very, very strong potential in terms of what it can enable in terms of next generation application. But this is where Amazon's experience and expertise in putting these foundation models to work internally really has helped us quite a bit. If you look at it, like amazon.com search is like a very, very important application in terms of what is the customer impact on number of customers who use that application openly, and the amount of dollar impact it does for an organization. And we have been doing it silently for a while now. And the same thing is true for like Alexa too, which actually not only uses it for natural language understanding other city, even national leverages is set for creating stories and various other examples. And now, our approach to it from AWS is we actually look at it as in terms of the same three tiers like we did in machine learning, because when you look at generative AI, we genuinely see three sets of customers. One is, like really deep technical expert practitioner startups. These are the startups that are creating the next generation models like the likes of stability AIs or Hugging Face with Bloom or AI21. And they generally want to build their own models, and they want the best price performance of their infrastructure for training and inference. That's where our investments in silicon and hardware and networking innovations, where Trainium and Inferentia really plays a big role. And we can nearly do that, and that is one. The second middle tier is where I do think developers don't want to spend time building their own models, let alone, they actually want the model to be useful to that data. They don't need their models to create like high school homeworks or various other things. What they generally want is, hey, I had this data from my enterprises that I want to fine tune and make it really work only for this, and make it work remarkable, can be for tech summarization, to generate a report, or it can be for better Q&A, and so forth. This is where we are. Our investments in the middle tier with SageMaker, and our partnership with Hugging Face and AI21 and co here are all going to very meaningful. And you'll see us investing, I mean, you already talked about CodeWhisperer, which is an open preview, but we are also partnering with a whole lot of top ISVs, and you'll see more on this front to enable the next wave of generated AI apps too, because this is an area where we do think lot of innovation is yet to be done. It's like day one for us in this space, and we want to enable that huge ecosystem to flourish. >> You know, one of the things Dave Vellante and I were talking about in our first podcast we just did on Friday, we're going to do weekly, is we highlighted the AI ChatGPT example as a horizontal use case, because everyone loves it, people are using it in all their different verticals, and horizontal scalable cloud plays perfectly into it. So I have to ask you, as you look at what AWS is going to bring to the table, a lot's changed over the past 13 years with AWS, a lot more services are available, how should someone rebuild or re-platform and refactor their application of business with AI, with AWS? What are some of the tools that you see and recommend? Is it Serverless, is it SageMaker, CodeWhisperer? What do you think's going to shine brightly within the AWS stack, if you will, or service list, that's going to be part of this? As you mentioned, CodeWhisperer and SageMaker, what else should people be looking at as they start tinkering and getting all these benefits, and scale up their ups? >> You know, if we were a startup, first, I would really work backwards from the customer problem I try to solve, and pick and choose, bar, I don't need to deal with the undifferentiated heavy lifting, so. And that's where the answer is going to change. If you look at it then, the answer is not going to be like a one size fits all, so you need a very strong, I mean, granted on the compute front, if you can actually completely accurate it, so unless, I will always recommend it, instead of running compute for running your ups, because it takes care of all the undifferentiated heavy lifting, but on the data, and that's where we provide a whole variety of databases, right from like relational data, or non-relational, or dynamo, and so forth. And of course, we also have a deep analytical stack, where data directly flows from our relational databases into data lakes and data virus. And you can get value along with partnership with various analytical providers. The area where I do think fundamentally things are changing on what people can do is like, with CodeWhisperer, I was literally trying to actually program a code on sending a message through Twilio, and I was going to pull up to read a documentation, and in my ID, I was actually saying like, let's try sending a message to Twilio, or let's actually update a Route 53 error code. All I had to do was type in just a comment, and it actually started generating the sub-routine. And it is going to be a huge time saver, if I were a developer. And the goal is for us not to actually do it just for AWS developers, and not to just generate the code, but make sure the code is actually highly secure and follows the best practices. So, it's not always about machine learning, it's augmenting with automated reasoning as well. And generative AI is going to be changing, and not just in how people write code, but also how it actually gets built and used as well. You'll see a lot more stuff coming on this front. >> Swami, thank you for your time. I know you're super busy. Thank you for sharing on the news and giving commentary. Again, I think this is a AWS moment and industry moment, heavy lifting, accelerated value, agility. AIOps is going to be probably redefined here. Thanks for sharing your commentary. And we'll see you next time, I'm looking forward to doing more follow up on this. It's going to be a big wave. Thanks. >> Okay. Thanks again, John, always a pleasure. >> Okay. This is SiliconANGLE's breaking news commentary. I'm John Furrier with SiliconANGLE News, as well as host of theCUBE. Swami, who's a leader in AWS, has been on theCUBE multiple times. We've been tracking the growth of how Amazon's journey has just been exploding past five years, in particular, past three. You heard the numbers, great performance, great reviews. This is a watershed moment, I think, for the industry, and it's going to be a lot of fun for the next 10 years. Thanks for watching. (bright music)

(light music) >> Hi everybody. Welcome to this Cube Conversation. I'm really pleased to announce a collaboration with Rob Strechay. He's a guest cube analyst, and we'll be working together to extract the signal from the noise. Rob is a long-time product pro, working at a number of firms including AWS, HP, HPE, NetApp, Snowplow. I did a stint as an analyst at Enterprise Strategy Group. Rob, good to see you. Thanks for coming into our Marlboro Studios. >> Well, thank you for having me. It's always great to be here. >> I'm really excited about working with you. We've known each other for a long time. You've been in the Cube a bunch. You know, you're in between gigs, and I think we can have a lot of fun together. Covering events, covering trends. So. let's get into it. What's happening out there? We're sort of exited the isolation economy. Things were booming. Now, everybody's tapping the brakes. From your standpoint, what are you seeing out there? >> Yeah. I'm seeing that people are really looking how to get more out of their data. How they're bringing things together, how they're looking at the costs of Cloud, and understanding how are they building out their SaaS applications. And understanding that when they go in and actually start to use Cloud, it's not only just using the base services anymore. They're looking at, how do I use these platforms as a service? Some are easier than others, and they're trying to understand, how do I get more value out of that relationship with the Cloud? They're also consolidating the number of Clouds that they have, I would say to try to better optimize their spend, and getting better pricing for that matter. >> Are you seeing people unhook Clouds, or just reduce maybe certain Cloud activities and going maybe instead of 60/40 going 90/10? >> Correct. It's more like the 90/10 type of rule where they're starting to say, Hey I'm not going to get rid of Azure or AWS or Google. I'm going to move a portion of this over that I was using on this one service. Maybe I got a great two-year contract to start with on this platform as a service or a database as a service. I'm going to unhook from that and maybe go with an independent. Maybe with something like a Snowflake or a Databricks on top of another Cloud, so that I can consolidate down. But it also gives them more flexibility as well. >> In our last breaking analysis, Rob, we identified six factors that were reducing Cloud consumption. There were factors and customer tactics. And I want to get your take on this. So, some of the factors really, you got fewer mortgage originations. FinTech, obviously big Cloud user. Crypto, not as much activity there. Lower ad spending means less Cloud. And then one of 'em, which you kind of disagreed with was less, less analytics, you know, fewer... Less frequency of calculations. I'll come back to that. But then optimizing compute using Graviton or AMD instances moving to cheaper storage tiers. That of course makes sense. And then optimize pricing plans. Maybe going from On Demand, you know, to, you know, instead of pay by the drink, buy in volume. Okay. So, first of all, do those make sense to you with the exception? We'll come back and talk about the analytics piece. Is that what you're seeing from customers? >> Yeah, I think so. I think that was pretty much dead on with what I'm seeing from customers and the ones that I go out and talk to. A lot of times they're trying to really monetize their, you know, understand how their business utilizes these Clouds. And, where their spend is going in those Clouds. Can they use, you know, lower tiers of storage? Do they really need the best processors? Do they need to be using Intel or can they get away with AMD or Graviton 2 or 3? Or do they need to move in? And, I think when you look at all of these Clouds, they always have pricing curves that are arcs from the newest to the oldest stuff. And you can play games with that. And understanding how you can actually lower your costs by looking at maybe some of the older generation. Maybe your application was written 10 years ago. You don't necessarily have to be on the best, newest processor for that application per se. >> So last, I want to come back to this whole analytics piece. Last June, I think it was June, Dev Ittycheria, who's the-- I call him Dev. Spelled Dev, pronounced Dave. (chuckles softly) Same pronunciation, different spelling. Dev Ittycheria, CEO of Mongo, on the earnings call. He was getting, you know, hit. Things were starting to get a little less visible in terms of, you know, the outlook. And people were pushing him like... Because you're in the Cloud, is it easier to dial down? And he said, because we're the document database, we support transaction applications. We're less discretionary than say, analytics. Well on the Snowflake earnings call, that same month or the month after, they were all over Slootman and Scarpelli. Oh, the Mongo CEO said that they're less discretionary than analytics. And Snowflake was an interesting comment. They basically said, look, we're the Cloud. You can dial it up, you can dial it down, but the area under the curve over a period of time is going to be the same, because they get their customers to commit. What do you say? You disagreed with the notion that people are running their calculations less frequently. Is that because they're trying to do a better job of targeting customers in near real time? What are you seeing out there? >> Yeah, I think they're moving away from using people and more expensive marketing. Or, they're trying to figure out what's my Google ad spend, what's my Meta ad spend? And what they're trying to do is optimize that spend. So, what is the return on advertising, or the ROAS as they would say. And what they're looking to do is understand, okay, I have to collect these analytics that better understand where are these people coming from? How do they get to my site, to my store, to my whatever? And when they're using it, how do they they better move through that? What you're also seeing is that analytics is not only just for kind of the retail or financial services or things like that, but then they're also, you know, using that to make offers in those categories. When you move back to more, you know, take other companies that are building products and SaaS delivered products. They may actually go and use this analytics for making the product better. And one of the big reasons for that is maybe they're dialing back how many product managers they have. And they're looking to be more data driven about how they actually go and build the product out or enhance the product. So maybe they're, you know, an online video service and they want to understand why people are either using or not using the whiteboard inside the product. And they're collecting a lot of that product analytics in a big way so that they can go through that. And they're doing it in a constant manner. This first party type tracking within applications is growing rapidly by customers. >> So, let's talk about who wins in that. So, obviously the Cloud guys, AWS, Google and Azure. I want to come back and unpack that a little bit. Databricks and Snowflake, we reported on our last breaking analysis, it kind of on a collision course. You know, a couple years ago we were thinking, okay, AWS, Snowflake and Databricks, like perfect sandwich. And then of course they started to become more competitive. My sense is they still, you know, compliment each other in the field, right? But, you know, publicly, they've got bigger aspirations, they get big TAMs that they're going after. But it's interesting, the data shows that-- So, Snowflake was off the charts in terms of spending momentum and our EPR surveys. Our partner down in New York, they kind of came into line. They're both growing in terms of market presence. Databricks couldn't get to IPO. So, we don't have as much, you know, visibility on their financials. You know, Snowflake obviously highly transparent cause they're a public company. And then you got AWS, Google and Azure. And it seems like AWS appears to be more partner friendly. Microsoft, you know, depends on what market you're in. And Google wants to sell BigQuery. >> Yeah. >> So, what are you seeing in the public Cloud from a data platform perspective? >> Yeah. I think that was pretty astute in what you were talking about there, because I think of the three, Google is definitely I think a little bit behind in how they go to market with their partners. Azure's done a fantastic job of partnering with these companies to understand and even though they may have Synapse as their go-to and where they want people to go to do AI and ML. What they're looking at is, Hey, we're going to also be friendly with Snowflake. We're also going to be friendly with a Databricks. And I think that, Amazon has always been there because that's where the market has been for these developers. So, many, like Databricks' and the Snowflake's have gone there first because, you know, Databricks' case, they built out on top of S3 first. And going and using somebody's object layer other than AWS, was not as simple as you would think it would be. Moving between those. >> So, one of the financial meetups I said meetup, but the... It was either the CEO or the CFO. It was either Slootman or Scarpelli talking at, I don't know, Merrill Lynch or one of the other financial conferences said, I think it was probably their Q3 call. Snowflake said 80% of our business goes through Amazon. And he said to this audience, the next day we got a call from Microsoft. Hey, we got to do more. And, we know just from reading the financial statements that Snowflake is getting concessions from Amazon, they're buying in volume, they're renegotiating their contracts. Amazon gets it. You know, lower the price, people buy more. Long term, we're all going to make more money. Microsoft obviously wants to get into that game with Snowflake. They understand the momentum. They said Google, not so much. And I've had customers tell me that they wanted to use Google's AI with Snowflake, but they can't, they got to go to to BigQuery. So, honestly, I haven't like vetted that so. But, I think it's true. But nonetheless, it seems like Google's a little less friendly with the data platform providers. What do you think? >> Yeah, I would say so. I think this is a place that Google looks and wants to own. Is that now, are they doing the right things long term? I mean again, you know, you look at Google Analytics being you know, basically outlawed in five countries in the EU because of GDPR concerns, and compliance and governance of data. And I think people are looking at Google and BigQuery in general and saying, is it the best place for me to go? Is it going to be in the right places where I need it? Still, it's still one of the largest used databases out there just because it underpins a number of the Google services. So you almost get, like you were saying, forced into BigQuery sometimes, if you want to use the tech on top. >> You do strategy. >> Yeah. >> Right? You do strategy, you do messaging. Is it the right call by Google? I mean, it's not a-- I criticize Google sometimes. But, I'm not sure it's the wrong call to say, Hey, this is our ace in the hole. >> Yeah. >> We got to get people into BigQuery. Cause, first of all, BigQuery is a solid product. I mean it's Cloud native and it's, you know, by all, it gets high marks. So, why give the competition an advantage? Let's try to force people essentially into what is we think a great product and it is a great product. The flip side of that is, they're giving up some potential partner TAM and not treating the ecosystem as well as one of their major competitors. What do you do if you're in that position? >> Yeah, I think that that's a fantastic question. And the question I pose back to the companies I've worked with and worked for is, are you really looking to have vendor lock-in as your key differentiator to your service? And I think when you start to look at these companies that are moving away from BigQuery, moving to even, Databricks on top of GCS in Google, they're looking to say, okay, I can go there if I have to evacuate from GCP and go to another Cloud, I can stay on Databricks as a platform, for instance. So I think it's, people are looking at what platform as a service, database as a service they go and use. Because from a strategic perspective, they don't want that vendor locking. >> That's where Supercloud becomes interesting, right? Because, if I can run on Snowflake or Databricks, you know, across Clouds. Even Oracle, you know, they're getting into business with Microsoft. Let's talk about some of the Cloud players. So, the big three have reported. >> Right. >> We saw AWSs Cloud growth decelerated down to 20%, which is I think the lowest growth rate since they started to disclose public numbers. And they said they exited, sorry, they said January they grew at 15%. >> Yeah. >> Year on year. Now, they had some pretty tough compares. But nonetheless, 15%, wow. Azure, kind of mid thirties, and then Google, we had kind of low thirties. But, well behind in terms of size. And Google's losing probably almost $3 billion annually. But, that's not necessarily a bad thing by advocating and investing. What's happening with the Cloud? Is AWS just running into the law, large numbers? Do you think we can actually see a re-acceleration like we have in the past with AWS Cloud? Azure, we predicted is going to be 75% of AWS IAS revenues. You know, we try to estimate IAS. >> Yeah. >> Even though they don't share that with us. That's a huge milestone. You'd think-- There's some people who have, I think, Bob Evans predicted a while ago that Microsoft would surpass AWS in terms of size. You know, what do you think? >> Yeah, I think that Azure's going to keep to-- Keep growing at a pretty good clip. I think that for Azure, they still have really great account control, even though people like to hate Microsoft. The Microsoft sellers that are out there making those companies successful day after day have really done a good job of being in those accounts and helping people. I was recently over in the UK. And the UK market between AWS and Azure is pretty amazing, how much Azure there is. And it's growing within Europe in general. In the states, it's, you know, I think it's growing well. I think it's still growing, probably not as fast as it is outside the U.S. But, you go down to someplace like Australia, it's also Azure. You hear about Azure all the time. >> Why? Is that just because of the Microsoft's software state? It's just so convenient. >> I think it has to do with, you know, and you can go with the reasoning they don't break out, you know, Office 365 and all of that out of their numbers is because they have-- They're in all of these accounts because the office suite is so pervasive in there. So, they always have reasons to go back in and, oh by the way, you're on these old SQL licenses. Let us move you up here and we'll be able to-- We'll support you on the old version, you know, with security and all of these things. And be able to move you forward. So, they have a lot of, I guess you could say, levers to stay in those accounts and be interesting. At least as part of the Cloud estate. I think Amazon, you know, is hitting, you know, the large number. Laws of large numbers. But I think that they're also going through, and I think this was seen in the layoffs that they were making, that they're looking to understand and have profitability in more of those services that they have. You know, over 350 odd services that they have. And you know, as somebody who went there and helped to start yet a new one, while I was there. And finally, it went to beta back in September, you start to look at the fact that, that number of services, people, their own sellers don't even know all of their services. It's impossible to comprehend and sell that many things. So, I think what they're going through is really looking to rationalize a lot of what they're doing from a services perspective going forward. They're looking to focus on more profitable services and bringing those in. Because right now it's built like a layer cake where you have, you know, S3 EBS and EC2 on the bottom of the layer cake. And then maybe you have, you're using IAM, the authorization and authentication in there and you have all these different services. And then they call it EMR on top. And so, EMR has to pay for that entire layer cake just to go and compete against somebody like Mongo or something like that. So, you start to unwind the costs of that. Whereas Azure, went and they build basically ground up services for the most part. And Google kind of falls somewhere in between in how they build their-- They're a sort of layer cake type effect, but not as many layers I guess you could say. >> I feel like, you know, Amazon's trying to be a platform for the ecosystem. Yes, they have their own products and they're going to sell. And that's going to drive their profitability cause they don't have to split the pie. But, they're taking a piece of-- They're spinning the meter, as Ziyas Caravalo likes to say on every time Snowflake or Databricks or Mongo or Atlas is, you know, running on their system. They take a piece of the action. Now, Microsoft does that as well. But, you look at Microsoft and security, head-to-head competitors, for example, with a CrowdStrike or an Okta in identity. Whereas, it seems like at least for now, AWS is a more friendly place for the ecosystem. At the same time, you do a lot of business in Microsoft. >> Yeah. And I think that a lot of companies have always feared that Amazon would just throw, you know, bodies at it. And I think that people have come to the realization that a two pizza team, as Amazon would call it, is eight people. I think that's, you know, two slices per person. I'm a little bit fat, so I don't know if that's enough. But, you start to look at it and go, okay, if they're going to start out with eight engineers, if I'm a startup and they're part of my ecosystem, do I really fear them or should I really embrace them and try to partner closer with them? And I think the smart people and the smart companies are partnering with them because they're realizing, Amazon, unless they can see it to, you know, a hundred million, $500 million market, they're not going to throw eight to 16 people at a problem. I think when, you know, you could say, you could look at the elastic with OpenSearch and what they did there. And the licensing terms and the battle they went through. But they knew that Elastic had a huge market. Also, you had a number of ecosystem companies building on top of now OpenSearch, that are now domain on top of Amazon as well. So, I think Amazon's being pretty strategic in how they're doing it. I think some of the-- It'll be interesting. I think this year is a payout year for the cuts that they're making to some of the services internally to kind of, you know, how do we take the fat off some of those services that-- You know, you look at Alexa. I don't know how much revenue Alexa really generates for them. But it's a means to an end for a number of different other services and partners. >> What do you make of this ChatGPT? I mean, Microsoft obviously is playing that card. You want to, you want ChatGPT in the Cloud, come to Azure. Seems like AWS has to respond. And we know Google is, you know, sharpening its knives to come up with its response. >> Yeah, I mean Google just went and talked about Bard for the first time this week and they're in private preview or I guess they call it beta, but. Right at the moment to select, select AI users, which I have no idea what that means. But that's a very interesting way that they're marketing it out there. But, I think that Amazon will have to respond. I think they'll be more measured than say, what Google's doing with Bard and just throwing it out there to, hey, we're going into beta now. I think they'll look at it and see where do we go and how do we actually integrate this in? Because they do have a lot of components of AI and ML underneath the hood that other services use. And I think that, you know, they've learned from that. And I think that they've already done a good job. Especially for media and entertainment when you start to look at some of the ways that they use it for helping do graphics and helping to do drones. I think part of their buy of iRobot was the fact that iRobot was a big user of RoboMaker, which is using different models to train those robots to go around objects and things like that, so. >> Quick touch on Kubernetes, the whole DevOps World we just covered. The Cloud Native Foundation Security, CNCF. The security conference up in Seattle last week. First time they spun that out kind of like reinforced, you know, AWS spins out, reinforced from reinvent. Amsterdam's coming up soon, the CubeCon. What should we expect? What's hot in Cubeland? >> Yeah, I think, you know, Kubes, you're going to be looking at how OpenShift keeps growing and I think to that respect you get to see the momentum with people like Red Hat. You see others coming up and realizing how OpenShift has gone to market as being, like you were saying, partnering with those Clouds and really making it simple. I think the simplicity and the manageability of Kubernetes is going to be at the forefront. I think a lot of the investment is still going into, how do I bring observability and DevOps and AIOps and MLOps all together. And I think that's going to be a big place where people are going to be looking to see what comes out of CubeCon in Amsterdam. I think it's that manageability ease of use. >> Well Rob, I look forward to working with you on behalf of the whole Cube team. We're going to do more of these and go out to some shows extract the signal from the noise. Really appreciate you coming into our studio. >> Well, thank you for having me on. Really appreciate it. >> You're really welcome. All right, keep it right there, or thanks for watching. This is Dave Vellante for the Cube. And we'll see you next time. (light music)

(upbeat music) (upbeat music) >> Hi everybody, welcome back to our coverage of the Cloud Native Security Con. I'm Dave Vellante, here in our Boston studio. We're connecting today with Palo Alto, with John Furrier and Lisa Martin. We're also live from the show floor in Seattle. But right now, I'm here with Andy Thurai who's from Constellation Research, friend of theCUBE, and we're going to discuss the intersection of AI and security, the potential of AI, the risks and the future. Andy, welcome, good to see you again. >> Good to be here again. >> Hey, so let's get into it, can you talk a little bit about, I know this is a passion of yours, the ethical considerations surrounding AI. I mean, it's front and center in the news, and you've got accountability, privacy, security, biases. Should we be worried about AI from a security perspective? >> Absolutely, man, you should be worried. See the problem is, people don't realize this, right? I mean, the ChatGPT being a new shiny object, it's all the craze that's about. But the problem is, most of the content that's produced either by ChatGPT or even by others, it's an access, no warranties, no accountability, no whatsoever. Particularly, if it is content, it's okay. But if it is something like a code that you use for example, one of their site projects that GitHub's co-pilot, which is actually, open AI + Microsoft + GitHub's combo, they allow you to produce code, AI writes code basically, right? But when you write code, problem with that is, it's not exactly stolen, but the models are created by using the GitHub code. Actually, they're getting sued for that, saying that, "You can't use our code". Actually there's a guy, Tim Davidson, I think he's named the professor, he actually demonstrated how AI produces exact copy of the code that he has written. So right now, it's a lot of security, accountability, privacy issues. Use it either to train or to learn. But in my view, it's not ready for enterprise grade yet. >> So, Brian Behlendorf today in his keynotes said he's really worried about ChatGPT being used to automate spearfishing. So I'm like, okay, so let's unpack that a little bit. Is the concern there that it just, the ChatGPT writes such compelling phishing content, it's going to increase the probability of somebody clicking on it, or are there other dimensions? >> It could, it's not necessarily just ChatGPT for that matter, right? AI can, actually, the hackers are using it to an extent already, can use to individualize content. For example, one of the things that you are able to easily identify when you're looking at the emails that are coming in, the phishing attack is, you look at some of the key elements in it, whether it's a human or even if it's an automated AI based system. They look at certain things and they say, "Okay, this is phishing". But if you were to read an email that looks exact copy of what I would've sent to you saying that, "Hey Dave, are you on for tomorrow? Or click on this link to do whatever. It could individualize the message. That's where the volume at scale to individual to masses, that can be done using AI, which is what scares me. >> Is there a flip side to AI? How is it being utilized to help cybersecurity? And maybe you could talk about some of the more successful examples of AI in security. Like, are there use cases or are there companies out there, Andy, that you find, I know you're close to a lot of firms that are leading in this area. You and I have talked about CrowdStrike, I know Palo Alto Network, so is there a positive side to this story? >> Yeah, I mean, absolutely right. Those are some of the good companies you mentioned, CrowdStrike, Palo Alto, Darktrace is another one that I closely follow, which is a good company as well, that they're using AI for security purposes. So, here's the thing, right, when people say, when they're using malware detection systems, most of the malware detection systems that are in today's security and malware systems, use some sort of a signature and pattern scanning in the malware. You know how many identified malwares are there today in the repository, in the library? More than a billion, a billion. So, if you are to check for every malware in your repository, that's not going to work. The pattern based recognition is not going to work. So, you got to figure out a different way of identification of pattern of usage, not just a signature in a malware, right? Or there are other areas you could use, things like the usage patterns. For example, if Andy is coming in to work at a certain time, you could combine a facial recognition saying, that should he be in here at that time, and should he be doing things, what he is supposed to be doing. There are a lot of things you could do using that, right? And the AIOps use cases, which is one of my favorite areas that I work, do a lot of work, right? That it has use cases for detecting things that are anomaly, that are not supposed to be done in a way that's supposed to be, reducing the noise so it can escalate only the things what you're supposed to. So, AIOps is a great use case to use in security areas which they're not using it to an extent yet. Incident management is another area. >> So, in your malware example, you're saying, okay, known malware, pretty much anybody can deal with that now. That's sort of yesterday's problem. >> The unknown is the problem. >> It's the unknown malware really trying to understand the patterns, and the patterns are going to change. It's not like you're saying a common signature 'cause they're going to use AI to change things up at scale. >> So, here's the problem, right? The malware writers are also using AI now, right? So, they're not going to write the old malware, send it to you. They are actually creating malware on the fly. It is possible entirely in today's world that they can create a malware, drop in your systems and it'll it look for the, let me get that name right. It's called, what are we using here? It's called the TTPs, Tactics, Techniques and procedures. It'll look for that to figure out, okay, am I doing the right pattern? And then malware can sense it saying that, okay, that's the one they're detecting. I'm going to change it on the fly. So, AI can code itself on the fly, rather malware can code itself on the fly, which is going to be hard to detect. >> Well, and when you talk about TTP, when you talk to folks like Kevin Mandia of Mandiant, recently purchased by Google or other of those, the ones that have the big observation space, they'll talk about the most malicious hacks that they see, involve lateral movement. So, that's obviously something that people are looking for, AI's looking for that. And of course, the hackers are going to try to mask that lateral movement, living off the land and other things. How do you see AI impacting the future of cyber? We talked about the risks and the good. One of the things that Brian Behlendorf also mentioned is that, he pointed out that in the early days of the internet, the protocols had an inherent element of trust involved. So, things like SMTP, they didn't have security built in. So, they built up a lot of technical debt. Do you see AI being able to help with that? What steps do you see being taken to ensure that AI based systems are secure? >> So, the major difference between the older systems and the newer systems is the older systems, sadly even today, a lot of them are rules-based. If it's a rules-based systems, you are dead in the water and not able, right? So, the AI-based systems can somewhat learn from the patterns as I was talking about, for example... >> When you say rules-based systems, you mean here's the policy, here's the rule, if it's not followed but then you're saying, AI will blow that away, >> AI will blow that away, you don't have to necessarily codify things saying that, okay, if this, then do this. You don't have to necessarily do that. AI can somewhat to an extent self-learn saying that, okay, if that doesn't happen, if this is not a pattern that I know which is supposed to happen, who should I escalate this to? Who does this system belong to? And the other thing, the AIOps use case we talked about, right, the anomalies. When an anomaly happens, then the system can closely look at, saying that, okay, this is not normal behavior or usage. Is that because system's being overused or is it because somebody's trying to access something, could look at the anomaly detection, anomaly prevention or even prediction to an extent. And that's where AI could be very useful. >> So, how about the developer angle? 'Cause CNCF, the event in Seattle is all around developers, how can AI be integrated? We did a lot of talk at the conference about shift-left, we talked about shift-left and protect right. Meaning, protect the run time. So, both are important, so what steps should be taken to ensure that the AI systems are being developed in a secure and ethically sound way? What's the role of developers in that regard? >> How long do you got? (Both laughing) I think it could go for base on that. So, here's the problem, right? Lot of these companies are trying to see, I mean, you might have seen that in the news that Buzzfeed is trying to hire all of the writers to create the thing that ChatGPT is creating, a lot of enterprises... >> How, they're going to fire their writers? >> Yeah, they replace the writers. >> It's like automated automated vehicles and automated Uber drivers. >> So, the problem is a lot of enterprises still haven't done that, at least the ones I'm speaking to, are thinking about saying, "Hey, you know what, can I replace my developers because they are so expensive? Can I replace them with AI generated code?" There are a few issues with that. One, AI generated code is based on some sort of a snippet of a code that has been already available. So, you get into copyright issues, that's issue number one, right? Issue number two, if AI creates code and if something were to go wrong, who's responsible for that? There's no accountability right now. Or you as a company that's creating a system that's responsible, or is it ChatGPT, Microsoft is responsible. >> Or is the developer? >> Or the developer. >> The individual developer might be. So, they're going to be cautious about that liability. >> Well, so one of the areas where I'm seeing a lot of enterprises using this is they are using it to teach developers to learn things. You know what, if you're to code, this is a good way to code. That area, it's okay because you are just teaching them. But if you are to put an actual production code, this is what I advise companies, look, if somebody's using even to create a code, whether with or without your permission, make sure that once the code is committed, you validate that the 100%, whether it's a code or a model, or even make sure that the data what you're feeding in it is completely out of bias or no bias, right? Because at the end of the day, it doesn't matter who, what, when did that, if you put out a service or a system out there, it is involving your company liability and system, and code in place. You're going to be screwed regardless of what, if something were to go wrong, you are the first person who's liable for it. >> Andy, when you think about the dangers of AI, and what keeps you up at night if you're a security professional AI and security professional. We talked about ChatGPT doing things, we don't even, the hackers are going to get creative. But what worries you the most when you think about this topic? >> A lot, a lot, right? Let's start off with an example, actually, I don't know if you had a chance to see that or not. The hackers used a bank of Hong Kong, used a defect mechanism to fool Bank of Hong Kong to transfer $35 million to a fake account, the money is gone, right? And the problem that is, what they did was, they interacted with a manager and they learned this executive who can control a big account and cloned his voice, and clone his patterns on how he calls and what he talks and the whole name he has, after learning that, they call the branch manager or bank manager and say, "Hey, you know what, hey, move this much money to whatever." So, that's one way of kind of phishing, kind of deep fake that can come. So, that's just one example. Imagine whether business is conducted by just using voice or phone calls itself. That's an area of concern if you were to do that. And imagine this became an uproar a few years back when deepfakes put out the video of Tom Cruise and others we talked about in the past, right? And Tom Cruise looked at the video, he said that he couldn't distinguish that he didn't do it. It is so close, that close, right? And they are doing things like they're using gems... >> Awesome Instagram account by the way, the guy's hilarious, right? >> So, they they're using a lot of this fake videos and fake stuff. As long as it's only for entertainment purposes, good. But imagine doing... >> That's right there but... >> But during the election season when people were to put out saying that, okay, this current president or ex-president, he said what? And the masses believe right now whatever they're seeing in TV, that's unfortunate thing. I mean, there's no fact checking involved, and you could change governments and elections using that, which is scary shit, right? >> When you think about 2016, that was when we really first saw, the weaponization of social, the heavy use of social and then 2020 was like, wow. >> To the next level. >> It was crazy. The polarization, 2024, would deepfakes... >> Could be the next level, yeah. >> I mean, it's just going to escalate. What about public policy? I want to pick your brain on this because I I've seen situations where the EU, for example, is going to restrict the ability to ship certain code if it's involved with critical infrastructure. So, let's say, example, you're running a nuclear facility and you've got the code that protects that facility, and it can be useful against some other malware that's outside of that country, but you're restricted from sending that for whatever reason, data sovereignty. Is public policy, is it aligned with the objectives in this new world? Or, I mean, normally they have to catch up. Is that going to be a problem in your view? >> It is because, when it comes to laws it's always miles behind when a new innovation happens. It's not just for AI, right? I mean, the same thing happened with IOT. Same thing happened with whatever else new emerging tech you have. The laws have to understand if there's an issue and they have to see a continued pattern of misuse of the technology, then they'll come up with that. Use in ways they are ahead of things. So, they put a lot of restrictions in place and about what AI can or cannot do, US is way behind on that, right? But California has done some things, for example, if you are talking to a chat bot, then you have to basically disclose that to the customer, saying that you're talking to a chat bot, not to a human. And that's just a very basic rule that they have in place. I mean, there are times that when a decision is made by the, problem is, AI is a black box now. The decision making is also a black box now, and we don't tell people. And the problem is if you tell people, you'll get sued immediately because every single time, we talked about that last time, there are cases involving AI making decisions, it gets thrown out the window all the time. If you can't substantiate that. So, the bottom line is that, yes, AI can assist and help you in making decisions but just use that as a assistant mechanism. A human has to be always in all the loop, right? >> Will AI help with, in your view, with supply chain, the software supply chain security or is it, it's always a balance, right? I mean, I feel like the attackers are more advanced in some ways, it's like they're on offense, let's say, right? So, when you're calling the plays, you know where you're going, the defense has to respond to it. So in that sense, the hackers have an advantage. So, what's the balance with software supply chain? Are the hackers have the advantage because they can use AI to accelerate their penetration of the software supply chain? Or will AI in your view be a good defensive mechanism? >> It could be but the problem is, the velocity and veracity of things can be done using AI, whether it's fishing, or malware, or other security and the vulnerability scanning the whole nine yards. It's scary because the hackers have a full advantage right now. And actually, I think ChatGPT recently put out two things. One is, it's able to direct the code if it is generated by ChatGPT. So basically, if you're trying to fake because a lot of schools were complaining about it, that's why they came up with the mechanism. So, if you're trying to create a fake, there's a mechanism for them to identify. But that's a step behind still, right? And the hackers are using things to their advantage. Actually ChatGPT made a rule, if you go there and read the terms and conditions, it's basically honor rule suggesting, you can't use this for certain purposes, to create a model where it creates a security threat, as that people are going to listen. So, if there's a way or mechanism to restrict hackers from using these technologies, that would be great. But I don't see that happening. So, know that these guys have an advantage, know that they're using AI, and you have to do things to be prepared. One thing I was mentioning about is, if somebody writes a code, if somebody commits a code right now, the problem is with the agile methodologies. If somebody writes a code, if they commit a code, you assume that's right and legit, you immediately push it out into production because need for speed is there, right? But if you continue to do that with the AI produced code, you're screwed. >> So, bottom line is, AI's going to speed us up in a security context or is it going to slow us down? >> Well, in the current version, the AI systems are flawed because even the ChatGPT, if you look at the the large language models, you look at the core piece of data that's available in the world as of today and then train them using that model, using the data, right? But people are forgetting that's based on today's data. The data changes on a second basis or on a minute basis. So, if I want to do something based on tomorrow or a day after, you have to retrain the models. So, the data already have a stale. So, that in itself is stale and the cost for retraining is going to be a problem too. So overall, AI is a good first step. Use that with a caution, is what I want to say. The system is flawed now, if you use it as is, you'll be screwed, it's dangerous. >> Andy, you got to go, thanks so much for coming in, appreciate it. >> Thanks for having me. >> You're very welcome, so we're going wall to wall with our coverage of the Cloud Native Security Con. I'm Dave Vellante in the Boston Studio, John Furrier, Lisa Martin and Palo Alto. We're going to be live on the show floor as well, bringing in keynote speakers and others on the ground. Keep it right there for more coverage on theCUBE. (upbeat music) (upbeat music) (upbeat music) (upbeat music)

(upbeat music) >> Hi everyone. Welcome to theCUBE special presentation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got two great guests, one for calling in from Germany, or videoing in from Germany, one from Maryland. We've got VMware and AWS. This is the customer successes with VMware Cloud on AWS Showcase: Accelerating Business Transformation. Here in the Showcase at Samir Kadoo, worldwide VMware strategic alliance solution architect leader with AWS. Samir, great to have you. And Daniel Rethmeier, principal architect global AWS synergy at VMware. Guys, you guys are working together, you're the key players in this relationship as it rolls out and continues to grow. So welcome to theCUBE. >> Thank you, greatly appreciate it. >> Great to have you guys both on. As you know, we've been covering this since 2016 when Pat Gelsinger, then CEO, and then then CEO AWS at Andy Jassy did this. It kind of got people by surprise, but it really kind of cleaned out the positioning in the enterprise for the success of VM workloads in the cloud. VMware's had great success with it since and you guys have the great partnerships. So this has been like a really strategic, successful partnership. Where are we right now? You know, years later, we got this whole inflection point coming, you're starting to see this idea of higher level services, more performance are coming in at the infrastructure side, more automation, more serverless, I mean and AI. I mean, it's just getting better and better every year in the cloud. Kind of a whole 'nother level. Where are we? Samir, let's start with you on the relationship. >> Yeah, totally. So I mean, there's several things to keep in mind, right? So in 2016, right, that's when the partnership between AWS and VMware was announced. And then less than a year later, that's when we officially launched VMware Cloud on AWS. Years later, we've been driving innovation, working with our customers, jointly engineering this between AWS and VMware. Day in, day out, as far as advancing VMware Cloud on AWS. You know, even if you look at the innovation that takes place with the solution, things have modernized, things have changed, there's been advancements. You know, whether it's security focus, whether it's platform focus, whether it's networking focus, there's been modifications along the way, even storage, right, more recently. One of the things to keep in mind is we're looking to deliver value to our customers together. These are our joint customers. So there's hundreds of VMware and AWS engineers working together on this solution. And then factor in even our sales teams, right? We have VMware and AWS sales teams interacting with each other on a constant daily basis. We're working together with our customers at the end of the day too. Then we're looking to even offer and develop jointly engineered solutions specific to VMware Cloud on AWS. And even with VMware to other platforms as well. Then the other thing comes down to is where we have dedicated teams around this at both AWS and VMware. So even from solutions architects, even to our sales specialists, even to our account teams, even to specific engineering teams within the organizations, they all come together to drive this innovation forward with VMware Cloud on AWS and the jointly engineered solution partnership as well. And then I think one of the key things to keep in mind comes down to we have nearly 600 channel partners that have achieved VMware Cloud on AWS service competency. So think about it from the standpoint, there's 300 certified or validated technology solutions, they're now available to our customers. So that's even innovation right off the top as well. >> Great stuff. Daniel, I want to get to you in a second upon this principal architect position you have. In your title, you're the global AWS synergy person. Synergy means bringing things together, making it work. Take us through the architecture, because we heard a lot of folks at VMware explore this year, formerly VMworld, talking about how the workloads on IT has been completely transforming into cloud and hybrid, right? This is where the action is. Where are you? Is your customers taking advantage of that new shift? You got AIOps, you got ITOps changing a lot, you got a lot more automation, edges right around the corner. This is like a complete transformation from where we were just five years ago. What's your thoughts on the relationship? >> So at first, I would like to emphasize that our collaboration is not just that we have dedicated teams to help our customers get the most and the best benefits out of VMware Cloud and AWS, we are also enabling us mutually. So AWS learns from us about the VMware technology, where VMware people learn about the AWS technology. We are also enabling our channel partners and we are working together on customer projects. So we have regular assembles globally and also virtually on Slack and the usual suspect tools working together and listening to customers. That's very important. Asking our customers where are their needs? And we are driving the solution into the direction that our customers get the best benefits out of VMware Cloud on AWS. And over the time, we really have involved the solution. As Samir mentioned, we just added additional storage solutions to VMware Cloud on AWS. We now have three different instance types that cover a broad range of workloads. So for example, we just edited the I4i host, which is ideally for workloads that require a lot of CPU power, such as, you mentioned it, AI workloads. >> Yeah, so I want to get us just specifically on the customer journey and their transformation, you know, we've been reporting on Silicon angle in theCUBE in the past couple weeks in a big way that the ops teams are now the new devs, right? I mean that sounds a little bit weird, but IT operations is now part of a lot more DataOps, security, writing code, composing. You know, with open source, a lot of great things are changing. Can you share specifically what customers are looking for when you say, as you guys come in and assess their needs, what are they doing, what are some of the things that they're doing with VMware on AWS specifically that's a little bit different? Can you share some of and highlights there? >> That's a great point, because originally, VMware and AWS came from very different directions when it comes to speaking people and customers. So for example, AWS, very developer focused, whereas VMware has a very great footprint in the ITOps area. And usually these are very different teams, groups, different cultures, but it's getting together. However, we always try to address the customer needs, right? There are customers that want to build up a new application from the scratch and build resiliency, availability, recoverability, scalability into the application. But there are still a lot of customers that say, "Well, we don't have all of the skills to redevelop everything to refactor an application to make it highly available. So we want to have all of that as a service. Recoverability as a service, scalability as a service. We want to have this from the infrastructure." That was one of the unique selling points for VMware on-premise and now we are bringing this into the cloud. >> Samir, talk about your perspective. I want to get your thoughts, and not to take a tangent, but we had covered the AWS re:MARS, actually it was Amazon re:MARS, machine learning automation, robotics and space was really kind of the confluence of industrial IoT, software, physical. And so when you look at like the IT operations piece becoming more software, you're seeing things about automation, but the skill gap is huge. So you're seeing low code, no code, automation, you know, "Hey Alexa, deploy a Kubernetes cluster." Yeah, I mean that's coming, right? So we're seeing this kind of operating automation meets higher level services, meets workloads. Can you unpack that and share your opinion on what you see there from an Amazon perspective and how it relates to this? >> Yeah. Yeah, totally, right? And you know, look at it from the point of view where we said this is a jointly engineered solution, but it's not migrating to one option or the other option, right? It's more or less together. So even with VMware Cloud on AWS, yes it is utilizing AWS infrastructure, but your environment is connected to that AWS VPC in your AWS account. So if you want to leverage any of the native AWS services, so any of the 200 plus AWS services, you have that option to do so. So that's going to give you that power to do certain things, such as, for example, like how you mentioned with IoT, even with utilizing Alexa, or if there's any other service that you want to utilize, that's the joining point between both of the offerings right off the top. Though with digital transformation, right, you have to think about where it's not just about the technology, right? There's also where you want to drive growth in the underlying technology even in your business. Leaders are looking to reinvent their business, they're looking to take different steps as far as pursuing a new strategy, maybe it's a process, maybe it's with the people, the culture, like how you said before, where people are coming in from a different background, right? They may not be used to the cloud, they may not be used to AWS services, but now you have that capability to mesh them together. >> Okay. >> Then also- >> Oh, go ahead, finish your thought. >> No, no, no, I was going to say what it also comes down to is you need to think about the operating model too, where it is a shift, right? Especially for that vStor admin that's used to their on-premises environment. Now with VMware Cloud on AWS, you have that ability to leverage a cloud, but the investment that you made and certain things as far as automation, even with monitoring, even with logging, you still have that methodology where you can utilize that in VMware Cloud on AWS too. >> Daniel, I want to get your thoughts on this because at Explore and after the event, as we prep for CubeCon and re:Invent coming up, the big AWS show, I had a couple conversations with a lot of the VMware customers and operators, and it's like hundreds of thousands of users and millions of people talking about and peaked on VMware, interested in VMware. The common thread was one person said, "I'm trying to figure out where I'm going to put my career in the next 10 to 15 years." And they've been very comfortable with VMware in the past, very loyal, and they're kind of talking about, I'm going to be the next cloud, but there's no like role yet. Architects, is it solution architect, SRE? So you're starting to see the psychology of the operators who now are going to try to make these career decisions. Like what am I going to work on? And then it's kind of fuzzy, but I want to get your thoughts, how would you talk to that persona about the future of VMware on, say, cloud for instance? What should they be thinking about? What's the opportunity? And what's going to happen? >> So digital transformation definitely is a huge change for many organizations and leaders are perfectly aware of what that means. And that also means to some extent, concerns with your existing employees. Concerns about do I have to relearn everything? Do I have to acquire new skills and trainings? Is everything worthless I learned over the last 15 years of my career? And the answer is to make digital transformation a success, we need not just to talk about technology, but also about process, people, and culture. And this is where VMware really can help because if you are applying VMware Cloud on AWS to your infrastructure, to your existing on-premise infrastructure, you do not need to change many things. You can use the same tools and skills, you can manage your virtual machines as you did in your on-premise environment, you can use the same managing and monitoring tools, if you have written, and many customers did this, if you have developed hundreds of scripts that automate tasks and if you know how to troubleshoot things, then you can use all of that in VMware Cloud on AWS. And that gives not just leaders, but also the architects at customers, the operators at customers, the confidence in such a complex project. >> The consistency, very key point, gives them the confidence to go. And then now that once they're confident, they can start committing themselves to new things. Samir, you're reacting to this because on your side, you've got higher level services, you've got more performance at the hardware level. I mean, a lot improvements. So, okay, nothing's changed, I can still run my job, now I got goodness on the other side. What's the upside? What's in it for the customer there? >> Yeah, so I think what it comes down to is they've already been so used to or entrenched with that VMware admin mentality, right? But now extending that to the cloud, that's where now you have that bridge between VMware Cloud on AWS to bridge that VMware knowledge with that AWS knowledge. So I will look at it from the point of view where now one has that capability and that ability to just learn about the cloud. But if they're comfortable with certain aspects, no one's saying you have to change anything. You can still leverage that, right? But now if you want to utilize any other AWS service in conjunction with that VM that resides maybe on-premises or even in VMware Cloud on AWS, you have that option to do so. So think about it where you have that ability to be someone who's curious and wants to learn. And then if you want to expand on the skills, you certainly have that capability to do so. >> Great stuff, I love that. Now that we're peeking behind the curtain here, I'd love to have you guys explain, 'cause people want to know what's goes on behind the scenes. How does innovation get happen? How does it happen with the relationships? Can you take us through a day in the life of kind of what goes on to make innovation happen with the joint partnership? Do you guys just have a Zoom meeting, do you guys fly out, you write code, go do you ship things? I mean, I'm making it up, but you get the idea. How does it work? What's going on behind the scenes? >> So we hope to get more frequently together in-person, but of course we had some difficulties over the last two to three years. So we are very used to Zoom conferences and Slack meetings. You always have to have the time difference in mind if you are working globally together. But what we try, for example, we have regular assembles now also in-person, geo-based, so for AMEA, for the Americas, for APJ. And we are bringing up interesting customer situations, architectural bits and pieces together. We are discussing it always to share and to contribute to our community. >> What's interesting, you know, as events are coming back, Samir, before you weigh in this, I'll comment as theCUBE's been going back out to events, we're hearing comments like, "What pandemic? We were more productive in the pandemic." I mean, developers know how to work remotely and they've been on all the tools there, but then they get in-person, they're happy to see people, but no one's really missed the beat. I mean, it seems to be very productive, you know, workflow, not a lot of disruption. More, if anything, productivity gains. >> Agreed, right? I think one of the key things to keep in mind is even if you look at AWS's, and even Amazon's leadership principles, right? Customer obsession, that's key. VMware is carrying that forward as well. Where we are working with our customers, like how Daniel said and meant earlier, right? We might have meetings at different time zones, maybe it's in-person, maybe it's virtual, but together we're working to listen to our customers. You know, we're taking and capturing that feedback to drive innovation in VMware Cloud on AWS as well. But one of the key things to keep in mind is yes, there has been the pandemic, we might have been disconnected to a certain extent, but together through technology, we've been able to still communicate, work with our customers, even with VMware in between, with AWS and whatnot, we had that flexibility to innovate and continue that innovation. So even if you look at it from the point of view, right? VMware Cloud on AWS Outposts, that was something that customers have been asking for. We've been able to leverage the feedback and then continue to drive innovation even around VMware Cloud on AWS Outposts. So even with the on-premises environment, if you're looking to handle maybe data sovereignty or compliance needs, maybe you have low latency requirements, that's where certain advancements come into play, right? So the key thing is always to maintain that communication track. >> In our last segment we did here on this Showcase, we listed the accomplishments and they were pretty significant. I mean geo, you got the global rollouts of the relationship. It's just really been interesting and people can reference that, we won't get into it here. But I will ask you guys to comment on, as you guys continue to evolve the relationship, what's in it for the customer? What can they expect next? Because again, I think right now, we're at an inflection point more than ever. What can people expect from the relationship and what's coming up with re:Invent? Can you share a little bit of kind of what's coming down the pike? >> So one of the most important things we have announced this year, and we will continue to evolve into that direction, is independent scale of storage. That absolutely was one of the most important items customer asked for over the last years. Whenever you are requiring additional storage to host your virtual machines, you usually in VMware Cloud on AWS, you have to add additional nodes. Now we have three different node types with different ratios of compute, storage, and memory. But if you only require additional storage, you always have to get also additional compute and memory and you have to pay for it. And now with two solutions which offer choice for the customers, like FS6 wanted a ONTAP and VMware Cloud Flex Storage, you now have two cost effective opportunities to add storage to your virtual machines. And that offers opportunities for other instance types maybe that don't have local storage. We are also very, very keen looking forward to announcements, exciting announcements, at the upcoming events. >> Samir, what's your reaction take on what's coming down on your side? >> Yeah, I think one of the key things to keep in mind is we're looking to help our customers be agile and even scaled with their needs, right? So with VMware Cloud on AWS, that's one of the key things that comes to mind, right? There are going to be announcements, innovations, and whatnot with upcoming events. But together, we're able to leverage that to advance VMware cloud on AWS. To Daniel's point, storage for example, even with host offerings. And then even with decoupling storage from compute and memory, right? Now you have the flexibility where you can do all of that. So to look at it from the standpoint where now with 21 regions where we have VMware Cloud on AWS available as well, where customers can utilize that as needed when needed, right? So it comes down to, you know, transformation will be there. Yes, there's going to be maybe where workloads have to be adapted where they're utilizing certain AWS services, but you have that flexibility and option to do so. And I think with the continuing events, that's going to give us the options to even advance our own services together. >> Well you guys are in the middle of it, you're in the trenches, you're making things happen, you've got a team of people working together. My final question is really more of a kind of a current situation, kind of future evolutionary thing that you haven't seen this before. I want to get both of your reaction to it. And we've been bringing this up in the open conversations on theCUBE is in the old days, let's go back this generation, you had ecosystems, you had VMware had an ecosystem, AWS had an ecosystem. You know, we have a product, you have a product, biz dev deals happen, people sign relationships, and they do business together and they sell each other's products or do some stuff. Now it's more about architecture, 'cause we're now in a distributed large scale environment where the role of ecosystems are intertwining and you guys are in the middle of two big ecosystems. You mentioned channel partners, you both have a lot of partners on both sides, they come together. So you have this now almost a three dimensional or multidimensional ecosystem interplay. What's your thoughts on this? Because it's about the architecture, integration is a value, not so much innovations only. You got to do innovation, but when you do innovation, you got to integrate it, you got to connect it. So how do you guys see this as an architectural thing, start to see more technical business deals? >> So we are removing dependencies from individual ecosystems and from individual vendors. So a customer no longer has to decide for one vendor and then it is a very expensive and high effort project to move away from that vendor, which ties customers even closer to specific vendors. We are removing these obstacles. So with VMware Cloud on AWS, moving to the cloud, firstly it's not a dead end. If you decide at one point in time because of latency requirements or maybe some compliance requirements, you need to move back into on-premise, you can do this. If you decide you want to stay with some of your services on-premise and just run a couple of dedicated services in the cloud, you can do this and you can man manage it through a single pane of glass. That's quite important. So cloud is no longer a dead end, it's no longer a binary decision, whether it's on-premise or the cloud, it is the cloud. And the second thing is you can choose the best of both worlds, right? If you are migrating virtual machines that have been running in your on-premise environment to VMware Cloud on AWS either way in a very, very fast cost effective and safe way, then you can enrich, later on enrich these virtual machines with services that are offered by AWS, more than 200 different services ranging from object-based storage, load balancing, and so on. So it's an endless, endless possibility. >> We call that super cloud in the way that we generically defining it where everyone's innovating, but yet there's some common services. But the differentiation comes from innovation where the lock in is the value, not some spec, right? Samir, this is kind of where cloud is right now. You guys are not commodity, amazon's completely differentiating, but there's some commodity things happen. You got storage, you got compute, but then you got now advances in all areas. But partners innovate with you on their terms. >> Absolutely. >> And everybody wins. >> Yeah, I 100% agree with you. I think one of the key things, you know, as Daniel mentioned before, is where it's a cross education where there might be someone who's more proficient on the cloud side with AWS, maybe more proficient with the VMware's technology. But then for partners, right? They bridge that gap as well where they come in and they might have a specific niche or expertise where their background, where they can help our customers go through that transformation. So then that comes down to, hey, maybe I don't know how to connect to the cloud, maybe I don't know what the networking constructs are, maybe I can leverage that partner. That's one aspect to go about it. Now maybe you migrated that workload to VMware Cloud on AWS. Maybe you want to leverage any of the native AWS services or even just off the top, 200 plus AWS services, right? But it comes down to that skillset, right? So again, solutions architecture at the back of the day, end of the day, what it comes down to is being able to utilize the best of both worlds. That's what we're giving our customers at the end of the day. >> I mean, I just think it's a refactoring and innovation opportunity at all levels. I think now more than ever, you can take advantage of each other's ecosystems and partners and technologies and change how things get done with keeping the consistency. I mean, Daniel, you nailed that, right? I mean you don't have to do anything. You still run it. Just spear the way you're working on it and now do new things. This is kind of a cultural shift. >> Yeah, absolutely. And if you look, not every customer, not every organization has the resources to refactor and re-platform everything. And we give them a very simple and easy way to move workloads to the cloud. Simply run them and at the same time, they can free up resources to develop new innovations and grow their business. >> Awesome. Samir, thank you for coming on. Daniel, thank you for coming to Germany. >> Thank you. Oktoberfest, I know it's evening over there, weekend's here. And thank you for spending the time. Samir, give you the final word. AWS re:Invent's coming up. We're preparing, we're going to have an exclusive with Adam, with Fryer, we'd do a curtain raise, and do a little preview. What's coming down on your side with the relationship and what can we expect to hear about what you got going on at re:Invent this year? The big show? >> Yeah, so I think Daniel hit upon some of the key points, but what I will say is we do have, for example, specific sessions, both that VMware's driving and then also that AWS is driving. We do have even where we have what are called chalk talks. So I would say, and then even with workshops, right? So even with the customers, the attendees who are there, whatnot, if they're looking to sit and listen to a session, yes that's there, but if they want to be hands-on, that is also there too. So personally for me as an IT background, been in sysadmin world and whatnot, being hands-on, that's one of the key things that I personally am looking forward. But I think that's one of the key ways just to learn and get familiar with the technology. >> Yeah, and re:Invent's an amazing show for the in-person. You guys nail it every year. We'll have three sets this year at theCUBE and it's becoming popular. We have more and more content. You guys got live streams going on, a lot of content, a lot of media. So thanks for sharing that. Samir, Daniel, thank you for coming on on this part of the Showcase episode of really the customer successes with VMware Cloud on AWS, really accelerating business transformation with AWS and VMware. I'm John Furrier with theCUBE, thanks for watching. (upbeat music)

>>Hey everyone. Welcome back to Chicago. Lisa Martin here with John Furrier. We're live with the Cube at Ansible Fest 2022. This is not only Ansible's 10th anniversary, John Wood. It's the first in-person event in three years. About 14 to 1500 people here talking about the evolution of automation, really the democratization opportunities. Ansible >>Is money, and this segment's gonna be great. Cub alumni are back, and we're gonna get an industry perspective on the automation journey. So it should be great. >>It will be great. We've got two alumni back for the price of wine. Scott Canine joins us, Director of Worldwide Automation at Kendra. A Nelson Shoe is back as well. Product marketing director at Red Hat. Guys, great to have you back on the, on the live cube. >>Oh, thank you for having us. And, and you know, it's really great to be back here live and in person and, and, you know, get a chance to see you guys again. >>Well, and also you get, you get such a sense of the actual Ansible community here. Yeah. And, and only a fraction of them that are here, but people are ready to be back. They're ready to collaborate in person. And I always can imagine the amount of innovation that happens at these events, just like off the show floor, people bumping into each other and go, Hey, I had this idea. What do you think, Scott? It's been just about a, a year since Kenel was formed. Talk to us about the last close to a year and what that's been like. Especially as the world has been so, chops >>The world been Yeah, exactly. Topsy turvy. People getting back to working in person and, and everything else. But, you know, you know, throw on that what we've done in the last year, taking Kendra, you know, outside of being a part of ibm Right. In our own company at this point, you know, and you know, you hear a lot of our executives and a lot of our people when we talk about it, like, Oh yeah, it's, you know, it's a $19 billion startup. We got freedom of action. We can do all these different things. But, you know, one of the ways I look at it is we are a $19 billion startup, which means we've got a lot of companies out there that are trusting us to, no matter what change we're doing, continue to deliver their operations, do it flawlessly, do it in a way so they can continue to, to service their clients effectively and, and don't break 'em. And, and so that to me, you know, the way we do that and the way I focusing on that is automation Ansible, obviously corridor strategy, getting there. >>Yeah. And I'd like to get your thoughts too, because we seeing a trend, we've been reporting on this with the cloud growth and the scale of cloud and distributed computing going cloud native, the automation is the front and piece center of all conversations. Automate this, make developers go faster. And with the pandemic, we're coming out of that pandemic. You post pandemic with large scale automation, system architecture, a lot more like architectural conversations and customers leaning on new things. Yeah. What are you seeing in this automation framework that you guys are talking about? What's been the hot playbook or recipe or, or architecture to, you know, play on words there, but I mean, this is kind of the, the key focus. >>Yeah. I mean, if you, one of the things that I com customer comp talks, I've been pulled into a lot recently, have all been around thinking about security, right? A lot in terms of security and compli, I think, I mean, think about the world environment as a whole, right here, everything that's been going on. So, so people are, are conscious of how much energy that's being used in their data centers, right? And people are conscious of how secure they are, right? Are they, you know, the, their end customers are trusting them with data information about them, right? And, and they're trusting us to make sure that those systems are secure to make sure that, you know, all that is taken care of in the right way. And so, you know that what's hot security and compliance, right? What can we do in the energy space, right? Can we do things to, to help clients understand better their energy consumption as, as, you know, especially as we get now in Europe to the winter months, can we do things there that'll help them also be better in that space, Right? Reduce their >>Costs and a lot more cloud rails obviously right there. You got closer and you got now Ansible, they're kind of there to help the customers put it together at scale. This has been the big conversation last year, remember was automate, automate, automate, right? This year it's automation everywhere, in every piece of the, the landscape edge. It's been big discussion tomorrow here about event driven stuff. This is kind of a change of focus and scope. Can you like, share your thoughts on how you see how big this is in terms of the, the, the customer journey >>In terms, I'm sorry, in terms of, >>In terms of their architecture, how they're rolling out automation, >>What's their Yeah, yeah. So, so in terms of their rolling out arch, arch in terms of them consuming architecture, right? And the architecture or consuming automation. Yeah. And rolling out the architecture for how they do that. You know, again, it, to me it's, it's a lot of, it's been focused around how do we do this in the most secure manner possible? How do we deliver the service to them and the most secure managers possible? How do they understand that it, that they can trust the automation and it's doing the right things on their environments, right? So it's not, you know, we're not pushing out or, or you know, it's not making bad policies >>And they're leaning on you guys. >>It's, it's not being putting malware out there, right? At the same time we're doing different things. And so they really rely on, on our customers, rely on us to really help them with that journey. >>I think a, a big part of that with Kendra as such a great partner and so many customers trusting them, is the fact that they really understand that enterprise. And so as, as Scott talks about the security aspect, we're not just talking to the IT operations people, right? We're talking across the enterprise, the security, the infrastructure, and the automation around that. So when we talk about hybrid cloud, we talk about network and security edge is a natural conversation to that, cuz absolutely at the edge network and security automation is critical. Otherwise, how are you gonna manage just the size of your edge as it grows? >>Yeah. And, and we've been, and that's another area that we've been having a a lot more conversations with clients on, is how do you do automation for IOT and edge based devices, right? We, you know, traditionally data center cloud, right? Kind of the core pieces of where we've been focusing on, but I, you know, recently I've been seeing a lot more opportunities and a lot more companies coming forward saying, you know, help us with the network space, help us with the iot space. We really wanna start getting to that level of automation and that part of our environments. And what >>Are some of the key barriers that customers are coming to you with saying, help us overcome these so that they can, you're smiling so that they can, can obviously attract and retain the right talent and also be able to determine what processes to automate to extract the most value and the most ROI for the organization. >>Yeah. And, and, and you know, that's, that's an interesting, the ROI conversation's always an interesting one, right? Because when you start having that with customers, some of the first things they think about, or the first, the natural place people go is, >>Oh, >>Labor takeout. I can do this with less people. Right? But that's not the end all be all of automation. In fact, you know, my personal view is that's, you know, maybe the, the the bottom 30%, right? That's kind of, then you have to think about the value you get above and beyond that standard operations, standardized processes, right? How are you gonna able to do those faster? How's that enabling your business, right? What's all the risks that's now been taken out by having these changes codified, right? By having them done in a manner that is repeatable, scalable, and, and, and really gets them to the point of, you know, what their business needs from an operational standpoint and >>Extracting that value. Nelson, talk about the automation journey from your perspective, How have you seen that evolve from your lens, especially over the last couple of years? >>It's a great question. You know, it's interesting because obviously all of our customers are at different stages of their automation journey. We have someone that just beginning looking at automation, they've been doing old scripts, if you will, the past. And then we have more that are embracing it, right? As a culture. So we have customers that are building cultures of automation, right? They have standups, they have automation guilds. It's, it's kind of a little bit of a, of a click. It's kind of, you know, building up steam in that momentum. And then we have, you know, the clients that Kindra works with, right? And they're very much focused on automation because they understand that they have a lack of resources, they don't have the expertise, they don't have the time to be able to deliver all this. Yeah. And that's really, Kendra really comes into effect to really help those customers accelerate their automation. Yeah. Right. And to that point, you know, we're doing a lot of innovation work with Kendra and we lean on them heavily because, you know, they're willing to make that commitment as a partner both on the, the, the day to day work that we do together as well as Ford looking at different architectures. >>Yeah. And, and the community aspect from our side internally has been tremendous in terms of us being able to expand what we'll be doing with automation and, and what a's been able to do with that community to get there. Right? Yeah. So to last month we did about 33 million day one, day two operations through automation, right? So that's what we've done. If you look at it, you know, if I break it down, it's really 80% of that standard global process stuff that we bring to the table. 20% of that is what our, our account teams are bringing specifically to their clients based on their needs and what they need to get done. Right. You know, one of my favorite examples of of, of this, right? We have a automation example out there for a, a client we've got in Japan, right? They tie, you know, they're, they're obviously concerned, you know, security a everything else that we've been talking about. >>They're also concerned about resiliency, right? In the face of natural disasters. Yeah. So they took our automation, they said, Okay, we're gonna tie your platform to seismic data that's coming through, and we understand what seismic data's happening. Okay, it's hitting a certain event. Let's automatically start kicking off resiliency operations so we can be prepared and thus keeps serving our clients when that's happening. Right? And that's not something like when you talk about a global team coming in and, and saying, we're gonna do all this. It's that community aspect, getting, getting the account focus, getting to that level, right? That's really brings value to clients. And that's one of the use cases, you know, and aaps enabled us to do with the a the community approach. We've got >>Now talk about this partnership. I think earlier when we were talking to Stephanie and Tom, the bottoms up Ansible community with top down kind of business objectives kind of come into play. You guys have a partnership where it's, there's some game changing things happening because Ansible's growing, continuing to have that scope grow from a skill set standpoint, expand the horizons, doing more automation at scale, and then you got business objectives where people wanna move faster in their, in their digital transformation. So to me, it's interesting that this part kind of hits both. >>It does really hit both. I mean, you know, the community cloud that Kendra has is so critical, right? Because they build that c i CF architecture internally, but they follow that community mantra, if you will. And community is so important to us, right? And that's really where we find innovation. So together with what we were call discussing about validated content earlier today becomes critical to build that content to really help people get started, Right? Validated content, content they can depend on and deliver, right? So that becomes critical on the other side, as you mentioned, is the reality of how do we get this done? Yeah. Right? How do we mature, how do we accelerate? And without the ability to drive those solutions to them to fix, if you are the problems that the line of business has. Well, if you don't answer those questions with the innovation, with the community, and then with the ap, it's, it, it does, it's gotta all come >>Together as, I mean, that community framework is interesting. I think we hear a lot in the cube, you know, Hey, let's do this. Sounds good. Who's gonna do it? Someone who's the operator. So there's a little skills gap going on. It's also a transformation in the roles of the operators in particular, and the dev, So the DevOps equation's completely going to the next level, right? And this is where people wanna move faster. So you're seeing a lot more managed services, a lot more Yes. Services that's, I won't say so much top down, but more like, let's do it and here's a play to get it done, right? Then backfill on the hiring, whether it's taking on a little bit of technical debt or going a little faster to get the proof points, >>Right? And I think one of the critical aspects is, you know, Ansible has it certified collections, right? And oftentimes we, we don't, I don't, I meet with customers two, three times a week, right? There's not a single one that doesn't emphasize the importance of partners and the importance of certified collections, Right? And kindra is included in that, right? Because they bring a lot of those certified collections. Use them, leverage them, it's helps customers get a jumpstarter, right? It's a few, it's their easy button, right? But they only get that and they value that because of the support that's there. >>Yeah. Right? They get the with >>The cert. Yeah. I was gonna say, just adding on the certified collections, right? We, so, you know, it was, it was great to see the hub come out with those capabilities because, you know, as we've gone through the last 12 months and, and change, one of the things that we focused more in on is network devices, network support, right? And, and so, you know, some of the certified collections out there for Cisco for F five, right? Some of those things we've been able to take back in and now build on top of with the expertise that we, we have in that space as well. And then use that as a starting point to more value for our clients. >>How is Kentrell working together with, with Red Hat and with Ansible to help organizations like you mentioned Nelson, they're on the journey varies considerably. Some are well on their way, others aren't. But for those to really start developing an automation, first culture, we talked a lot about cultural ship, we talked about it this morning. You can feel the power of that community and driving it, but how do you guys work together to help companies and any industry kind of really start understanding what an automation first culture is and then building it internally and getting some grounds? Well, >>Well, it's interesting, right? One of the, one of the things that really is we found really helpful is assessments, right? So you have silos and pockets of automation, and that's that challenge, right? So to be able to bring that, if you are automation community within an enterprise together, we often go out and we'll do an assessment, right? An automation assessment to really understand holistically how the enterprise could leverage automation not just in the pockets, but to bring it together. And when they bring that automation together, they can share, playbooks can share their experiences, right? And with Kindra and the multiple and the practices they have, right? They really bring that home from an industry perspective. They also bring that home, if you will, from a technology perspective. And they bring that together. So, you know, Kindra in that respect is the glue for our customer success. >>What's news? What's the next big thing that you guys see? Because if this continues down the road, this path, people are gonna get, the winds gonna get the successes. The new beachhead, if you will, is established. You got the edge around the corner. What's next for you guys in the partnership? How do you see it developing? >>No, we're looking at >>No, it's all good. So really, you know, I, I mentioned it earlier and, and the jour the automation journey paralleled by innovation, right? Customers today are automating, they're doing a great job. There's multiple tools out there. We understand we're not gonna be the only tool in the shed, but Ansible can come in and integrate that entire environment. And in a hybrid cloud environment, you want that there, right? I think what next is obviously the hybrid cloud is critical. The edge is critical, right? And I think that, you know, the needs and the requirements that Kindra hears that we have is kind of that future. And, you know, we, we often, often in, in Red Hat, we talk about a north star, right? And when I work with partners, ikin, do we talk about the North Star, where we want to get to? And that is the acceleration of automation. And I think both by the practical aspect of working with our customers and the innovation as partners, as business partners, technology partners will help accelerate >>That. Yeah. Scott, your perspective to bridge to the future is obviously hybrid and edge, how you bringing your customers along? >>Yes. So, so we see, you know, when we talk about my, when I talk about my automation strategy, our automated strategy, right? It's about being automated, orchestrated and intelligent, right? Kind of those, those three layers of the stack. We've been building out a lot of work, what we call our integrated AIOps layer for actionable insights, right? We've got a, you know, a goal to integrate that and, and we have integrated into our automation service for how we're delivering the whole package to our clients so they can better see opportunities for automation. What's the best way to go about it? You know, what are the, what are some of the, the issues they have, vulnerabilities they have in their environment and really bringing it to them in, in a real holistic manner. In fact, we internally, we call it our F five steering wheel, right? Based on the, the race thing, right? >>Because you think about the, the racing cars, f fives know they're right there, right? They got everything they need in front of 'em. Yeah. So our goal is been to, to include that into our automation view and service and build that out, right? So that's one way we're doing it. The additional way is, is through some announcements you probably heard, hopefully heard the last couple weeks through something called Kendra Bridge, right? Kendra Bridge is more the digitization of, of the way we deliver services for our clients to make it easier for them to consume and, and to, to make the barrier to entry for things like getting automation, getting it more in their environment, right? Lower as much as possible, right? So really integrated AIOps kind bridge. Those are really the two ways we see it as, as going forward. >>It's interesting, you know, we live through a lot of these different inflection points in the industry. Every time there's a big inflection point, there's more complexity that needs to be tamed, you know? And so you got innovation. If you got innovation coming and you got the clients wanna simplify and tame the complexity, this is a big part of what you guys do. >>Absolutely. Yeah. I mean, how do we, you know, most, when the clients come to us, right? Like I said, one, it's about trust. They trust us to do it because we can make it easy for them to not have to worry about that, right? Yeah. They don't have to worry about what it takes to secure the environment, manage it, run it, design it, build it for the, the cloud. We give 'em the ability, we give them the ability to focus on their core business while we do the stuff that's important to them, which >>Is absolutely critical that you, you can't emphasize trust in this relationship enough. I wish we had more time, guys, you're gonna have to come back. I think that's basically what this is boil down to. But thanks so much guys for talking with John and me about how Kendra and and Ansible are working together, really enabling your customers to, to unlock the value of automation across their organization and really make some big business changes. We appreciate your insights and your time. Fantastic. Thank you. Happy to do it and happy to do it any time. All right. Our pleasure. Thank you so much for our guests and John Furrier. I'm Lisa Martin. You're watching The Cube Live from Chicago. This is day one of our coverage of Ansible Fest 22. Don't go anywhere. Our next guest joins us in just a minute.