(energetic, playful techno music) [Voiceover] - Live from Las Vegas, it's the CUBE covering AWS re:Invent 2018. Brought to you by Amazon web Services, Intel, and their ecosystem partners. >> We are live here at the Sands Expo, one of eight venues that are actually here for AWS re:Invent this week as we continue our three day coverage here with you Tuesday, Wednesday, and Thursday as well, live from Las Vegas here on the CUBE. Along with Justin Warren, I'm John Walls, and we're now joined by Phil Tee, who is the CEO and co-founder of Moodsoft. Phil, good to see you this afternoon. >> Great to be with you, thank you for inviting me along. >> You bet, nice to have you. In fact, I'm going to be the only guy without a charming accent on this set as a matter of fact. A UK and an Aussie here. Your world's data, right? And it kind of reminds me of the old movie Jaws when Robert says we're going to need a bigger boat, right? AI ops, is that your bigger boat? Is that how you're handling this world of data? >> I think that's exactly spot on and one of the things we observe at Moodsoft with our customers is just this crazy complexity that they have to deal with. I mean, we cover everything from large financials, telephone companies, e-commerce businesses, and the drive to adopt agile and cloud and software defined in the the enterprise has driven complexity to the point where the poor old human brain is just out of luck, right? The unaided "I'll figure it out by myself" approach, dead in the water, and you've got to use this artificial intelligence approach precisely as your "bigger boat" to go catch that shark. >> Right. So tell us, there's a lot of hype around AI, and machine learning, and all of these different buzzwords are getting thrown around. Dial us in a little bit. Explain what you mean by AI in the context you're talking about things with Moodsoft. >> So, I think that's very perceptive. There's a tweet going around at the moment which describes the difference between machine learning and AI as, if it's written in Python it's probably machine learning, if it's on a Powerpoint deck it's probably AI. Which is kind of funny, but to the point. And there is a ton of hype going around artificial intelligence. There are some purists who claim there is no such thing as AI and everything we talk about today is really machine learning or data driven algorithms. And there's some truth in that. Really, what we mean by AI is the full panoply of both feature detection, you know, looking for patterns that are not obvious to the human eye all the way through to deep learning, neural nets, convolutional neural nets, where you are training a system to recognize features of the data as representative of something underlying that you're hunting for. So in the case of AI ops it's looking for the cause of, or looking for the presence of, a potential service-impacting outage in the data that we monitor, in the events. But one thing it's not going to do is, it's not going to unplug itself from the internet and come and kill you anytime soon. It's really quite benign and very useful to our customers with what they deal with. >> So, to that point, because you have so much data, and it seems like, I hate to say, most of it isn't needed or most of it isn't of value, but a lot of it isn't, if not most. How do you then decern, how do you assess value and assign value to what really is important and then, put it to use today, when you're getting so much more information than you were even a year ago? >> So just to put a little bit of context on the amount of data, way back before the cloud and virtualization, a typical enterprise, a high event rate would have been 100, 200, events a second. Nowadays, in an average customer of ours, you add a zero or two to that rate, maybe even three. And it's one of the reasons why the legacy systems really struggle with that data. So, job one is, if you accept, and I certainly do, that most of that data is junk. Most of it is inconsequential. You've got to have an algorithmic way of getting rid of that. You know, the old-fashioned way was creating lists of "ignore it because it's a certain severity", "ignore it because it comes from this list of hosts", you know, the whole listing approach. What we do is we use information science. So we can measure the semantic content, and the informational content, of an event to work out whether it's telling us something of import. And we use that technique with great effectiveness to eliminate as much as ninety, ninety-five, percent of the inbound data as effectively affecting nothing. So that narrows the data lake, you feel, down to a point where we can process it in real time through much more computer-intensive AI algorithms to kind of get that high-quality indication of an instance or a potential instance. >> A lot of machine learning and AI is based on learning from history, so, "we've seen all this stuff before and we know what that means", or, even encouraging the machines to go and look at the historical data and then pull out the details as you said. Even things that a human might miss, you'll look at that data and then learn new things. How does that work when we're doing all of this innovation? When there's all of this change and novelty coming in, how does the AI system cope with that kind of environment? >> So, you have to have a dual approach. I mean, I guess everyone's familiar with Mikolas Nassim Taleb's book, The Black Swans. He was trying to explain why it is that you can get a bunch of Nobel Prize winners in a room to design a hedge fund and it can still go bankrupt in the blink of and eye, like the long-term capital management. And the truth of the matter is, yes, an awful lot of the techniques that are supervised and based upon a training set are vulnerable to the "unknown unknowns" to misquote Donald Rumsfeld, and that's why we use a combination of unsupervised feature detection and supervised learning. The unsupervised feature detection just knows something as an unusual, highly correlated, pattern or feature in the data and needs no prior understanding of what's going on. Now, interestingly, there are some hybrid techniques now. You may have heard of something called transfer learning, which is the idea that you partially train a neural net on some kind of standard corpus. It'd be like the stuff that you already know and adapting that sort of partially trained net to something that is literally very, very, very adapted to the system that it's monitoring, it does that very quickly rather than having to wait for a certain critical amount of data before the net is converged. And so those sort of techniques, which we also experiment with at Moodsoft, I think are going to be interesting directions for us in the future with our platform, but there's maybe a hundred PhDs a week given out in AI and machine learning these days. It's definitely getting a lot of focus and there's a ton of innovation that's coming down the line. One thing that we're particularly committed to is shortening the distance between when something's invented and when we can get it into our customer's hands. >> There's usually quite a lag, historically, it's about ten years before someone discovers something and then it actually makes it into the business world so if we could shorten that cycle that would be quite useful. >> We know an academic called professor Maggie Bowden who's just getting ready to retire and she was one of the original authors of the neural net papers in the 1960's, so that kind of gives you an idea of the lag, it could be many, many, decades and it's a shame because the truth of the matter is the pressure on all the people coming to a show like this that want to benefit from the public cloud, new ways of thinking about the application development toolchain, they don't have time to wait around for that innovation to come to them. We've got to drive it a lot faster and, certainly, we view that as one of our missions at Moodsoft, as being passionately involved and sort of shortening that gap between innovation and a production implementation as something really cool. >> So what have you seen at the show so far that you think you want to take to your customers and say, "oh, actually, this is happening and you need to get on to this now"? >> One thing I've observed here is, I guess if we would've been here two years ago, nobody was talking about AI ops. I mean essentially the entirety of how people looked at the cloud was "same old stuff, just lives somewhere different, we can use all of the old techniques". You walk around here, there's a bunch of startups, more established companies, recognizing that a new approach is necessary and my sense of it is that this market which, I mean, let's be honest, we were pretty lonely in it two or three years ago, is starting to feel like it's a little more populated and that's goodness, we're very happy about that, so that is definitely a take away. You know, to go to customers and say "this is no longer bleeding edge, it's simply leading edge". >> Not just a gap in the market, there is actually a market in that gap. >> You're the bigger boat. >> Well, we hope so. >> Phil, thanks for being with us. We appreciate your time here on the CUBE and once again, have a great show and we do thank you for your time, sir. >> Thank you very much indeed. Great talking to you both. >> Phil Tee from Moodsoft joining us here on the CUBE. We're at AWS re:Invent and we're at the Sands, and we're in Las Vegas. (energetic, playful techno music)

>> [Announcer] Live from Boston Massachusetts. This is theCUBE. Covering Sparks Summit East 2017. Brought to you by Databricks. Now here are your hosts, Dave Vellante and George Gilbert. >> [Dave] Welcome back to Boston everybody, this is Spark Summit East #SparkSummit And this is theCUBE. Ion Stoica is here. He's Executive Chairman of Databricks and Professor of Computer Science at UCal Berkeley. The smarts is rubbing off on me. I always feel smart when I co-host with George. And now having you on is just a pleasure, so thanks very much for taking the time. >> [Ion] Thank you for having me. >> So loved the talk this morning, we learned about RISELabs, we're going to talk about that. Which is the son of AMP. You may be the father of those two, so. Again welcome. Give us the update, great keynote this morning. How's the vibe, how are you feeling? >> [Ion] I think it's great, you know, thank you and thank everyone for attending the summit. It's a lot of energy, a lot of interesting discussions, and a lot of ideas around. So I'm very happy about how things are going. >> [Dave] So let's start with RISELabs. Maybe take us back, to those who don't understand, so the birth of AMP and what you were trying to achieve there and what's next. >> Yeah, so the AMP was a six-year Project at Berkeley, and it involved around eight faculties and over the duration of the lab around 60 students and postdocs, And the mission of the AMPLab was to make sense of big data. AMPLab started in 2009, at the end of 2009, and the premise is that in order to make sense of this big data, we need a holistic approach, which involves algorithms, in particular machine-learning algorithms, machines, means systems, large-scale systems, and people, crowd sourcing. And more precisely the goal was to build a stack, a data analytic stack for interactive analytics, to be used across industry and academia. And, of course, being at Berkeley, it has to be open source. (laugh) So that's basically what was AMPLab and it was a birthplace for Apache Spark that's why you are all here today. And a few other open-source systems like Mesos, Apache Mesos, and Alluxio which was previously called Tachyon. And so AMPLab ended in December last year and in January, this January, we started a new lab which is called RISE. RISE stands for Real-time Intelligent Secure Execution. And the premise of the new lab is that actually the real value in the data is the decision you can make on the data. And you can see this more and more at almost every organization. They want to use their data to make some decision to improve their business processes, applications, services, or come up with new applications and services. But then if you think about that, what does it mean that the emphasis is on the decision? Then it means that you want the decision to be fast, because fast decisions are better than slower decisions. You want decisions to be on fresh data, on live data, because decisions on the data I have right now are original but those are decisions on the data from yesterday, or last week. And then you also want to make targeted, personalized decisions. Because the decisions on personal information are better than aggregate information. So that's the fundamental premise. So therefore you want to be on platforms, tools and algorithms to enable intelligent real-time decisions on live data with strong security. And the security is a big emphasis of the lab because it means to provide privacy, confidentiality and integrity, and as you hear about data breaches or things like that every day. So for an organization, it is extremely important to provide privacy and confidentiality to their users and it's not only because the users want that, but it also indirectly can help them to improve their service. Because if I guarantee your data is confidential with me, you are probably much more willing to share some of your data with me. And if you share some of the data with me, I can build and provide better services. So that's basically in a nutshell what the lab is and what the focus is. >> [Dave] Okay, so you said three things: fast, live and targeted. So fast means you can affect the outcome. >> Yes. Live data means it's better quality. And then targeted means it's relevant. >> Yes. >> Okay, and then my question on security, I felt like when cloud and Big Data came to fore, security became a do-over. (laughter) Is that a fair assessment? Are you doing it over? >> [George] Or as Bill Clinton would call it, a Mulligan. >> Yeah, if you get a Mulligan on security. >> I think security is, it's always a difficult topic because it means so many things for so many people. >> Hmm-mmm. >> So there are instances and actually cloud is quite secure. It's actually cloud can be more secure than some on-prem deployments. In fact, if you hear about these data leaks or security breaches, you don't hear them happening in the cloud. And there is some reason for that, right? It is because they have trained people, you know, they are paranoid about this, they do a specification maybe much more often and things like that. But still, you know, the state of security is not that great. Right? For instance, if I compromise your operating system, whether it's in cloud or in not in the cloud, I can't do anything. Right? Or your VM, right? On all this cloud you run on a VM. And now you are going to allow on some containers. Right? So it's a lot of attacks, or there are attacks, sophisticated attacks, which means your data is encrypted, but if I can look at the access patterns, how much data you transferred, or how much data you access from memory, then I can infer something about what you are doing about your queries, right? If it's more data, maybe it's a query on New York. If it's less data it's probably maybe something smaller, like maybe something at Berkeley. So you can infer from multiple queries just looking at the access. So it's a difficult problem. But fortunately again, there are some new technologies which are developed and some new algorithms which gives us some hope. One of the most interesting technologies which is happening today is hardware enclaves. So with hardware enclaves you can execute the code within this enclave which is hardware protected. And even if your operating system or VM is compromised, you cannot access your code which runs into this enclave. And Intel has Intell SGX and we are working and collaborating with them actively. ARM has TrustZone and AMB also announced they are going to have a similar technology in their chips. So that's kind of a very interesting and very promising development. I think the other aspect, it's a focus of the lab, is that even if you have the enclaves, it doesn't automatically solve the problem. Because the code itself has a vulnerability. Yes, I can run the code in hardware enclave, but the code can send out >> Right. >> data outside. >> Right, the enclave is a more granular perimeter. Right? >> Yeah. So yeah, so you are looking and the security expert is in your lab looking at this, maybe how to split the application so you run only a small part in the enclave, which is a critical part, and you can make sure that also the code is secure, and the rest of the code you run outside. But the rest of the code, it's only going to work on data which is encrypted. Right? So there is a lot of interesting research but that's good. >> And does Blockchain fit in there as well? >> Yeah, I think Blockchain it's a very interesting technology. And again it's real-time and the area is also very interesting directions. >> Yeah, right. >> Absolutely. >> So you guys, I want George, you've shared with me sort of what you were calling a new workload. So you had batch and you have interactive and now you've got continuous- >> Continuous, yes. >> And I know that's a topic that you want to discuss and I'd love to hear more about that. But George, tee it up. >> Well, okay. So we were talking earlier and the objective of RISE is fast and continuous-type decisions. And this is different from the traditional, you either do it batch or you do it interactive. So maybe tell us about some applications where that is one workload among the other traditional workloads. And then let's unpack that a little more. >> Yeah, so I'll give you a few applications. So it's more than continuously interacting with the environment continuously, but you also learn continuously. I'll give you some examples. So for instance in one example, think about you want to detect a network security attack, and respond and diagnose and defend in the real time. So what this means is that you need to continuously get logs from the network and from the more endpoints you can get the better. Right? Because more data will help you to detect things faster. But then you need to detect the new pattern and you need to learn the new patterns. Because new security attacks, which are the ones that are effective, are slightly different from the past one because you hope that you already have the defense in place for the past ones. So now you are going to learn that and then you are going to react. You may push patches in real time. You may push filters, installing new filters to firewalls. So that's kind of one application that's going in real time. Another application can be about self driving. Now self driving has made tremendous strides. And a lot of algorithms you know, very smart algorithms now they are implemented on the cars. Right? All the system is on the cars. But imagine now that you want to continuously get the information from this car, aggregate and learn and then send back the information you learned to the cars. Like for instance if it's an accident or a roadblock an object which is dropped on the highway, so you can learn from the other cars what they've done in that situation. It may mean in some cases the driver took an evasive action, right? Maybe you can monitor also the cars which are not self-driving, but driven by the humans. And then you learn that in real time and then the other cars which follow through the same, confronted with the same situation, they now know what to do. Right? So this is again, I want to emphasize this. Not only continuous sensing environment, and making the decisions, but a very important components about learning. >> Let me take you back to the security example as I sort of process the auto one. >> Yeah, yeah. >> So in the security example, it doesn't sound like, I mean if you have a vast network, you know, end points, software, infrastructure, you're not going to have one God model looking out at everything. >> Yes. >> So I assume that means there are models distributed everywhere and they don't know what a new, necessarily but an entirely new attack pattern looks like. So in other words, for that isolated model, it doesn't know what it doesn't know. I don't know if that's what Rumsfeld called it. >> Yes (laughs). >> How does it know what to pass back for retraining? >> Yes. Yes. Yes. So there are many aspects and there are many things you can look at. And it's again, it's a research problem, so I cannot give you the solution now, I can hypothesize and I give you some examples. But for instance, you can look about, and you correlate by observing the affect. Some of the affects of the attack are visible. In some cases, denial of service attack. That's pretty clear. Even the And so forth, they maybe cause computers to crash, right? So once you see some of this kind of anomaly, right, anomalies on the end devices, end host and things like that. Maybe reported by humans, right? Then you can try to correlate with what kind of traffic you've got. Right? And from there, from that correlation, probably you can, and hopefully, you can develop some models to identify what kind of traffic. Where it comes from. What is the content, and so forth, which causes behavior, anomalous behavior. >> And where is that correlation happening? >> I think it will happen everywhere, right? Because- >> At the edge and at the center. >> Absolutely. >> And then I assume that it sounds like the models both at the edge and at the center are ensemble models. >> Yes. >> Because you're tracking different behavior. >> Yes. You are going to track different behavior and you are going to, I think that's a good hypothesis. And then you are going to assemble them, assemble to come up with the best decision. >> Okay, so now let's wind forward to the car example. >> Yeah. >> So it sound like there's a mesh network, at least, Peter Levine's sort of talk was there's near-local compute resources and you can use bitcoin to pay for it or Blockchain or however it works. But that sort of topology, we haven't really encountered before in computing, have we? And how imminent is that sort of ... >> I think that some of the stuff you can do today in the cloud. I think if you're on super-low latency probably you need to have more computation towards the edges, but if I'm thinking that I want kind of reactions on tens, hundreds of milliseconds, in theory you can do it today with the cloud infrastructure we have. And if you think about in many cases, if you can't do it within a few hundredths of milliseconds, it's still super useful. Right? To avoid this object which has dropped on the highway. You know, if I have a few hundred milliseconds, many cars will effectively avoid that having that information. >> Let's have that conversation about the edge a little further. The one we were having off camera. So there's a debate in our community about how much data will stay at the edge, how much will go into the cloud, David Flores said 90% of it will stay at the edge. Your comment was, it depends on the value. What do you mean by that? >> I think that that depends who am I and how I perceive the value of the data. And, you know, what can be the value of the data? This is what I was saying. I think that value of the data is fundamentally what kind of decisions, what kind of actions it will enable me to take. Right? So here I'm not just talking about you know, credit card information or things like that, even exactly there is an action somebody's going to take on that. So if I do believe that the data can provide me with ability to take better actions or make better decisions I think that I want to keep it. And it's not, because why I want to keep it, because also it's not only the decision it enables me now, but everyone is going to continuously improve their algorithms. Develop new algorithms. And when you do that, how do you test them? You test on the old data. Right? So I think that for all these reasons, a lot of data, valuable data in this sense, is going to go to the cloud. Now, is there a lot of data that should remain on the edges? And I think that's fair. But it's, again, if a cloud provider, or someone who provides a service in the cloud, believes that the data is valuable. I do believe that eventually it is going to get to the cloud. >> So if it's valuable, it will be persisted and will eventually get to the cloud? And we talked about latency, but latency, the example of evasive action. You can't send the back to the cloud and make the decision, you have to make it real time. But eventually that data, if it's important, will go back to the cloud. The other question of all this data that we are now processing on a continuous basis, how much actually will get persisted, most of it, much of it probably does not get persisted. Right? Is that a fair assumption? >> Yeah, I think so. And probably all the data is not equal. All right? It's like you want to maybe, even if you take a continuous video, all right? On the cars, they continuously have videos from multiple cameras and radar and lidar, all of this stuff. This continuous. And if you think about this one, I would assume that you don't want to send all the data to the cloud. But the data around the interesting events, you may want to do, right? So before and after the car has a near-accident, or took an evasive action, or the human had to intervene. So in all these cases, probably I want to send the data to the cloud. But for the most cases, probably not. >> That's good. We have to leave it there, but I'll give you the last word on things that are exciting you, things you're working on, interesting projects. >> Yeah, so I think this is what really excites me is about how we are going to have this continuous application, you are going to continuously interact with the environment. You are going to continuously learn and improve. And here there are many challenges. And I just want to say a few more there, and which we haven't discussed. One, in general it's about explainability. Right? If these systems augment the human decision process, if these systems are going to make decisions which impact you as a human, you want to know why. Right? Like I gave this example, assuming you have machine-learning algorithms, you're making a diagnosis on your MRI, or x-ray. You want to know why. What is in this x-ray causes that decision? If you go to the doctor, they are going to point and show you. Okay, this is why you have this condition. So I think this is very important. Because as a human you want to understand. And you want to understand not only why the decision happens, but you want also to understand what you have to do, you want to understand what you need to do to do better in the future, right? Like if your mortgage application is turned down, I want to know why is that? Because next time when I apply to the mortgage, I want to have a higher chance to get it through. So I think that's a very important aspect. And the last thing I will say is that this is super important and information is about having algorithms which can say I don't know. Right? It's like, okay I never have seen this situation in the past. So I don't know what to do. This is much better than giving you just the wrong decision. Right? >> Right, or a low probability that you don't know what to do with. (laughs) >> Yeah. >> Excellent. Ion, thanks again for coming in theCUBE. It was really a pleasure having you. >> Thanks for having me. >> You're welcome. All right, keep it right there everybody. George and I will be back to do our wrap right after this short break. This is theCUBE. We're live from Spark Summit East. Right back. (techno music)

live from las vegas it's the cube covering knowledge 60 brought to you by service now here your host dave vellante and Jeff Frick we're back Jeff Frick and I are pleased to be wrapping up day one for us for the cube at knowledge 16s a plastic piece no service house big events been a long day okay farriers texted me from SA and looks like they had a good event down there as well but but we're here at knowledge 16 great day financial analyst meeting yesterday set up the cube had a great kick off today at the the keynotes with Frank's luqman and and company laying out their vision she said robert gates on as a rock star right i saw him at the cio event so service now has a separate cio event within the event and they bring in a lot of speakers and they share you know it's behind closed doors CIOs talking to other CIOs pretty impressive was great walking over with him ten minutes he came on now remember he replaced rumsfeld all right george w bush brought him in asking him to replace rumsfeld it was like it would be like Belichick replacing Parcells right Rumsfeld effusive outgoing controversial hey and then and then and then of course belcheck you know very straight narrow and and that's kind of way Gates is right i mean he was very measured and in yet opinionated met serving eight presidents all of all of which had great sense of humor except to he said right jimmy carter and and richard nixon yeah dark days then take take what you will from that he's head so pretty interesting but so what's your take on day one at knowledge you know kind of following up on some of the stuff that dr. gates talked about it the themes are actually really simple you know and he listed the traits of leadership you know these are not things that you never heard before carrying it with the trust humor and I think the themes here at as service now are very similar Dave and that it's it's about work it's not about records it's you know for time and time again about it's about effective response not necessarily you know building the biggest mode in the security in the security aspect and you know it's the action platformer we get work done so it just seems like this kind of methodical just boom boom boom stick another knitting moving down the road moving down the field as we like to say and continuing just to execute and as they see everything as a service that now that opens up this huge opportunity to go well beyond itsm which is you know consistent with the vision and I don't keep talking about that 2013 interview with rebels our first meeting with him you know to execute on that vision of a platform and now going into shared services which we've heard a lot about you know a little bit into HR a little bit into legal and continuing to move down that path where you know this seems like a good opportunity for a head but they're just executing just keep executing well and I Tom now is the big opportunity facing them and I think it's going to provide a Mick shift to to a new set of products for service now IT operations management they've made some acquisitions they are a service management is now it's got its tentacles everywhere and I mean essentially helping orchestrate chef and puppet if you want they could do the orchestration for you so cloud management is a new area for these guys than this whole notion of inter clouding and managing multiple disparate clouds is something that service now can help attack I mean it's pick a problem that involves a service workflow and service now is going to knock it down how many things in business involve a service workflow it's like everything everything we do everything we touch has a service workflow aspect to it so every project every new initiative every acquisition it's just you know the market opportunities enormous and what service now has done a really good job of doing is taking this little notion of a like the Big Bang IT Service Management he'll help desk changed man and problem management change management etc and exploded that in all different directions into new vectors you mentioned a little bit in hrs I think it's increasingly getting traction in HR legal logistics you're now seeing service now lay out a vision of touching and helping to essentially orchestrate request service requests around the ERP systems around the CRM systems which are systems of record and relatively rigid systems of record right and service now can help orchestrate all the activities around that it's an enormous opportunity so the TAM I pegged the tam in 2014 I wrote an article that John furrier II published on Forbes I pegged the tam at 30 billion at that time and remember when I went through the analysis David floor you help me at ease you know it just feels like it could even be higher and I remember discussing that with David said yeah but 30 billion so huge already and they get this tiny little company and you're on thin ice we better be conservative here and now it's up to 60 billion i think the 60 billion is is understated Jeff well Darryl from from H&R Block in Canada you know they do this annual thing I left I called it a merger acquisition at a divestiture to build the infrastructure to execute the annual tax process for Canada 84,000 tasks everything from painting the building to signage to computers to paper to hiring people firing people i mean how does a lot of different tasks that they now manage with service now I thought that was pretty a fascinating story you were not when we had Lawrence on from from from ey not understand young anymore ey and talked about now they can provide a level of detail in the IT FM the financial management is like what's the cost of an application that no one ever knew before because they never added in the data center cost you know this is just software and maintenance and now people can start making interesting informed decisions about end-of-life enough which has come up in a number of our conversation so that people are turning off other applications and and service now is taking that workload the other thing I wanted to talk about we talked about this at the open but when you and I walked the floor at 22 the ServiceNow 2013 it was struck us that one of the challenges they had is to evolve this ecosystem and in that but by the way they they still have that challenge but they've done a really good job and you've seen one of the things we said is where the real big guys KPMG was here but you know the the Accenture of the world the youngs at the time now they are going all-in so accenture acquires cloud sherpas CSC acquires fruition so those guys like to focus on big opportunities so the only area now the other thing we talked about when we were at the Aria was the down market opportunity you know we said boy wouldn't it be nice if they had a solution for small companies take a put in a page out of the the Salesforce playbook and they've announced offerings there you're not hearing anything about them you know because and I think the reason is at least in part there's so much opportunity in the global 2000 they're really laser focused on that piece we got to do some more digging and find out what's going on there I know initially there was some concerns about sort of the the growth path and but we haven't heard a peep unless I missed it about the down market product the entry-level product guys the guys like us right you know he'd use it I don't know if I have 84,000 tasks to put the cube production together but i could not the few that i was not to have an automated in this system absolutely yeah so and then the other thing Dave which which you know we ettore on talking about the design and and the the watch and the fact that he sits in a room he had a surf shop in the Maldives before he came to work for service now for a couple years and he sits with Fred and so again just this unique culture of having kind of the mad scientist you know elder coder with the the fellow surf shop design guy and to come together and to try things and to come up with the watch and told the story the watch and I had to build credibility over years to try new things to get to the point where you could say hey let's let's talk about the what let's do a watch and is a form factor of the wash and what are the types of notifications and work behavior that we can better represent represent in this form factor and I think it's just you just cannot underestimate the strength of having you know a driven visionary leader that pulls people to him and inspires people which he so clearly does well and he's young at heart I mean a sec i would say i think he was coding in the keynotes today i got we gotta ask him but he comes on you know but they you know you look at this company and there's some folks at this company that been around for a while you know it's not a bunch of kids you know co diem there are right but a lot of the senior leadership team and the technical team the development team have been around the block right this is not their first rodeo and yet they're able to focus on simplicity you know Fred used to talk about the Amazon experience lat you know last year I think it was the uber experience I think I know we're gonna see some more stuff on on Wednesday though the watch still as we scratching my head a little bit but look low when did the Apple watch come out right i mean window if you look at apple's kind of the people at stamp you know this is now kind of a valid new technical assed year right austrian they're already kind of thinking of new ways to use this fourth basket right well so one of the guests said today you know things change so quickly now you know we it's true we used to go to these conferences and you'd be talking about the same cloud narrative two years straight hey right now it's like every six months it's something new every three months it's something new you know whether it's you know the way i OT just exploded on the scene you know hadoop which was so hot now the dupes like passe you know everybody's talking about you know spark and you know other new real-time methods and streaming and and it's just amazing to see the pace of innovation and so servers now seems to be a company that can keep up with that the other thing is i'd look at my notes on is back to your comment about the system integrators you know we had center and see you see both talking about them getting out of the plumbing business and really moving more of their efforts with their clients to the high-value stuff and you think wow that's kind of counterproductive they've made a lot of money on I'm doing heavy lifting infrastructure implementations and integration and all that big nasty stuff even they see the writing on the wall it's better to get behind this transformation the cult of the rotation to the new and to build their practice around helping their customers execute in a cloud enable the world versus necessarily continuing to stitch together infrastructure well I mean I think that's it's important I mean the hallmark of a great company is one that can can navigate through transitions we we've covered EMC for years we've seen their their Executive Joe Tucci talk about the waves I I always believed in the DMC strategy for example was was the right one but it could not navigate those waves all right it's been a lot of great companies the digital is the primes the way thanks you know and so we'll see if well I mean guys like the service companies tend to be able to make those transitions all right because they they do you know eat from the trough so to speak right right hey they wait until there's a lot of food and then they go in and and pig out and I do a really good job of it and they're doing it now so that tells you there's food so that's a huge sign a confirmation about this ecosystem so all right anyway a big another big day tomorrow start off with the keynotes at eight a.m. pacific time and and then we start up i think at nine thirty again right correct we start at nine thirty and again we've got a great selection of service now executives of course but more importantly what we look forward to really is the customers and and again as we've said a number of times one of the reasons why this is one of our favorite shows is because we get to talk to practitioners we get to talk to people that are executing that are in the trenches that are transforming their own companies in this competitive world and they happen to be using service now as part of that strategy and there's a lot of them here so we will be extracting the signal from the noise as we do with the cube thanks for watching everybody this is a wrap day one we're here at servicenow knowledge 2016 at the mandalay bay we'll see you tomorrow service management