(bright upbeat music) >> Hello everyone and welcome back to Las Vegas, Nevada. We're live from the show floor here at AWS re:Invent on theCUBE. My name is Savannah Peterson joined by my VIP co-host John Furrier. John, what's your hot take? >> We get wall-to-wall coverage day three of theCUBE (laughing loudly) shows popping, another day tomorrow. >> How many interviews have we done so far? >> I think we're over a hundred I think, (laughing loudly) we might be pushing a hundred. >> We've had a really fantastic line up of guests on theCUBE so far. We are in the meat of the sandwich right now. We've got a full line up of programming all day long and tomorrow. We are lucky to be joined by two fantastic gentlemen on our next segment. Brad, who's a familiar face. We just got to see you in that last one. Thank you for being here, you still doing good? >> Still good. >> Okay, great, glad nothing's changed in the last 14 minutes. >> 'no, we're good. >> Would've been tragic. And welcome, Mark, the CEO of Cloud Saver. Mark, how you doing this morning? >> I'm doing great, thanks so much. >> Savannah: How's the show going for ya'? >> It's going amazing. The turnout's just fantastic. It's record turnouts here. It's been lots of activity, it's great to be part of. >> So I suspect most people know about AMD, but Mark, I'm going to let you give us just a little intro to Cloud Saver so the audience is prepped... >> 'yeah, absolutely. So at Cloud Saver we help companies manage their Cloud spin. And the way that we do it is a little bit unique. Most people try and solve Cloud cost management just through a software only solution but we have a different perspective. There's so many complexities and nuances to managing your Cloud spin, that we don't think that software's enough. So our solution is a full managed service so we can plan our own proprietary technology with a full service delivery team, so that we come in and provide project management, Cloud engineering, FinOps analysts, and we come in and basically do all the cost authorization for the company. And so it's been a fantastic solution for us and something that's really resonated well within our customer base. >> I love your slogan. "Clean up the Cloud with the Cloud Saver Tag Manager'. >> Mark: That's right. >> So yesterday in the Keynote, Adams Lesky said, "Hey if you want to tighten your belt, come to the Cloud." So, big focus right now on right sizing. >> That's right. >> I won't say repatriation 'cause that's not kind of of happening, but like people are looking at it like they're not going to, it's not the glory days where you leave all your lights on in your house and you go to bed, you don't worry about the electricity bill. Now people are like, "Okay, what am I doing? Why am I doing it?" A lot more policy, a lot more focus. What are you guys seeing as the low hanging fruit, best practices, the use cases that people are implementing right now? >> Yeah, if you think about where things are at now from a Cloud cost management perspective, there's a lot of frustration in the marketplace because everybody sees their cost continually going up. And what typically happens is they'll say, okay we need to figure out what's going on with this cost and figure out where we can make some changes. And so they go out and get a cost visibility tool and then they're a little bit disappointed because all that visibility tool is completely dependent upon properly tagging your resources. So what a lot of people don't understand is that a lot of their pain that they're experiencing, the root cause is actually they've got a data problem which is why we built a entire solution to help companies clean up their Cloud, clean up their tags. It really is a foundational piece to help them understand how to manage their costs. >> I just.. >> Data is back in the data problem again >> Shocking, right? Not a theme we've heard on the show. Not a theme we've heard on the show at all. I mean, I think with tags it matters more than people realize and it can get very messy very quick. I know that this partnership is relatively new, six months, you told us before this show. Brad what does this partnership mean for AMD customers? >> Yeah, it's critical, they have a fantastic approach to this kind of a full service approach to cost optimization, compete optimization. AMD we're very, extremely focused on providing most cost efficient, most performance, and most energy efficient products on the market. And as Adam talked about, come to the Cloud to tighten your belt. I'll follow up. When you come to the Cloud, your choice matters, right? Your choice matters on what you use and what the downstream impact and cost is. And it also matters in sustainability and other other factors with our products. >> You know, yesterday Zeyess Karvellos one of our analysts on theCUBE, he used his own independent shop. We were talking about this focus and he actually made a comment I want to get your both reaction to, he said "Spend more in the Cloud, save more." Meaning there are ways to spend more on the Cloud and save more at the same time. >> Right. >> It's not just cut and eliminate, it's right side. I don't know what the right word is. Can you guys.. >> No, I think what you're saying is, is that there are areas where you need to spend more so you can be more efficient and get value that way, but there's also plenty of areas where you're spending money unnecessarily. Either you have resources that nobody's using. Let's find those and pull them to the front and center and turn them off, right? Or if you've over provisioned certain areas let's pull those back. So I think having the right balance of where you spend your money to get the value makes total sense. >> John: Yeah >> I like that holistic approach too. I like that you're not just looking at one thing. I mean, people, you're kind of, I'm thinking of you as like the McKinsey or like the dream team that just comes in tidies everything up. Makes sure that people are being, getting that total cost optimization. It's exciting. So who, I imagine, I mean obviously the entire organization benefits, but who benefits most? What types of roles? Who's using you? >> Right, so, Cloud cost management really benefits the entire organization, especially when times get tougher and everybody's looking to tighten their belt with cost. You know.. >> Wait every time when you say that, I'm like conscious, (laughing loudly) of my abdomen. we're in Vegas, there's great food, (laughing loudly) and we got, (laughing loudly) thanks a lot Adam, thanks a lot. (all laughing loudly). >> No, but it really does benefit everybody across the organization and it also helps people to keep cost management kind of front and center, right? No company allows people to have a complete blank check to go out there for infrastructure and as a way to make sure you've got proper checks and balances in place so that you're responsibly managing your IT organization. >> Yeah, and going back to the spend comment, spend more, you know, to save money. You know, look, we're going to be facing a very difficult situation in 23. I think there's going to be a lot of headwinds for a lot of companies. And the way to look at this is it's if you can provide yourself additional operating capital to work, there's other aspects to working with the business. Time to market, right? You're talking about addressing your top line. There's other ways to use applications and the services from AWS to help enable your business to grow even faster in '23 right? So '23 is a time to build, not necessarily a time to hang back and hope everything turns out okay. >> Yeah we can't go over it, (chuckles) We can't go under it, we got to go through it... >> Got to make it work >> Got to make our way through it. I think it's, yeah, it's so important. So as the partnership grows, what's next for you two companies? Brad will go to you first. >> Yeah sure you know, we're very excited to partner with Cloud Saver. It's fantastic company, have great team. And for us it's AMB is entering into the partnership space of this now. So now we've got a great position with AWS. We love their products, and now we're going to try to enable as many partners as we can in some specific areas. And for us cost optimization is priority number one. So you'll see a lot of programs that come out in '23 around this area. We're going to dedicate a lot of sales resources to help as many enterprise customers as we can, working with our close partners like Cloud Saver. >> Next ecosystem developing for you guys. >> Absolutely, absolutely, and you know AMD's they're still fairly new in the Cloud space, right? And this is a journey that takes a long time, and this is the next leg in our growth in the environment. >> Well, certainly the trend is more horsepower, more under the hood, more capabilities, customized >> Oh that's coming. >> Workloads. You're starting to see the specialized instances, you can see what's happening and soon it's going to be like a, it's own like computer in the Cloud >> Right. >> More horsepower. >> You think about this, I mean more than 400 instance types, more than 400 types of services out there in that range. And you think about all the potential interactions and applications. It's incredibly complex, right? >> Yeah that decision matrix just went like this in my brain when you said that. That is wild. And everyone wants to do more, faster, easier but also with the comfort of that cost savings, in terms of your customers priorities, I mean, you're talking to a lot of different people across a lot of different industries both of you are, I'm sure is cost optimization the number one priority as we're going into 2023? >> Yeah. Matter of fact, I have a chance to obviously speak with AWS leadership on a regular basis. Every single, they keep telling me for the past two months, every single CEO they're speaking to right now, it's the very first things out of the mouth. It's top of mind for every major corporation right now. And I think the message is also the same. It's like, great, let's help you do that but at the same time, is it not a bad time to re:Invest with some of those additional savings, right? And I think that's where the value of else comes into play. >> Yeah, and I think what you guys are demonstrating to also is another tell sign of this what I call NextGen Cloud evolution, which is as the end-to-end messaging and positioning expands and as you see more solutions. You know, let's face it, it's going to be more complex. So the complexity will be abstracted away by new opportunities like what you guys are doing, what you're enabling. So you're starting to see kind of platforms emerging across the board as well as more ISVs. So ISVs, people building software, starting to see now more symbiotic relationship, for developers and entrepreneurship. >> Yeah, so the complexity of the Cloud is certainly something that's not going to get any less as time goes on, right? And I think as companies realize that, they see it, they acknowledge it and I think they're going to lean on partners to help them navigate those waters. So that's where I think the combination of AMD and Cloud Saver, we can really partner very well because I think we're both very passionate about creating customer value, and I think there's a tremendous number of ways that we can collaborate together to bring that to the customers. >> And you know what's interesting too you guys are both hitting on this is that this next partner channel whatever you want to call it is very joint engineering and development. It's not just relationships and selling, there's integration and the new products that can come out is a phenomenal, we're going to watch. I think I predict that the ecosystem's going to explode big time in terms of value, just new things, joint engineering, API... >> 'it's so collaborative too. >> Yeah, it's going to be... >> 'well, the innovation in the marketplace right now is absolutely on fire. I mean, it's so exciting to see all the new technologies have on board. And to be able to see that kind of permeate throughout the marketplace is something that's just really fun and excited to be part of. >> Oh, when you think about the doom and gloom that we hear every day and you look around right now, everybody's building, right? And... >> this and smiling. >> And smiling, right? >> Paul: Today, (laughing loudly) >> Until Thursday when the legs start to get out. >> Yeah. >> Yeah, what recession? I mean, it's so crowded here. And again, this is the point that the Amazon is now a big player in this economy in 2008 that last recession, they weren't a factor. Now you got be tightening new solutions. I think you're going to see, I think more agility. I think Amazon and the ecosystem might propel us out the recession faster if you get the tailwind that might be a big thing we're watching. >> I agree. Cloud computing is inevitable. >> Yeah. >> It's inevitable. >> Yeah, it's no longer a conversation, it's a commitment. And I think we all certainly agree with that. So, Brad is versed in this challenge because we did it in our last segment. But Mark, we have a new tradition I should say, at re:Invent here, where we're looking for your 32nd Instagram reel, your sizzle your thought leadership hot take on the most important story or theme of the show this year. >> For the show as a whole. Wow, well, I think innovation is absolutely front and center today. I think, of the new technologies that we're seeing out there are absolutely phenomenal. I think they're taking the whole Cloud computing to the next level, and I think it's going to have a dramatic impact on how people develop applications and run workloads in the Cloud. >> Well done. What do you think John? I think you nailed it. >> Nailed it. Yeah, want to go for round two? >> Sure. >> Sure, I'll give a shot, (laughing loudly) So... >> 'get it, Brad. >> So, when in public Cloud choice matters? >> It matters. Think about the instance types you use think about the configurations you use and think about the applications you're layering in there and why they're there, right? Optimize those environments. Take advantage of all the tools you have. >> Yeah, you're going to start tuning your Cloud now. I mean, as it gets bigger and better, stronger you're going to start to see just fine tuning more craft, I guess. >> Mark: Yeah. >> In there, great stuff. >> Paul, and in these interesting times, I'm not committed to calling it a recession yet. I still have a chart of hope. I think that the services and the value that you provide to your customers are going to be one of those painkillers that will survive through this. I mean we're seeing a little bit of the trimming of the fat, of extraneous spending in the tech sector as a whole. But I can't imagine folks not wanting to leverage AMD and Cloud Saver, it's exciting, yeah. >> Saving money never goes out of style right? (laughing loudly) >> Saving money is always sexy. I love that, yeah, (laughing loudly) It's actually really... That's a great line goes on. Mark, thank you so much for being here and sharing your story with us. We really appreciate it, Brad. It's been a fabulous thing. You're just going to stay here all day, right? >> I'll just hang out, yeah. >> All right. >> I'm yours. >> I love that. And thank you all for tuning to us live here from the show floor at AWS re:Invent in fabulous sunny Las Vegas Nevada with John Furrier, I'm Savannah Peterson you're watching theCUBE, the leader in high tech coverage. (bright upbeat 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)