(music playing) >> Narrator: Live, from downtown San Francisco It's the Cube. Covering IBM Chief Data Officer Startegy Summit 2018. Brought to you by: IBM >> Welcome back to San Francisco everybody we're at the Parc 55 in Union Square. My name is Dave Vellante, and you're watching the Cube. The leader in live tech coverage and this is our exclusive coverage of IBM's Chief Data Officer Strategy Summit. They hold these both in San Francisco and in Boston. It's an intimate event, about 150 Chief Data Officers really absorbing what IBM has done internally and IBM transferring knowledge to its clients. Steven Eluk is here. He is one of those internal practitioners at IBM. He's the Vice President of Deep Learning and the Global Chief Data Office at IBM. We just heard from him and some of his strategies and used cases. He's joined by Sumit Gupta, a Cube alum. Who is the Vice President of Machine Learning and deep learning within IBM's cognitive systems group. Sumit. >> Thank you. >> Good to see you, welcome back Steven, lets get into it. So, I was um paying close attention when Bob Picciano took over the cognitive systems group. I said, "Hmm, that's interesting". Recently a software guy, of course I know he's got some hardware expertise. But bringing in someone who's deep into software and machine learning, and deep learning, and AI, and cognitive systems into a systems organization. So you guys specifically set out to develop solutions to solve problems like Steven's trying to solve. Right, explain that. >> Yeah, so I think ugh there's a revolution going on in the market the computing market where we have all these new machine learning, and deep learning technologies that are having meaningful impact or promise of having meaningful impact. But these new technologies, are actually significantly I would say complex and they require very complex and high performance computing systems. You know I think Bob and I think in particular IBM saw the opportunity and realized that we really need to architect a new class of infrastructure. Both software and hardware to address what data scientist like Steve are trying to do in the space, right? The open source software that's out there: Denzoflo, Cafe, Torch - These things are truly game changing. But they also require GPU accelerators. They also require multiple systems like... In fact interestingly enough you know some of the super computers that we've been building for the scientific computing world, those same technologies are now coming into the AI world and the enterprise. >> So, the infrastructure for AI, if I can use that term? It's got to be flexible, Steven we were sort of talking about that elastic versus I'm even extending it to plastic. As Sumit you just said, it's got to have that tooling, got to have that modern tooling, you've got to accommodate alternative processor capabilities um, and so, that forms what you've used Steven to sort of create new capabilities new business capabilities within IBM. I wanted to, we didn't touch upon this before, but we touched upon your data strategy before but tie it back to the line of business. You essentially are a presume a liaison between the line of business and the chief data office >> Steven: Yeah. >> Officer office. How did that all work out, and shake out? Did you defining the business outcomes, the requirements, how did you go about that? >> Well, actually, surprisingly, we have very little new use cases that we're generating internally from my organization. Because there's so many to pick from already throughout the organization, right? There's all these business units coming to us and saying, "Hey, now the data is in the data lake and now we know there's more data, now we want to do this. How do we do it?" You know, so that's where we come in, that's where we start touching and massaging and enabling them. And that's the main efforts that we have. We do have some derivative works that have come out, that have been like new offerings that you'll see here. But mostly we already have so many use cases that from those businesses units that we're really trying to heighten and bring extra value to those domains first. >> So, a lot of organizations sounds like IBM was similar you created the data lake you know, things like "a doop" made a lower cost to just put stuff in the data lake. But then, it's like "okay, now what?" >> Steven: Yeah. >> So is that right? So you've got the data and this bog of data and you're trying to make more sense out of it but get more value out of it? >> Steven: Absolutely. >> That's what they were pushing you to do? >> Yeah, absolutely. And with that, with more data you need more computational power. And actually Sumit and I go pretty far back and I can tell you from my previous roles I heightened to him many years ago some of the deficiencies in the current architecture in X86 etc and I said, "If you hit these points, I will buy these products." And what they went back and they did is they, they addressed all of the issues that I had. Like there's certain issues... >> That's when you were, sorry to interrupt, that's when you were a customer, right? >> Steven: That's when I was... >> An external customer >> Outside. I'm still an internal customer, so I've always been a customer I guess in that role right? >> Yep, yep. >> But, I need to get data to the computational device as quickly as possible. And with certain older gen technologies, like PTI Gen3 and certain issues around um x86. I couldn't get that data there for like high fidelity imaging for autonomous vehicles for ya know, high fidelity image analysis. But, with certain technologies in power we have like envy link and directly to the CPU. And we also have PTI Gen4, right? So, so these are big enablers for me so that I can really keep the utilization of those very expensive compute devices higher. Because they're not starved for data. >> And you've also put a lot of emphasis on IO, right? I mean that's... >> Yeah, you know if I may break it down right there's actually I would say three different pieces to the puzzle here right? The highest level from Steve's perspective, from Steven's teams perspective or any data scientist perspective is they need to just do their data science and not worry about the infrastructure, right? They actually don't want to know that there's an infrastructure. They want to say, "launch job" - right? That's the level of grand clarity we want, right? In the background, they want our schedulers, our software, our hardware to just seamlessly use either one system or scale to 100 systems, right? To use one GPU or to use 1,000 GPUs, right? So that's where our offerings come in, right. We went and built this offering called Powder and Powder essentially is open source software like TensorFlow, like Efi, like Torch. But performace and capabilities add it to make it much easier to use. So for example, we have an extremely terrific scheduling software that manages jobs called Spectrum Conductor for Spark. So as the name suggests, it uses Apache Spark. But again the data scientist doesn't know that. They say, "launch job". And the software actually goes and scales that job across tens of servers or hundreds of servers. The IT team can determine how many servers their going to allocate for data scientist. They can have all kinds of user management, data management, model management software. We take the open source software, we package it. You know surprisingly ugh most people don't realize this, the open source software like TensorFlow has primarily been built on a (mumbles). And most of our enterprise clients, including Steven, are on Redhat. So we, we engineered Redhat to be able to manage TensorFlow. And you know I chose those words carefully, there was a little bit of engineering both on Redhat and on TensorFlow to make that whole thing work together. Sounds trivial, took several months and huge value proposition to the enterprise clients. And then the last piece I think that Steven was referencing too, is we also trying to go and make the eye more accessible for non data scientist or I would say even data engineers. So we for example, have a software called Powder Vision. This takes images and videos, and automatically creates a trained deep learning model for them, right. So we analyze the images, you of course have to tell us in these images, for these hundred images here are the most important things. For example, you've identified: here are people, here are cars, here are traffic signs. But if you give us some of that labeled data, we automatically do the work that a data scientist would have done, and create this pre trained AI model for you. This really enables many rapid prototyping for a lot of clients who either kind of fought to have data scientists or don't want to have data scientists. >> So just to summarize that, the three pieces: It's making it simpler for the data scientists, just run the job - Um, the backend piece which is the schedulers, the hardware, the software doing its thing - and then its making that data science capability more accessible. >> Right, right, right. >> Those are the three layers. >> So you know, I'll resay it in my words maybe >> Yeah please. >> Ease of use right, hardware software optimized for performance and capability, and point and click AI, right. AI for non data scientists, right. It's like the three levels that I think of when I'm engaging with data scientists and clients. >> And essentially it's embedded AI right? I've been making the point today that a lot of the AI is going to be purchased from companies like IBM, and I'm just going to apply it. I'm not going to try to go build my own, own AI right? I mean, is that... >> No absolutely. >> Is that the right way to think about it as a practitioner >> I think, I think we talked about it a little bit about it on the panel earlier but if we can, if we can leverage these pre built models and just apply a little bit of training data it makes it so much easier for the organizations and so much cheaper. They don't have to invest in a crazy amount of infrastructure, all the labeling of data, they don't have to do that. So, I think it's definitely steering that way. It's going to take a little bit of time, we have some of them there. But as we as we iterate, we are going to get more and more of these types of you know, commodity type models that people could utilize. >> I'll give you an example, so we have a software called Intelligent Analytics at IBM. It's very good at taking any surveillance data and for example recognizing anomalies or you know if people aren't suppose to be in a zone. Ugh and we had a client who wanted to do worker safety compliance. So they want to make sure workers are wearing their safety jackets and their helmets when they're in a construction site. So we use surveillance data created a new AI model using Powder AI vision. We were then able to plug into this IVA - Intelligence Analytic Software. So they have the nice gooey base software for the dashboards and the alerts, yet we were able to do incremental training on their specific use case, which by the way, with their specific you know equipment and jackets and stuff like that. And create a new AI model, very quickly. For them to be able to apply and make sure their workers are actually complaint to all of the safety requirements they have on the construction site. >> Hmm interesting. So when I, Sometimes it's like a new form of capture says identify "all the pictures with bridges", right that's the kind of thing you're capable to do with these video analytics. >> That's exactly right. You, every, clients will have all kinds of uses I was at a, talking to a client, who's a major car manufacturer in the world and he was saying it would be great if I could identify the make and model of what cars people are driving into my dealership. Because I bet I can draw a ugh corelation between what they drive into and what they going to drive out of, right. Marketing insights, right. And, ugh, so there's a lot of things that people want to do with which would really be spoke in their use cases. And build on top of existing AI models that we have already. >> And you mentioned, X86 before. And not to start a food fight but um >> Steven: And we use both internally too, right. >> So lets talk about that a little bit, I mean where do you use X86 where do you use IBM Cognitive and Power Systems? >> I have a mix of both, >> Why, how do you decide? >> There's certain of work loads. I will delegate that over to Power, just because ya know they're data starved and we are noticing a complication is being impacted by it. Um, but because we deal with so many different organizations certain organizations optimize for X86 and some of them optimize for power and I can't pick, I have to have everything. Just like I mentioned earlier, I also have to support cloud on prim, I can't pick just to be on prim right, it so. >> I imagine the big cloud providers are in the same boat which I know some are your customers. You're betting on data, you're betting on digital and it's a good bet. >> Steven: Yeah, 100 percent. >> We're betting on data and AI, right. So I think data, you got to do something with the data, right? And analytics and AI is what people are doing with that data we have an advantage both at the hardware level and at the software level in these two I would say workloads or segments - which is data and AI, right. And we fundamentally have invested in the processor architecture to improve the performance and capabilities, right. You could offer a much larger AI models on a power system that you use than you can on an X86 system that you use. Right, that's one advantage. You can train and AI model four times faster on a power system than you can on an Intel Based System. So the clients who have a lot of data, who care about how fast their training runs, are the ones who are committing to power systems today. >> Mmm.Hmm. >> Latency requirements, things like that, really really big deal. >> So what that means for you as a practitioner is you can do more with less or is it I mean >> I can definitely do more with less, but the real value is that I'm able to get an outcome quicker. Everyone says, "Okay, you can just roll our more GPU's more GPU's, but run more experiments run more experiments". No no that's not actually it. I want to reduce the time for a an experiment Get it done as quickly as possible so I get that insight. 'Cause then what I can do I can get possibly cancel out a bunch of those jobs that are already running cause I already have the insight, knowing that that model is not doing anything. Alright, so it's very important to get the time down. Jeff Dean said it a few years ago, he uses the same slide often. But, you know, when things are taking months you know that's what happened basically from the 80's up until you know 2010. >> Right >> We didn't have the computation we didn't have the data. Once we were able to get that experimentation time down, we're able to iterate very very quickly on this. >> And throwing GPU's at the problem doesn't solve it because it's too much complexity or? >> It it helps the problem, there's no question. But when my GPU utilization goes from 95% down to 60% ya know I'm getting only a two-thirds return on investment there. It's a really really big deal, yeah. >> Sumit: I mean the key here I think Steven, and I'll draw it out again is this time to insight. Because time to insight actually is time to dollars, right. People are using AI either to make more money, right by providing better customer products, better products to the customers, giving better recommendations. Or they're saving on their operational costs right, they're improving their efficiencies. Maybe their routing their trucks in the right way, their routing their inventory in the right place, they're reducing the amount of inventory that they need. So in all cases you can actually coordinate AI to a revenue outcome or a dollar outcome. So the faster you can do that, you know, I tell most people that I engage with the hardware and software they get from us pays for itself very quickly. Because they make that much more money or they save that much more money, using power systems. >> We, we even see this internally I've heard stories and all that, Sumit kind of commented on this but - There's actually sales people that take this software & hardware out and they're able to get an outcome sometimes in certain situations where they just take the clients data and they're sales people they're not data scientists they train it it's so simple to use then they present the client with the outcomes the next day and the client is just like blown away. This isn't just a one time occurrence, like sales people are actually using this right. So it's getting to the area that it's so simple to use you're able to get those outcomes that we're even seeing it you know deals close quicker. >> Yeah, that's powerful. And Sumit to your point, the business case is actually really easy to make. You can say, "Okay, this initiative that you're driving what's your forecast for how much revenue?" Now lets make an assumption for how much faster we're going to be able to deliver it. And if I can show them a one day turn around, on a corpus of data, okay lets say two months times whatever, my time to break. I can run the business case very easily and communicate to the CFO or whomever the line of business head so. >> That's right. I mean just, I was at a retailer, at a grocery store a local grocery store in the bay area recently and he was telling me how In California we've passed legislation that does not allow plastic bags anymore. You have to pay for it. So people are bringing their own bags. But that's actually increased theft for them. Because people bring their own bag, put stuff in it and walk out. And he didn't want to have an analytic system that can detect if someone puts something in a bag and then did not buy it at purchase. So it's, in many ways they want to use the existing camera systems they have but automatically be able to detect fraudulent behavior or you know anomalies. And it's actually quite easy to do with a lot of the software we have around Power AI Vision, around video analytics from IBM right. And that's what we were talking about right? Take existing trained AI models on vision and enhance them for your specific use case and the scenarios you're looking for. >> Excellent. Guys we got to go. Thanks Steven, thanks Sumit for coming back on and appreciate the insights. >> Thank you >> Glad to be here >> You're welcome. Alright, keep it right there buddy we'll be back with our next guest. You're watching "The Cube" at IBM's CDO Strategy Summit from San Francisco. We'll be right back. (music playing)

>> Narrator: Live from Las Vegas, it's the Cube, covering IBM Think 2018, brought to you by IBM. >> We're back at IBM Think 2018. You're watching the Cube, the leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host, Peter Burris. Ken King is here; he's the general manager of OpenPOWER from IBM, and Sumit Gupta, PhD, who is the VP, HPC, AI, ML for IBM Cognitive. Gentleman, welcome to the Cube >> Sumit: Thank you. >> Thank you for having us. >> So, really, guys, a pleasure. We had dinner last night, talked about Picciano who runs the OpenPOWER business, appreciate you guys comin' on, but, I got to ask you, Sumit, I'll start with you. OpenPOWER, Cognitive systems, a lot of people say, "Well, that's just the power system. "This is the old AIX business, it's just renaming it. "It's a branding thing.", what do you say? >> I think we had a fundamental strategy shift where we realized that AI was going to be the dominant workload moving into the future, and the systems that have been designed today or in the past are not the right systems for the AI future. So, we also believe that it's not just about silicon and even a single server. It's about the software, it's about thinking at the react level and the data center level. So, fundamentally, Cognitive Systems is about co-designing hardware and software with an open ecosystem of partners who are innovating to maximize the data and AI support at a react level. >> Somebody was talkin' to Steve Mills, probably about 10 years ago, and he said, "Listen, if you're going to compete with Intel, "you can copy them, that's not what we're going to do." You know, he didn't like the spark strategy. "We have a better strategy.", is what he said, and "Oh, strategies, we're going to open it up, "we're going to try to get 10% of the market. "You know, we'll see if we can get there.", but, Ken, I wonder if you could sort of talk about, just from a high level, the strategy and maybe go into the segments. >> Yeah, absolutely, so, yeah, you're absolutely right on the strategy. You know, we have completely opened up the architecture. Our focus on growth is around having an ecosystem and an open architecture so everybody can innovate on top of it effectively and everybody in the ecosystem can profit from it and gains good margins. So, that's the strategy, that's how we design the OpenPOWER ecosystem, but, you know, our segments, our core segments, AIX in Unix is still a core, very big core segment of ours. Unix itself is flat to declining, but AIX is continuing to take share in that segment through all the new innovations we're delivering. The other segments are all growth segments, high growth segments, whether it's SAP HANA, our cognitive infrastructure in modern day to platform, or even what we're doing in the HyperScale data centers. Those are all significant growth opportunities for us, and those are all Linux based, and, so, that is really where a lot of the OpenPOWER initiatives are driving growth for us and leveraging the fact that, through that ecosystem, we're getting a lot of incremental innovation that's occurring and it's delivering competitive differentiation for our platform. I say for our platform, but that doesn't mean just for IBM, but for all the ecosystem partners as well, and a lot of that was on display on Monday when we had our OpenPOWER summit. >> So, to talk about more about the OpenPOWER summit, what was that all about, who was there? Give us some stats on OpenPOWER and ecosystem. >> Yeah, absolutely. So, it was a good day, we're up to well over 300 members. We have over 50 different systems that are coming out in the market from IBM or our partners. Over 20 different manufacturers out there actually developing OpenPOWER systems. A lot of announcements or a lot of statements that were made at the summit that we thought were extremely valuable, first of all, we got the number one server vendor in Europe, Atos, designing and developing P9, the number on in Japan, Hitachi, the number one in China, Inspur. We got top ODMs like Super Micro, Wistron, and others that are also developing their power nine. We have a lot of different component providers on the new PCIe gen four, on the open cabinet capabilities, a lot of announcements made by a number of component partners and accelerator partners at the summit as well. The other thing I'm excited about is we have over 70 ISVs now on the platform, and a number of statements were made and announcements on Monday from people like MapD, Anaconda, H2O, Conetica and others who are leveraging those innovations bought on the platform like NVLink and the coherency between GPU and CPU to do accelerated analytics and accelerated GPU database kind of capabilities, but the thing that had me the most excited on Monday were the end users. I've always said, and the analysts always ask me the questions of when are you going to start penetration in the market? When are you going to show that you've got a lot of end users deploying this? And there were a lot of statements by a lot of big players on Monday. Google was on stage and publicly said the IO was amazing, the memory bandwidth is amazing. We are deploying Zaius, which is the power nine server, in our data centers and we're ready for scale, and it's now Google strong which is basically saying that this thing is hardened and ready for production, but we also (laughs) had a number of other significant ones, Tencent talkin' about deploying OpenPOWER, 30% better efficiency, 30% less server resources required, the cloud armor of Alibaba talkin' about how they're putting on their on their X-Dragon, they have it in a piler program, they're asking everybody to use it now so they can figure out how do they go into production. PayPal made statements about how they're using it, but the machine learning and deep learning to do fraud detection, and we even had Limelight, who is not as big a name, but >> CDN, yeah. >> They're a CDN tool provider to people like Netflix and others. We're talkin' about the great capability with the IO and the ability to reduce the buffering and improve the streaming for all these CDN providers out there. So, we were really excited about all those end users and all the things they're saying. That demonstrates the power of this ecosystem. >> Alright, so just to comment on the architecture and then, I want to get into the Cognitive piece. I mean, you guys did, years ago, little Indians, recognizing you got to get software based to be compatible. You mentioned, Ken, bandwidth, IO bandwidth, CAPI stuff that you've done. So, there's a lot of incentives, especially for the big hyperscale guys, to be able to do more with less, but, to me, let's get into the AI, the Cognitive piece. Bob Picciano comes over from running a $15 billion analytics business, so, obviously, he's got some knowledge. He's bringin' in people like you with all these cool buzzwords in your title. So, talk a little bit about infrastructure for AI and why power is the right platform. >> Sure, so, I think we all recognize that the performance advantages and even power advantages that we were getting from Dennard scaling, also known as Moore's law, is over, right. So, people talk about the end of Moore's Law, and that's really the end of gaining processor performance with Dennard scaling and the Moore's Law. What we believe is that to continue to meet the performance needs of all of these new AI and data workloads, you need accelerators, and not just computer accelerators, you actually need accelerated networking. You need accelerated storage, you need high-density memory sitting very close to the compute power, and, if you really think about it, what's happened is, again, system view, right, we're not silicon view, we're looking at the system. The minute you start looking at the silicon you realize you want to get the data to where the computer is, or the computer where the data is. So, it all becomes about creating bigger pipelines, factor of pipelines, to move data around to get to the right compute piece. For example, we put much more emphasis on a much faster memory system to make sure we are getting data from the system memory to the CPU. >> Coherently. >> Coherently, that's the main memory. We put interfaces on power nine including NVLink, OpenCAPI, and PCIe gen four, and that enabled us to get that data either from the network to the system memory, or out back to the network, or to storage, or to accelerators like GPUs. We built and embedded these high-speed interconnects into power nine, into the processor. Nvidia put NVLink into their GPU, and we've been working with marketers like Xilinx and Mellanox on getting OpenCAPI onto their components. >> And we're seeing up to 10x for both memory bandwidth and IO over x86 which is significant. You should talk about how we're seeing up to 4x improvement in training of MLDL algorithms over x86 which is dramatic in how quickly you can get from data to insight, right? You could take training and turn it from weeks to days, or days to hours, or even hours to minutes, and that makes a huge difference in what you can do in any industry as far as getting insight out of your data which is the competitive differentiator in today's environment. >> Let's talk about this notion of architecture, or systems especially. The basic platform for how we've been building systems has been relatively consistent for a long time. The basic approach to how we think about building systems has been relatively consistent. You start with the database manager, you run it on an Intel processor, you build your application, you scale it up based on SMP needs. There's been some variations; we're going into clustering, because we do some other things, but you guys are talking about something fundamentally different, and flash memory, the ability to do flash storage, which dramatically changes the relationship between the processor and the data, means that we're not going to see all of the organization of the workloads around the server, see how much we can do in it. It's really going to be much more of a balanced approach. How is power going to provide that more balanced systems approach across as we distribute data, as we distribute processing, as we create a cloud experience that isn't in one place, but is in more places. >> Well, this ties exactly to the point I made around it's not just accelerated compute, which we've all talked about a lot over the years, it's also about accelerated storage, accelerated networking, and accelerated memories, right. This is really, the point being, that the compute, if you don't have a fast pipeline into the processor from all of this wonderful storage and flash technology, there's going to be a choke point in the network, or they'll be a choke point once the data gets to the server, you're choked then. So, a lot of our focus has been, first of all, partnering with a company like Mellanox which builds extremely high bandwidth, high-speed >> And EOF. >> Right, right, and I'm using one as an example right. >> Sure. >> I'm using one as an example and that's where the large partnerships, we have like 300 partnerships, as Ken talked about in the OpenPOWER foundation. Those partnerships is because we brought together all of these technology providers. We believe that no one company can own the agenda of technology. No one company can invest enough to continue to give us the performance we need to meet the needs of the AI workloads, and that's why we want to partner with all these technology vendors who've all invested billions of dollars to provide the best systems and software for AI and data. >> But fundamentally, >> It's the whole construct of data centric systems, right? >> Right. >> I mean, sometimes you got to process the data in the network, right? Sometimes you got to process the data in the storage. It's not just at the CPU, the GPUs a huge place for processing that data. >> Sure. >> How do you do that all coherently and how do things work together in a system environment is crucial versus a vertically integrated capability where the CPU provider continues to put more and more into the processor and disenfranchise the rest of the ecosystem. >> Well, that was the counter building strategies that we want to talk about. You have Intel who wants to put as much on the die as possible. It's worked quite well for Intel over the years. You had to take a different strategy. If you tried to take Intel on with that strategy, you would have failed. So, talk about the different philosophies, but really I'm interested in what it means for things like alternative processing and your relationship in your ecosystem. >> This is not about company strategies, right. I mean, Intel is a semiconductor company and they think like a semiconductor company. We're a systems and software company, we think like that, but this is not about company strategy. This is about what the market needs, what client workloads need, and if you start there, you start with a data centric strategy. You start with data centric systems. You think about moving data around and making sure there is heritage in this computer, there is accelerated computer, you have very fast networks. So, we just built the US's fastest supercomputer. We're currently building the US's fastest supercomputer which is the project name is Coral, but there are two supercomputers, one at Oak Ridge National Labs and one at Lawrence Livermore. These are the ultimate HPC and AI machines, right. Its computer's a very important part of them, but networking and storage is just as important. The file system is just as important. The cluster management software is just as important, right, because if you are serving data scientists and a biologist, they don't want to deal with, "How many servers do I need to launch this job on? "How do I manage the jobs, how do I manage the server?" You want them to just scale, right. So, we do a lot of work on our scalability. We do a lot of work in using Apache Spark to enable cluster virtualization and user virtualization. >> Well, if we think about, I don't like the term data gravity, it's wrong a lot of different perspectives, but if we think about it, you guys are trying to build systems in a world that's centered on data, as opposed to a world that's centered on the server. >> That's exactly right. >> That's right. >> You got that, right? >> That's exactly right. >> Yeah, absolutely. >> Alright, you guys got to go, we got to wrap, but I just want to close with, I mean, always says infrastructure matters. You got Z growing, you got power growing, you got storage growing, it's given a good tailwind to IBM, so, guys, great work. Congratulations, got a lot more to do, I know, but thanks for >> It's going to be a fun year. comin' on the Cube, appreciate it. >> Thank you very much. >> Thank you. >> Appreciate you having us. >> Alright, keep it right there, everybody. We'll be back with our next guest. You're watching the Cube live from IBM Think 2018. We'll be right back. (techno beat)