>>Hey everyone. Welcome back to the Cube's live coverage of snowflake summit 22 live from Las Vegas. Caesar's forum. Lisa Martin here with Dave Valante. We've got a couple of guests here. We're gonna be talking about every day. AI. You wanna know what that means? You're in the right spot. Kurt UL joins us, the chief customer officer at data ICU and the mod Conn, the head of AI and ML strategy at snowflake guys. Great to have you on the program. >>It's wonderful to be here. Thank you so much. >>So we wanna understand Kurt what everyday AI means, but before we do that for the audience who might not be familiar with data, I could give them a little bit of an overview. What about what you guys do your mission and maybe a little bit about the partnership? >>Yeah, great. Uh, very happy to do so. And thanks so much for this opportunity. Um, well, data IKU, we are a collaborative platform, uh, for enterprise AI. And what that means is it's a software, you know, that sits on top of incredible infrastructure, notably snowflake that allows people from different backgrounds of data, analysts, data, scientists, data, engineers, all to come together, to work together, to build out machine learning models and ultimately the AI that's gonna be the future, uh, of their business. Um, and so we're very excited to, uh, to be here, uh, and you know, very proud to be a, a, a very close partner of snowflake. >>So Amad, what is Snowflake's AI strategy? Is it to, is it to partner? Where do, where do you pick up? And Frank said today, we, we're not doing it all. Yeah. The ecosystem by design. >>Yeah. Yeah, absolutely. So we believe in the best of breed look. Um, I think, um, we, we think that we're the best data platform and for data science and machine learning, we want our customers to really use the best tool for their use cases. Right. And, you know, data ICU is, is our leading partner in that space. And so, you know, when, when you talk about, uh, machine learning and data science, people talk about training a model, but it's really the difficult part and challenges are really, before you train the model, how do you get access to the right data? And then after you train the model, how do you then run the model? And then how do you manage the model? Uh, that's very, very important. And that's where our partnership with, with data, uh, IKU comes in place. Snowflake provides the platform that can process data at scale for the pre-processing bit and, and data IKU comes in and really, uh, simplifies the process for deploying the models and managing the model. >>Got it. Thank >>You. You talk about KD data. Aico talks about everyday AI. I wanna break that down. What do you mean by that? And how is this partnership with snowflake empowering you to deliver that to companies? >>Yeah, absolutely. So everyday AI for us is, uh, you know, kind of a future state that we are building towards where we believe that AI will become so pervasive in all of the business processes, all the decision making that organizations have to go through that it's no longer this special thing that we talk about. It's just the, the day to day life of, uh, of our businesses. And we can't do that without partners like snowflake and, uh, because they're bringing together all of that data and ensuring that there is the, uh, the computational horsepower behind that to drive that we heard that this morning in some of the keynote talking about that broad democratization and the, um, let's call it the, uh, you know, the pressure that that's going to put on the underlying infrastructure. Um, and so ultimately everyday AI for us is where companies own that AI capability. They're building it themselves very broad, uh, participation in the development of that. And all that work then is being pushed down into best of breed, uh, infrastructure, notably of course, snowflake. Well, >>You said push down, you, you guys, you there's a term in the industry push down optimization. What does that mean? How is it evolving? Why is it so important? >>So Amma, do you want to take a first step at that? >>Yeah, absolutely. So, I mean, when, when you're, you know, processing data, so saying data, um, before you train a, uh, a model, you have to do it at scale, that that, that data is, is coming from all different sources. It's human generated machine generated data, we're talking millions and billions of rows of data. Uh, and you have to make sense of it. You have to transform that data into the right kind of features into the right kind of signals that inform the machine learning model that you're trying to, uh, train. Uh, and so that's where, you know, any kind of large scale data processing is automatically pushed down by data IQ, into snowflakes, scalable infrastructure. Um, so you don't get into like memory issues. You don't get into, um, uh, situations where you're where your pipeline is running overnight, and it doesn't finish in time. Right? And so, uh, you can really take advantage of the scalable nature of cloud computing, uh, using Snowflake's infrastructure. So a lot of that processing is actually getting pushed down from data I could down into the scalable snowflake compute engine. How >>Does this affect the life of a data scientist? You always hear a data scientist spend 80% of the time wrangling data. Uh, I presume there's an infrastructure component around that you trying, we heard this morning, you're making infrastructure, my words, infrastructure, self serve, uh, does this directly address that problem and, and talk about that. And what else are you doing to address that 80% problem? >>It, it certainly does, right? Uh, that's how you solve for, uh, data scientists needing to have on demand access to computing resources, or of course, to the, uh, to the underlying data, um, is by ensuring that that work doesn't have to run on their laptop, doesn't have to run on some, you know, constrained, uh, physical machines, uh, in, in a data center somewhere. Instead it gets pushed down into snowflake and can be executed at scale with incredible parallelization. Now what's really, uh, I important is the ongoing development, uh, between the two products, uh, and within that technology. And so today snowflake, uh, announced the introduction of Python within snow park, um, which is really, really exciting, uh, because that really opens up this capability to a much wider audience. Now DataCo provides that both through a visual interface, um, in historically, uh, since last year through Java UDFs, but that's kind of the, the two extremes, right? You have people who don't code on one side, you know, very no code or a low code, uh, population, and then a very high code population. On the other side, this Python, uh, integration really allows us to, to touch really kind the, the fat center of the data science population, who, uh, who, for whom, you know, Python really is the lingua franca that they've been learning for, uh, for decades now. Sure. So >>Talking about the data scientist, I wanna elevate that a little bit because you both are enterprise customers, data ICO, and snowflake Kurt as the chief customer officer, obviously you're with customers all the time. If we look at the macro environment of all the challenges, companies have to be a data company these days, if you're not, you're not gonna be successful. It's how do we do that? Extract insights, value, action, take it. But I'm just curious if your customer conversations are elevating up to the C-suite or, or the board in terms of being able to get democratize access to data, to be competitive, new products, new services, we've seen tremendous momentum, um, on, on the, the part of customer's growth on the snowflake side. But what are you hearing from customers as they're dealing with some of these current macro pains? >>Yeah, no, I, I think it is the conversation today, uh, at that sea level is not only how do we, you know, leverage, uh, new infrastructure, right. You know, they they're, you know, most of them now are starting to have snowflake. I think Frank said, uh, you know, 50% of the, uh, fortune 500, so we can say most, um, have that in place. Um, but now the question is, how do we, how do we ensure that we're getting access to that data, to that, to that computational horsepower, to a broader group of people so that it becomes truly a transformational initiative and not just an it initiative, not just a technology initiative, but really a core business initiative. And that, that really has been a pivot. You know, I've been, you know, with my company now for almost eight years, right. Uh, and we've really seen a change in that discussion going from, you know, much more niche discussions at the team or departmental level now to truly corporate strategic level. How do we build AI into our corporate strategy? How do we really do that in practice? And >>We hear a lot about, Hey, I want to inject data into apps, AI, and machine intelligence into applications. And we've talked about, those are separate stacks. You got the data stack and analytics stack over here. You got the application development, stack the databases off in the corner. And so we see you guys bringing those worlds together. And my question is, what does that stack look like? I took a snapshot. I think it was Frank's presentation today. He had infrastructure at the lowest level live data. So infrastructure's cloud live data. That's multiple data sources coming in workload execution. You made some announcements there. Mm-hmm, <affirmative>, uh, to expend expand that application development. That's the tooling that is needed. Uh, and then marketplace, that's how you bring together this ecosystem. Yes. Monetization is how you turn data into data products and make money. Is that the stack, is that the new stack that's emerging here? Are you guys defining that? >>Absolutely. Absolutely. You talked about like the 80% of the time being spent by data scientists and part of that is actually discovering the right data. Right. Um, being able to give the right access to the right people and being able to go and discover that data. And so you, you, you go from that angle all the way to processing, training a model. And then all those predictions that are insights that are coming out of the model are being consumed downstream by data applications. And so the two major announcements I'm super excited about today is, is the ability to run Python, which is snow park, uh, in, in snowflake. Um, that will do, you know, you can now as a Python developer come and bring the processing to where the data lives rather than move the data out to where the processing lives. Right. Um, so both SQL developers, Python developers, fully enabled. Um, and then the predictions that are coming out of models that are being trained by data ICU are then being used downstream by these data applications for most of our customers. And so that's where number, the second announcement with streamlet is super exciting. I can write a complete data application without writing a single line of JavaScript CSS or HTML. I can write it completely in Python. It's it makes me super excited as, as a Python developer, myself >>And you guys have joint customers that are headed in this direction, doing this today. Where, where can you talk about >>That? Yeah, we do. Uh, you know, there's a few that we're very proud of. Um, you know, company, well known companies like, uh, like REI or emeritus. Um, but one that was mentioned today, uh, this morning by Frank again, uh, Novartis, uh, pharmaceutical company, you know, they have been extremely successful, uh, in accelerating their AI and ML development by expanding access to their data. And that's a combination of, uh, both the data ICU, uh, layer, you know, allowing for that work to be developed in that, uh, in that workspace. Um, but of course, without, you know, the, the underlying, uh, uh, platform of snowflake, right, they, they would not have been able to, to have re realized those, uh, those gains. And they were talking about, you know, very, very significant increases in inefficiency everything from data access to the actual model development to the deployment. Um, it's just really, really honestly inspiring to see. >>And it was great to see Novartis mentioned on the main stage, massive time to value there. We've actually got them on the program later this week. So that was great. Another joint customer, you mentioned re I we'll let you go, cuz you're off to do a, a session with re I, is that right? >>Yes, that's exactly right. So, uh, so we're going to be doing a fireside chat, uh, talking about, in fact, you know, much of the same, all of the success that they've had in accelerating their, uh, analytics, workflow development, uh, the actual development of AI capabilities within, uh, of course that, uh, that beloved brand. >>Excellent guys, thank you so much for joining Dave and me talking about everyday AI, what you're doing together, data ICO, and snowflake to empower organizations to actually achieve that and live it. We appreciate your insights. Thank you both. You guys. Thank you for having us for our guests and Dave ante. I'm Lisa Martin. You're watching the Cube's live coverage of snowflake summit 22 from Las Vegas. Stick around our next guest joins us momentarily.

>> Narrator: Live from New York, it's The Cube. Covering AWS Global Summit 2019. Brought to you by Amazon Web Services. >> Welcome back, I'm Stu Miniman and with my co-host Corey Quinn and we're here at AWS New York City Summit. Always happy when we have users on the program to tell their story, and joining us for the first time, Ahmad Haider who's the Lead Enterprise Data Science Architecter at AGCO, an agricultural company based down in Georgia. Ahmed thanks so much for joining us. >> Thank you for having me. >> All right so, agriculture obviously y'know we understand in general, y'know the joke I have for most people is well luckily, your industry isn't going through much change (laughter) and of course yeah, that's the response we get in most but y'know give us the thumbnail, AGCO, how long's the company been around? The focus and y'know right, some of those changes that you're seeing in the industry. >> Sure, so let me start just about AGCO, so AGCO is about a 9.4 million dollar agricultural equipment manufacturer, it's been around for 20 plus years and we are well known in the industry so some of our famous brands like Valtra, Fendt, Massey. Coming back to your other question, we are not going through a lot of change, I get that very often and you know what, it was an eyeopener when I joined AGCO. So the farming industry is actually going through a lot of change, you must have heard of Agrotech and so the farmers now, they want better efficient solutions that will help them manage their farms while they focus on the core work of farming and they are looking at companies which manufacture agricultural equipment to help provide that digital support, help provide solutions that help manage a farm better, help them to provide the maintenance better, help them optimize the equipment and so on and that's where we are trying to help them out. >> Yep, so it's always easy to look at any industry and they're like oh they have it easy and it's not changing that much. You've got data science in your title, talk a little bit about your role inside the company, y'know we know how important data is to most companies but of course with a data scientist, it's your job to help unlock that power. >> Yeah, definitely. Let me give you a little bit of background and that will help frame this much better so AGCO realized the part of data a while ago but very recently they started working on this so something called a digital experience, digital customer experience program. What that does is basically it creates you a set of connected solutions that manage the data of our customers, our dealers, our part and machine data in a fast, reliable and secure manner and all these digital solutions that we are creating, they are powered through analytics to leverage new market insights, to unlock new opportunities, to help understand our customers better. So given that particular space, I help design the AGCO's data science vision, that involves, first of all, setting up a data science platform that enables us to maximize the user data that we have. Secondly, working with our business to identify analytics use-cases which could be a part of the product roadmap and build them out and then execute this on the data science platform and thirdly, from the point of view of architecture, understanding what things go in the design, making sure everything's state of the art, help the design document and making sure that we are staying right at the top in terms of agriculture, in terms of data science and pushing at the boundaries in all their products. >> What are the, I guess, hidden secrets of data science across the board as the sheer amount of time and effort that has to be put into data normalization before you can start getting useful information out of it, was that a significant concern given what you do? Or given the fact that you more or less control the entire thing and you can reformulate the data as it's ingested? >> That was a very valid concern, I mean what most people don't talk about is the quality of data. They only talk about the data science, the fancy things, so we had the same challenges. Our data was distributed in different places, had different formats, had different levels of cleanliness so what I did was, during the building of the data science platform, I recognized this challenge proactively and made sure that we do cleanse the data, we normalize it to a format that's usable for our use-cases but we don't do it all at once, we go use-case by use-case, we identify our business priorities, we normalize the data, we cleanse it, we normalize it, bring it to a format that can be used going forward and we do it with every use-case. Over time, majority of it will be normalized but that will take an incremental, gradual of course. >> All right, Ahmad bring us into the role of cloud in your environment. >> Sure, so cloud is a very important component, so historically, we were more like an on premises organization and when we went on cloud data, it was a very important change, more so from the point of view, if you think about it, for a company to migrate or position itself, transform itself into a software organization in terms of data science, you need a lot of accelerators, you need data scientists, you need infrastructure, you need data engineers and you need people to manage all of this and all that hiring talent takes time but what cloud does is, there's the ability to procure services on demand and something which is fully managed, all services, that allows you to overcome a lot of those barriers quickly while you have time to actually build other solutions on top of the cloud. Over time when we understand our processes better, our demands better, then we can think about, okay where does it make sense to go hybrid but cloud is that great accelerator that allowed us to set up this data analytics platform which we did in roughly about fifteen weeks. Before that I was working in another organization where we did this on premises and I can tell you it took at least like three times if not more, so that I mean, I think that's the real value of cloud apart from all it's machine learning services and everything. It helped us to accelerate that process easily. How, I guess, in the workflow that you'd wind up going through how close is the data that you're generating to the cloud? Are you doing this at the edge, are you doing this in the field in some cases? I guess where is the data entering your pipeline? >> Yeah, so there are different forms of data that we have, we have a lot of data that is customer-related data that essentially is more or less slow-moving data that we have in the organization. That constitutes the major bulk of the data, apart from that, we have data that are coming from machines which are these smart machines operating in the field and data comes through the satellite and comes to our servers. We also have data that comes from the edge from some of these machinery that are operating in the factory and from there you will get data on the edge. Among all these different data sources that we have, I would say the predominant, or the initial focus, the pillar focus is to first start with the data that we have in abundance, so that's essentially the customer data, our dealer data to be able to understand that better, derive new market insights but our focus is to go forward, getting data from these machines combining that with the soil data with the farming data, with the agronomy data to deliver these very precise, things like precise planting schedules, things like predictive maintenance of machines as they operate out in the field and things like value driven care. So those are things that we are hoping to do with this as well. >> Right, you mentioned machine learning, y'know where are you along your journey kind of with the MLAI and the like? >> That's a really good question, so AGCO as a whole, I think we are at different stages at different parts of the organization so a lot of the organization is focused on generating value through descriptive analytics and explorative analytics whereby we are exploring the data and we are finding these insights and then making decisions on top of them. We are going into the area of predictive analytics fairly recently, about a year so and we essentially, that is our next step so we went into predictive analytics, we are creating machine learning models, we are creating combined stat models. We are using services like SageMaker on the cloud, we are using Spark libraries, we are using Cyclone, we are using Arc, all of that to create predictive analytics solutions. So in terms of the technology that we use right now, it's actually pretty much state of the art, we have created our own model management engines. We are using what Amazon provides and we supplement them with what we have. So we are pretty much at state of the art in terms of current what we are doing. We're hoping to take that state of the art and apply it to large parts of the organization. >> So as you look at, I guess some of the higher level differentiated services coming down in the world of machine learning, do you find that a lot of what you're doing today and in a few years is going to be something that's being handled automatically and then you're able to focus on the more interesting parts of the work? Or is there really no end in sight for I guess sort of some of the current block and tackle that a lot of data scientists are sort of struggling with today? >> I'm sorry I couldn't hear a part of your voice >> No, my apologies. Just a you see things continuing to evolve in this space, are you finding, are you predicting that there's going to be more I guess higher level services that solve some of this problem for you or is a lot of it I guess, block and tackle, not really having a relief point in sight? >> That's a very good question, I get that very often. So, I would like to say the answer, it depends but I'll describe that answer. So there are some parts of this machine learning AI that I think will be solved by newer services, by technology going forward. You can take an example, I'll give you a concrete example, SageMaker, which is fairly recent offering by Amazon about a year ago that we started using SageMaker, it didn't have a lot of competence that it currently has and we had to build a lot of the competence to get towards something called model management. Now, we built all of that but lo and behold after we went, they actually added a lot of these. So over technology, they will take care of a lot of these things which you currently do by smart automation. Now smart automation can take care of a lot of things, it helps you identify when you need to retrain a model, it helps you to deploy a model, it helps you to identify the trigger points but what analytics, I mean, where I think the challenge will come is how to actually apply it to the business because that needs a lot of context and for that you need to understand where are these perfect pinpoints, where do you actually apply it? Does it make sense to use it in a prioritization model? Does it make sense to use it as a explorative model? Does it make sense to use an attribution model? And to help define that use-case in the beginning to essentially say going from a business landscape to come to a specific problem that you want to solve, that is a part that I think will take some time and can't be readily addressed by these technologies but everything down the line, I fairly see that in a few years all of that will be available. >> All right, Ahmad are you speaking here at the conference? >> No, I actually spoke at the keynote in Atlanta. >> Okay >> And the summit >> Great, give us a little about y'know what you get out of coming to some of the regional summits here from Amazon. >> Yeah definitely, so I get a lot out of it. So, the biggest thing is I get to know what are the different things that are happening in the industry from the point of business, so not just about technologies right. Like lots of different technologies coming on but how are people using it? How does it make an impact in their business? Because for me the intersection of technology and business is the key point. So coming to a lot of these regional summits where they have these different business partners, they come in and they describe their work and connecting with them. That, for me, is the main draw, apart from that there's the other piece which is you get to know about the different things that are being done in this space. For example, if you go to AWS summit, you get to know everything that is coming to the cloud and you can try and experiment that and you can basically create like a nice ecosystem. If you go to an Azure summit, you get something similar. So that state of the art is also important but more important is the draw, that intersection. >> And I guess just one followup on that is y'know the data scientist community is y'know, what are some of your best sources of y'know learning and sharing today? >> That's a very good question, data science is one of those aspects because two parts to it. I don't know, I mean now there are machine learning engineers too, so but one part is the technical part of this, to be able to create these models with pinpoint accuracy and the second is applications. So in terms of the first part of learning about creating these models, the best sources in that case would be self-learning, I have, I went through, when I was doing this, I did my PhD, I learned a lot of stuff and then I go through a lot of articles when new things come out, you go through them, once you have the different sources, there are lots of them. The second part, right, applications, I have found the best source of learning there is actually interacting with people who use these technologies. Interacting with people, let's say who have no experience of data science, they have experience of business and then working with them to understand how can you take this insight that's created out of a model and impart into business, for that there's no other substitute than just talking to people, understanding the pinpoints and then solving those. >> All right, well Ahmad thank you so much for giving the update on AGCO and your role inside. >> Thank you >> All right, for Corey Quinn, I'm Stu Miniman, we'll be back with more coverage here from AWS' New York City Summit. Thanks as always for watching the cube. (upbeat electronic music)

>> Announcer: From Denver, Colorado, it's theCUBE, covering Super Computing 17. Brought to you by Intel. (soft electronic music) Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're gettin' towards the end of the day here at Super Computing 2017 in Denver, Colorado. 12,000 people talkin' really about the outer limits of what you can do with compute power and lookin' out into the universe and black holes and all kinds of exciting stuff. We're kind of bringin' it back, right? We're all about democratization of technology for people to solve real problems. We're really excited to have our last guest of the day, bringin' the energy, Armughan Ahmad. He's SVP and GM, Hybrid Cloud and Ready Solutions for Dell EMC, and a many-time CUBE alumni. Armughan, great to see you. >> Yeah, good to see you, Jeff. So, first off, just impressions of the show. 12,000 people, we had no idea. We've never been to this show before. This is great. >> This is a show that has been around. If you know the history of the show, this was an IEEE engineering show, that actually turned into high-performance computing around research-based analytics and other things that came out of it. But, it's just grown. We're seeing now, yesterday the super computing top petaflops were released here. So, it's fascinating. You have some of the brightest minds in the world that actually come to this event. 12,000 of them. >> Yeah, and Dell EMC is here in force, so a lot of announcements, a lot of excitement. What are you guys excited about participating in this type of show? >> Yeah, Jeff, so when we come to an event like this, HBC-- We know that HBC is also evolved from your traditional HBC, which was around modeling and simulation, and how it started from engineering to then clusters. It's now evolving more towards machine learning, deep learning, and artificial intelligence. So, what we announced here-- Yesterday, our press release went out. It was really related to how our strategy of advancing HBC, but also democratizing HBC's working. So, on the advancing, on the HBC side, the top 500 super computing list came out. We're powering some of the top 500 of those. One big one is TAC, which is Texas Institute out of UT, University of Texas. They now have, I believe, the number 12 spot in the top 500 super computers in the world, running an 8.2 petaflops off computing. >> So, a lot of zeros. I have no idea what a petaflop is. >> It's very, very big. It's very big. It's available for machine learning, but also eventually going to be available for deep learning. But, more importantly, we're also moving towards democratizing HBC because we feel that democratizing is also very important, where HBC should not only be for the research and the academia, but it should also be focused towards the manufacturing customers, the financial customers, our commercial customers, so that they can actually take the complexity of HBC out, and that's where our-- We call it our HBC 2.0 strategy, off learning from the advancements that we continue to drive, to then also democratizing it for our customers. >> It's interesting, I think, back to the old days of Intel microprocessors getting better and better and better, and you had Spark and you had Silicon Graphics, and these things that were way better. This huge differentiation. But, the Intel I32 just kept pluggin' along and it really begs the question, where is the distinction now? You have huge clusters of computers you can put together with virtualization. Where is the difference between just a really big cluster and HBC and super computing? >> So, I think, if you look at HBC, HBC is also evolving, so let's look at the customer view, right? So, the other part of our announcement here was artificial intelligence, which is really, what is artificial intelligence? It's, if you look at a customer retailer, a retailer has-- They start with data, for example. You buy beer and chips at J's Retailer, for example. You come in and do that, you usually used to run a SEQUEL database or you used to run a RDBMS database, and then that would basically tell you, these are the people who can purchase from me. You know their purchase history. But, then you evolved into BI, and then if that data got really, very large, you then had an HBC cluster, would which basically analyze a lot of that data for you, and show you trends and things. That would then tell you, you know what, these are my customers, this is how many times they are frequent. But, now it's moving more towards machine learning and deep learning as well. So, as the data gets larger and larger, we're seeing datas becoming larger, not just by social media, but your traditional computational frameworks, your traditional applications and others. We're finding that data is also growing at the edge, so by 2020, about 20 billion devices are going to wake up at the edge and start generating data. So, now, Internet data is going to look very small over the next three, four years, as the edge data comes up. So, you actually need to now start thinking of machine learning and deep learning a lot more. So, you asked the question, how do you see that evolving? So, you see an RDBMS traditional SQL evolving to BI. BI then evolves into either an HBC or hadoop. Then, from HBC and hadoop, what do you do next? What you do next is you start to now feed predictive analytics into machine learning kind of solutions, and then once those predictive analytics are there, then you really, truly start thinking about the full deep learning frameworks. >> Right, well and clearly like the data in motion. I think it's funny, we used to make decisions on a sample of data in the past. Now, we have the opportunity to take all the data in real time and make those decisions with Kafka and Spark and Flink and all these crazy systems that are comin' to play. Makes Hadoop look ancient, tired, and yesterday, right? But, it's still valid, right? >> A lot of customers are still paying. Customers are using it, and that's where we feel we need to simplify the complex for our customers. That's why we announced our Machine Learning Ready Bundle and our Deep Learning Ready Bundle. We announced it with Intel and Nvidia together, because we feel like our customers either go to the GPU route, which is your accelerator's route. We announced-- You were talking to Ravi, from our server team, earlier, where he talked about the C4140, which has the quad GPU power, and it's perfect for deep learning. But, with Intel, we've also worked on the same, where we worked on the AI software with Intel. Why are we doing all of this? We're saying that if you thought that RDBMS was difficult, and if you thought that building a hadoop cluster or HBC was a little challenging and time consuming, as the customers move to machine learning and deep learning, you now have to think about the whole stack. So, let me explain the stack to you. You think of a compute storage and network stack, then you think of-- The whole eternity. Yeah, that's right, the whole eternity of our data center. Then you talk about our-- These frameworks, like Theano, Caffe, TensorFlow, right? These are new frameworks. They are machine learning and deep learning frameworks. They're open source and others. Then you go to libraries. Then you go to accelerators, which accelerators you choose, then you go to your operating systems. Now, you haven't even talked about your use case. Retail use case or genomic sequencing use case. All you're trying to do is now figure out TensorFlow works with this accelerator or does not work with this accelerator. Or, does Caffe and Theano work with this operating system or not? And, that is a complexity that is way more complex. So, that's where we felt that we really needed to launch these new solutions, and we prelaunched them here at Super Computing, because we feel the evolution of HBC towards AI is happening. We're going to start shipping these Ready Bundles for machine learning and deep learning in first half of 2018. >> So, that's what the Ready Solutions are? You're basically putting the solution together for the client, then they can start-- You work together to build the application to fix whatever it is they're trying to do. >> That's exactly it. But, not just fix it. It's an outcome. So, I'm going to go back to the retailer. So, if you are the CEO of the biggest retailer and you are saying, hey, I just don't want to know who buys from me, I want to now do predictive analytics, which is who buys chips and beer, but who can I sell more things to, right? So, you now start thinking about demographic data. You start thinking about payroll data and other datas that surround-- You start feeding that data into it, so your machine now starts to learn a lot more of those frameworks, and then can actually give you predictive analytics. But, imagine a day where you actually-- The machine or the deep learning AI actually tells you that it's not just who you want to sell chips and beer to, it's who's going to buy the 4k TV? You're makin' a lot of presumptions. Well, there you go, and the 4k-- But, I'm glad you're doin' the 4k TV. So, that's important, right? That is where our customers need to understand how predictive analytics are going to move towards cognitive analytics. So, this is complex but we're trying to make that complex simple with these Ready Solutions from machine learning and deep learning. >> So, I want to just get your take on-- You've kind of talked about these three things a couple times, how you delineate between AI, machine learning, and deep learning. >> So, as I said, there is an evolution. I don't think a customer can achieve artificial intelligence unless they go through the whole crawl walk around space. There's no shortcuts there, right? What do you do? So, if you think about, Mastercard is a great customer of ours. They do an incredible amount of transactions per day, (laughs) as you can think, right? In millions. They want to do facial recognitions at kiosks, or they're looking at different policies based on your buying behavior-- That, hey, Jeff doesn't buy $20,000 Rolexes every year. Maybe once every week, you know, (laughs) it just depends how your mood is. I was in the Emirates. Exactly, you were in Dubai (laughs). Then, you think about his credit card is being used where? And, based on your behaviors that's important. Now, think about, even for Mastercard, they have traditional RDBMS databases. They went to BI. They have high-performance computing clusters. Then, they developed the hadoop cluster. So, what we did with them, we said okay. All that is good. That data that has been generated for you through customers and through internal IT organizations, those things are all very important. But, at the same time, now you need to start going through this data and start analyzing this data for predictive analytics. So, they had 1.2 million policies, for example, that they had to crunch. Now, think about 1.2 million policies that they had to say-- In which they had to take decisions on. That they had to take decisions on. One of the policies could be, hey, does Jeff go to Dubai to buy a Rolex or not? Or, does Jeff do these other patterns, or is Armughan taking his card and having a field day with it? So, those are policies that they feed into machine learning frameworks, and then machine learning actually gives you patterns that they can now see what your behavior is. Then, based on that, eventually deep learning is when they move to next. Deep learning now not only you actually talk about your behavior patterns on the credit card, but your entire other life data starts to-- Starts to also come into that. Then, now, you're actually talking about something before, that's for catching a fraud, you can actually be a lot more predictive about it and cognitive about it. So, that's where we feel that our Ready Solutions around machine learning and deep learning are really geared towards, so taking HBC to then democratizing it, advancing it, and then now helping our customers move towards machine learning and deep learning, 'cause these buzzwords of AIs are out there. If you're a financial institution and you're trying to figure out, who is that customer who's going to buy the next mortgage from you? Or, who are you going to lend to next? You want the machine and others to tell you this, not to take over your life, but to actually help you make these decisions so that your bottom line can go up along with your top line. Revenue and margins are important to every customer. >> It's amazing on the credit card example, because people get so pissed if there's a false positive. With the amount of effort that they've put into keep you from making fraudulent transactions, and if your credit card ever gets denied, people go bananas, right? The behavior just is amazing. But, I want to ask you-- We're comin' to the end of 2017, which is hard to believe. Things are rolling at Dell EMC. Michael Dell, ever since he took that thing private, you could see the sparkle in his eye. We got him on a CUBE interview a few years back. A year from now, 2018. What are we going to talk about? What are your top priorities for 2018? >> So, number one, Michael continues to talk about that our vision is advancing human progress through technology, right? That's our vision. We want to get there. But, at the same time we know that we have to drive IT transformation, we have to drive workforce transformation, we have to drive digital transformation, and we have to drive security transformation. All those things are important because lots of customers-- I mean, Jeff, do you know like 75% of the S&P 500 companies will not exist by 2027 because they're either not going to be able to make that shift from Blockbuster to Netflix, or Uber taxi-- It's happened to our friends at GE over the last little while. >> You can think about any customer-- That's what Michael did. Michael actually disrupted Dell with Dell technologies and the acquisition of EMC and Pivotal and VMWare. In a year from now, our strategy is really about edge to core to the cloud. We think the world is going to be all three, because the rise of 20 billion devices at the edge is going to require new computational frameworks. But, at the same time, people are going to bring them into the core, and then cloud will still exist. But, a lot of times-- Let me ask you, if you were driving an autonomous vehicle, do you want that data-- I'm an Edge guy. I know where you're going with this. It's not going to go, right? You want it at the edge, because data gravity is important. That's where we're going, so it's going to be huge. We feel data gravity is going to be big. We think core is going to be big. We think cloud's going to be big. And we really want to play in all three of those areas. >> That's when the speed of light is just too damn slow, in the car example. You don't want to send it to the data center and back. You don't want to send it to the data center, you want those decisions to be made at the edge. Your manufacturing floor needs to make the decision at the edge as well. You don't want a lot of that data going back to the cloud. All right, Armughan, thanks for bringing the energy to wrap up our day, and it's great to see you as always. Always good to see you guys, thank you. >> All right, this is Armughan, I'm Jeff Frick. You're watching theCUBE from Super Computing Summit 2017. Thanks for watching. We'll see you next time. (soft electronic music)

>> Announcer: Live from the Javits Center in New York City, it's the theCUBE, covering Inforum 2017. Brought to you by Infor. (bright electronic music) >> We are back with theCUBE's coverage of Inforum 2017. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by Barig Siraj and Nasser Bayram. They are both of the Zahid Group, out of Saudi Arabia. Thank you both so much for joining us. >> Good to be here, thank you for having us. >> So I want you to start out by just explaining to our viewers a little bit about what Zahid Group and Zahid Tractor, what you do. >> We are a large group based in Saudi Arabia. We're very diversified. We are mainly in heavy equipment, capital equipment business. We are the importer of Caterpillar machinery and Volvo trucks, Renault trucks, and many other products. More than 40 franchises. We have locations in more than 40 locations, or branches, more than 40 locations for their area, and we have about 4,000 employees, and we mainly focus on providing sales and after-sales services in the kingdom, with a big focus on after-sales. We pride ourselves to be the second to none when it comes down to after-sales services, and we strongly believe in technology and in digital transformation that is sweeping the world of business, and thus far, we embarked on this journey five years ago. >> So what does that digital transformation mean for your business, and generally, and then specifically for IT. Maybe you can start, Nasser. >> Well, first, we have to agree. The business model has changed. There are new business models that has disrupted every single industry landscape out there, and you have to be ready to change and accept that transformation, otherwise, you'll be left behind. The digital transformation takes you beyond managing an organization introducing an IT platform or technology. You have to change the way you think and your readiness to be able to manage where the future is going. If we look, we just attended this session, 52% of Fortune 500 companies in year 2000 no longer exist. They went out of business. In 2015, 55% of Fortune 500 companies lost money. There was no economic crisis or downfall. It simply missed the boat, or they did not, they were not very innovative in their digital strategy or thinking ahead, allowing their industry to be disrupted by people like Uber, Amazon, Alibaba, Souq, an other new entrants with very great innovative ideas and technologies. The old business model of cutting cost or restructuring an organization no longer works. You need to think differently and act differently, and hence, digital transformation becomes critical for your organization, and implementing an ERP platform, standardizing rationalization of your ERP platform, if you have more than one, like in our case, we have more than one, you have to have one standardized platform, one standardized processes, business processes, so that we have one source of data in order to be ready for the future where you can mine that data, have it be by analytics or business intelligence, in order to be able to better serve your customers and learning on about their behavior, about their trends, and how you can better position production services for them in the future to buy, and for you to remain profitable. >> So Barig, okay so now, that's, what Nasser just described, I'm inferring, is much more real-time, much faster, and more data. Your ability to analyze that data wherever it is, how do you, and the processes and people behind that as we talked about technology's the easy part even though some technology's even more complicated than ever. So what does that mean for the IT organization? >> Well for IT organization, we had, and we still have a legacy application built over 30 years. Now, and there we could not reap the benefits of the data mining, the standardization, even that just from AI capabilities on top of that. We cannot reap that until we have that standardized ERP Platform across all our companies. So basically, that's the tall order that was put on our plate, and what we have done, we started the journey. We're partly through it. We went live with two of our companies. We still have three more to go, and we've done it with lesser volume, allowing us to learn and therefore, once we reach our biggest volume company, we would have learned as an organization, not just applying the technology that even the personnel, the change management, the resistant pockets have to deal with all of that. >> Can you give an example of what you've learned along the way, becoming, as we said, it is so much about change management, and it's about getting people over this fear of change. Can you give an example of what you've learned, of what you're doing differently for the companies that have yet to have the rollout? >> The biggest learning experience we had, we just went live with one of our companies, called EJAR, which is a rental company. The success there of the learning, the success is a learning experience. We have a long journey for to go live with five companies, and this is the first one to go live. What we learned by doing that company first is the challenges of change management, how to support on live, challenge of data migration, data cleansing, readiness of the organization, not simply from change management perspective, but also from IT, legal, readiness of your documentation, the contracts, et cetera. It's a vast learning curve to overcome, and we're very happy that we took the strategic decision to go live once more company, so that we gain that experience, and that is the real success we got out of this project now. Now we better we feel we are in better position for the new companies to go forward with, when we go live, we learn so much about change management, where we failed and where we succeeded, we learn better about our readiness, whether it is Zahid Tractor, or Infor, or our IT, our infrastructure, our training program, our after go-live support, the war room was set up to support the go-live, and go in production. We've been two month in production. We're still having some challenges, but nothing that, there are no showstoppers, however, more and more every day, we learn more and more, and we are better positioned to go live with a bing bang on the big company. >> Nasser, as the executive in sort of leading this transformation, do you look for and demand new metrics, new types of KPIs that you want to see? >> Well, definitely, you do the whole thing because of the new metrics. The new metrics have to have built into it, not simply the traditional KPIs of your GPs and revenue and discount and so on, you need to look at customer behavior, customer analytics, pricing positioning, where you are going forward. In the old days, everybody would sit down around the table, say, "Hey, we're number one, okay?" That doesn't hold water anymore. You're number one in what? It's about number one in responding to customer requirements on that customer behavior. Today, with Amazon.com, many retail businesses are challenged, they're going out of business. How do you stop that business model? You can't. So how do you compete? You can. To do that, you have to have the right data in place, the right organization in place, and the right mindset to be able to lead your organization to compete in the new market space. >> Can you give our viewers some examples of the kind of data that you are deriving, in terms of this business analytics, in terms of understanding and deepening your understanding of customer behavior, and what customers want, and how it's changing, how you approach your customers and what you do for them. >> I'll give you a comparison. When we have a legacy systems, what you do at end of day, you extract your data, you transform it and you load it up to your data mart or data warehouse, and then you run your report, and if you're lucky, you have savvy users who can create their own reports on the fly, but with the way we're going with an integrated ERP solution and one standardized platform, we do hope we have the right analytics in place, and business intelligence in place, that we give our management the right data to make decisions, ready to make decisions. Not filtered data, not reports designed, and that takes me straight into your question on IT and ability to IT to deliver. There is no way for any IT organization to cope with the changes. Nowadays, when Amazon went live recently with Whole Food, it took them three to six, three to four months to deal with legal, to deal with retail, with pricing, with the announcement, the whole nine yards of marketing. How did they have their IT ready? That's a challenge. How can you do that in four to six month? That is the challenge in the future. If you don't have the right platform to do that, you will never be able to compete, and data analytics are critical for you to respond or predict the behavior of customer, so before a customer comes next time to the counter, you already have certain statistics that tell you what that person is ready for, and that takes you straight also into IoT. Your products, or our products now, are connected to the Internet. If you don't have IoT in place, connected to your back end, and your analytics, you won't be able to compete, and that would be the differentiator in the future. Those who could do that versus those who will continue to follow the old brick and mortar business model, restructuring and cost-cutting and whatnot. >> So your instrumenting your heavy equipment in the field, presumably, and that's, you're well down the road with that. That changes the data model, it changes the analytics model so I wonder if you could describe that a little bit. I mean, obviously you're processing data at the edge. How much data stays at the edge versus comes back to your central location, maybe you could add some color to that whole equation. >> Well the devices that are put on the machines, there are several ways of putting. The older models, you have, actually the PSSR has to actually go with his laptop, hook it up, suck the data, and bring it back for analytics. The newer models are more, are sending it to, directly to us, and enabling our, what I call tower, to do equipment monitoring, and be able to anticipate, we call up the customer and saying, "By the way." Actually tell the salesmen to call up the customer and saying, "You need to bring your machine in "because it's, you might face a failure "in so amount of time." So improving the customer side, that is, that is that part, but coming back to the organization change issue, we went from a legacy application that the branch managers waited until the end of the month to get the truth, to now being able to, seeing the performance on a daily basis, because they're seeing the truth because everything is connected, whereas before, whatever they did, they don't, their piece of the puzzle, they have a lot missing, and they, information that they waited until it show, send them back there, a report. >> And none of this takes place in the public cloud, is that right? >> No, it does, to add to that, the data is stored in the cloud. Customers have access to it, along with our SOS lab, which is oil sampling lab. They have access to the data to see what is happening, like predictive analysis of their machine performance, and as a result of analyzing the oil, plus any data collected from these machines. We do have cloud implementation. We just went live with our treasury management system. It is on the cloud, and it was our first deployment on the cloud, though the implementation of Infor today is still on-premise. Long-term, down the road, we may be looking at the cloud. >> I got to ask you, we hear Infor messaging about microspecialization, that last mile, all the hard stuff that nobody else wants to do. Is that something that you take advantage of in your industry, or is it? >> I'll give you an example. We utilize the implementation accelerator from Infor for the rental, and it's 77% of our processes map directly into that, so we, that enabled us, that, to have EJAR, which is a rental company, go much smoother. Now, we're working with Infor to enhance their equipment implementation accelerator, and it will be partly the same ratio, around 70% of the processes that we're going to go live with, are the standard processes in the product, out of the box, for the equipment rental, for the equipment business space. >> Our objective is to reduce customization as much as possible, go out of the box, or native, out of the box, as much as possible, but you have to accept the fact, depending on your business environment and some localization requirement, you have to do some customization. However we do have a governance in place, to make sure it's to the minimal. Otherwise, long-term, you'll be challenged with release management and change management and so on, and when you speak of the cloud, if you ever elect to go to the cloud, you can kiss customization goodbye. (Dave laughs) You have to be ready to adopt and adapt. >> And how about your security regime, as a result of the edge and IoT and now, cloud, how is that evolving? >> That's close to my heart. (laughs) >> Yeah, I'll bet, and probably the board's. >> Actually, well, (laughs) actually, interesting enough, many organization, like ourselves included, we invested so much money in building firewalls and security systems to protect what's behind the wall. Now with the cloud, well your most important data is no longer behind the wall. >> Rebecca: It's right there. >> It's outside the wall, so you have to have some kind of a hybrid security system, and you really have to pick the right partner who is hosting your cloud application, leasing your cloud application to you, so the challenge or the perspective of security, cybersecurity, changes drastically and totally, and your understanding of it has to change, otherwise, you just stay behind your own wall and guess what? You can end up locking yourself behind the wall, and you're going to miss the boat, but this does not mean that you'll let down your guard. You have to maintain your security awareness, you have to maintain your security diligence, and you should not underestimate the threats out there, because even if you are on the cloud, the biggest threat nowadays is through phishing. That's what we call the human firewall. Relegating the right awareness, the right education to your organization from within, to understand the threats and the danger of such a threat, otherwise, your password, that's how you access the cloud, you'll end up be compromised and guess what? So will be your data. >> Yes, so, Barig, Nasser, thank you so much for joining us. It's been great to have you on the program. >> Our pleasure. >> Thank you. >> Nasser: Thank you for hosting us, thank you. >> See you guys again, great, thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from Inforum after this. (bright electronic music) (bright instrumental music)

>> Announcer: Live from San Francisco, it's The Cube covering DevNet Create 2017 brought to you by Cisco. >> Welcome back here, we're live here in San Francisco for SiliconANGLE's the Cube's exclusive 2 days of coverage for Cisco's inaugural event DevNet Create, building on their 3 year old successful DevNet program which is Cisco core developer program now foraying out into the world of cloud native developers, open source, great move for Cisco. Our next guest, Wissam Ali-Ahmad, lead solutions architect with Splunk. Good to see you. >> Good to see you too, John. >> Here with Peter Burris of course, my co-host. >> Wissam: Hi, Peter. >> So Splunk being here is an important thing because you guys have been riding the wave for cloud, certainly your relationship with Amazon web service is well known, very successful. Splunk as a company went public, well known. You guys really, really hit a niche around big data and how cloud has helped you guys accelerate your business. So you've been transformed, but continuing to grow, so you're riding that wave, but now Cisco's on the wave, and Cisco's been involved in the wave. But from a relationship standpoint, oh yeah, we're the networking guys, we're going to come in and help Docker with this, we're going to come in and help Splunk with this, so they've been kind of a helper, not the main player. This is a new way to get back in and be really enabled for the cloud world. What's your reaction to this move by Cisco? >> I mean, we have a great partnership with Cisco for many years. And I think, you know, Splunk plays a good, as you said, we're a good player there. We integrate well. I mean, all the initiatives Cisco's involved with, we have integrations with Cisco on many levels with different technology. And also Splunk, the deal is with Splunk is that you need to bring invisibly to everything, and Splunk is that platform where you have access to all that data throughout all, all is like all that machine data so you have access to all that data, not only application data, not only network data. You need to look at everything these days. Especially when there's attacks. You know we heard recently, of course everybody heard about WannaCry, and to the tech, that attack, you need to look at everything, because you could someone bring in a laptop behind the firewall even, and they can be affected already, and if you don't have access to see what they're doing, not just from a network perspective, like what apps in the cloud they're accessing, you know, what other files on the locally, so, because you have access to all that data in Splunk, you should be able to get better visibility. >> And you guys have a unique position in the sense that you're close, again, to the machine. You know, logs and data We had Amanda on from Cisco, who was, in her tribe as a developer, she's not necessarily a network engineer, but she's brought on that mojo in from the developer community. When she was first day on the job, you know, they were doing some Python, some rest API stuff, you know, basic 101 stuff, but she didn't want to do an app that was showing hey, how many Twitter followers do I have? She had to go in and look at the devices. So now the opportunity with IOT is that for Cisco to make and expose the network for programmability >> Wissam: Right. >> And extend it. How are they going to do that? I mean you're closer to those guys in your relationship, but that's what everyone wants. They want the infrastructure to just go, that's DevOps >> Right. Yeah, they want the edge to come to them. They want data to be more accessible to all the users. And then so Cisco's on that path, definitely on that path, to get more infrastructure visibility in the data center and the networks, so they're definitely on that path of doing that. >> And let me build on this, so if we think about the various components associated with some of the things that Splunk does. A leader in the application of machine and AI and big data related technologies, to solving business problems. The algorithms for doing this have been around for a long time. The hardware couldn't do it, so you had to write really tight software to do it, and you were one of the first companies out there to really do that. And then it was, we'll point all that at sources of data, that you can apply these technologies, to create better business value. And there were two places where people did it. Web logs, for online marketing, and IT, since IT technology throws off an enormous amount of data. So as I think about it, the relationship with Cisco is especially interesting, because Cisco is going to be one of those companies that encourages people to create new sources of data and a lot of it, IOT and other places, and bring it back to companies and technologies that have a proven track record for generating value out of that data. So talk a bit about how Splunk intends to, going back to what John said, riding that wave. The algorithms are here, the hardware can do it, now we've got to get access to more of the data, and here comes Cisco being really serious about moving a lot of data around. What do you think? >> I mean, we like when people bring in a lot of data into Splunk. We also have been focusing a lot on the personas. On the, we call the Sherlock, the data Sherlock. Right, so that unique persona is where they need to look at, how do I make sense of my data? Not only just about bringing data, but how do I make sense of that data. What are solutions? What are use case I need to have better impact on the business? So we're actually helping solve real kind of business use cases. This morning, Yelp had a webinar about how they use Splunk driving all the web infrastructure for Yelp, the Yelp back end for all their-- >> Peter: This is still in the IT? >> Yeah. >> Peter: It's not Yelps marketing group, this is still in the IT? >> But they are correlating that with other business use cases, yes. >> Of course, it will start coming together. So where do you see some of these use cases popping up, now that Cisco is helping to create those new sources, and get people to, you know, acculturating people to the idea that these are sources of value, business value. Where do you see some of the new use cases? >> There's a lot of use cases now coming up around business analytics, around IOT as you mentioned. And an added element of machine learning across different data sources. So if I want to look at not just performance of one service, let's say my elevator, I want to see how that's going to affect other areas of my business, too. So you're able to see not only the power of correlating that data, but also be able to apply machine learning on that data. So there's a lot of use cases around business analytics. Security's always there, because security, as you know, attack vectors are getting complex every few months or so, so you need to also chase that, and you need to look at all the data, the behaviors in that data, to get better predictability, to get better prevention detection. >> So Splunk is emerging as a great software company for a lot of IT pros, but it still is more in the op side. How is this conference and the likelihood or the notion that developers are increasingly going to be part of that use case, it's utilizing data and data-related services to better understand operations, but find new ways of creating value out of the capabilities provided by that. What's the developer angle here for Splunk? >> Great question. We actually are focusing a lot on developer tools. So Splunk, being a platform. I always say Splunk is a full-feature platform for machine data and big data. So it's open in the sense that developers can develop their own content on Splunk. They can extend what we have. So an example of that is, the recent project called Mexico Contaro. So that's a project full that's looking at internet usage and coverage on Mexico, in Mexico City and across all the cities. And this was using Splunk to end Meraki API's, and bring all that data together, and network data to try to give exposure to kind of like government analytics. And that's a neat case because not necessarily only IT, but also helping all the goods out there. >> So Cisco, Meraki and other sources, plus Splunk to be able to get deep visibility into a number of ways, you know, a very complex system like Mexico City, which is about as complex as you get, actually operates. >> Wissam: Yes. That's one, yeah. >> Tell about the Splunk direction now, because everyone's been questioning about the public offering, because you're not putting numbers out there, active community, it's not that you guys aren't being transparent, but you've got to go to the next level of growth. Obviously Cisco's coming at the cloud native world. We see the cloud native compute foundation, really with great support of the Linux foundation. New open source stuff's going on all the time. How is Splunk looking at the future right now? What's next? I mean obviously security, we heard that at Dot Conf last year, but you guys have really a good position with the data. You have good account names. You've got great blue chip customers. What's next? What's the product solution look like for you guys? What's the new architecture? What's the new plan? >> I think more listening, looking at all the scale, and cloud and listen to the customers, making the data onboarding easier, making it more scalable, covering more use cases that we talked about. Innovate a lot of areas around machine learning, all that to cover more of the use cases, so we're definitely moving forward to go the next step beyond just-- >> So let's take another example. So DevOps, right, everyone loves the DevOps. It's not like a solution, you can't buy DevOps, you just got to do it, right? So that's pretty clear. You can't just write an Agile manifesto and say, "We're DevOps." You got to have a vision, maybe write a manifesto just to get the people motivated, but put the right people in place, let the things organically develop. So the question is, what is an ideal architecture, and what is a best practice, from your standpoint, where you've seen examples of people who've transformed into this DevOps world, where they really got the ball rolling, got some change happening, and then scaled it. Can you give us a kind of a pattern that you've seen the customers? >> I have not seen personally a lot of that, but definitely there's transformation happening. It's not easy to move into that DevOps switch. You cannot do it overnight. So you need as much as possible tools that would actually give exposure, how am I doing, right? Am I pushing my code at the speed it's expected to be? Do I have bugs addressed early on? So that kind of exposure you need a system that will give you basically to analyze all that data too, and then at Splunk we have a story on DevOps. DevOps and application exposure monitoring and that. And the unique thing about Splunk is that you don't only look at what's inside the application, which was AMP's that do application management, but you should look at everything, so we look outside the black box. Not inside the app, but look at outside too, so we're going to give you exposure of your whole DevOp process You know, from the beginning, the whole condis integration, so I see Splunk helping organizations moving into that kind of new process. >> But there's an interesting relationship between tools and process, or tools and skills, so John, you'll probably laugh at this. Many years ago I found myself sitting in a room with the CEO of a very, very large pharmaceutical, me and a group of other other consultants, and he said, the discussion was, are we going to buy SAP or not? And after two hours of people arguing about it, he finally said, "Screw it, we're doing it, "I'm sick and tired of these process arguments. "We're just going to do what SAP says in the process." There's a relationship between the practices suggested by Splunk and the types of things that a business actually does in a DevOps sense. What is this, how is Splunk changing the notion of DevOps, and how is now as Splunk extends itself, how is DevOps and new practices and new ways of thinking, altering the way that Splunk delivers capability? >> I mean, we always listen to our customers. And then we've actually been looking at addressing use cases, like on DevOps, from a persona aspect. Like as a DevOp engineer, I won't be able to address this kind of issues, and we listen to that, and we try to address those, not only just by a tool, but also by looking at best practices around that. And sometimes we manifest those through apps. So Splunk can actually, you can publish an app as a developer if you're not happy as a customer, you can modify, take one of our existing free apps, and then modify them cue on process, so we're not kind of specific rigid to certain way, and I know DevOps, and Agile Ward, is not even like a religion, you know, you're not supposed to follow, you're supposed to be flexible in certain areas, and even implementing DevOps comes in Agile way too. >> But it's still pedagogical, and John in many respects, there's your manifesto for DevOps, right? Is your choice of tools and how they come together, and degree to which they're integrated kind of take priority. >> Well, you got eight minutes until you have to go up on stage and do your talk. Here we're live in San Francisco. What are you going to be speaking about when you hit the stage in eight minutes? You have seven minutes to explain (laughs). >> (Laughs) Deliver pitch. So I'll be focusing a lot on the integrations that we have with various Cisco products, so we have, with Splunk you're able to bring in a lot of the API, data through API integrations, so I'm going to show how easy that process is to bring that data if you have an API like Meraki or ACI or Ice. And I'll also be focusing more on how the data you can do it from the cloud, easy, without having an agent involved, without having any software you need to install to collect the data, and we'll be talking more about the Mexico Contaro case, and then do some fun live demos also. >> But Cisco's got good API's, people might not know that, but they are API'd up pretty well on the equipment and the gear and the platform. >> Yes, of course. >> Just commentary on that, your reaction to share for people who are not fluent in Cisco, in terms of their enablement of getting data out? >> Yes, Cisco has a lot of good API's, capabilities around sharing that data, the openness of it has been great, and made easy for us, even for our customers to bring that data, the API, that data into Splunk, so it's a matter of a few minutes now to point to that API and bring that data into Splunk, and yeah, that's good. >> Wissam Ali-Ahmad, going on stage in seven minutes, you got it all done, congratulations. Thanks for coming on The Cube. I know you've got your big speech here to the packed house. Inaugural event here, Cisco's DevNet Create. Thanks for coming on The Cube. >> Thank you, John. >> More live coverage here in San Fransciso. This is The Cube, I'm John Furrier, with my co-host Peter Burris. Stay with us as we get down to wrapping up day two. Stay with us for more coverage after this short break. >> Hi, I'm April Mitchell, and I'm the senior directory of strategy and plan

>> Voiceover: Live from Las Vegas. It's The Cube. Covering Dell EMC World 2017. Brought to you by Dell EMC. >> And the band played on. You might be able to hear the guitar player off in the distance. It's that time of day here at Dell EMC World 2017, along with John Furrier. I'm John Walls. Glad to have you here on The Cube. We are officially, John and I now, Node-O-Ramas. (laughing) We have joined the blue button club. We'll explain that in just a little bit. Tell you what it's all about. Here with me to do that is Armughan Ahmad, who is the SVP of Blueprint Solutions and Alliances of Dell EMC. Just had a launch. >> Yeah. Had to be one of the two. And Brian Payne who is the VP of Product Management in the server division at Dell EMC. Brian, thank you for being with us. >> Absolutely. Thanks for having me >> All right, so first off, let's talk 14G. Big server news, you guys make. I'm sure that's really had a lot of your attention this week. A lot of people want to know, Brian, what's up? Tell me about the excitement you generate with that announcement. >> Absolutely, it's generated a ton of excitement and it's not just been this week. It's been a lot of build up for driving a new generation of servers into the market. We start with what our customers are telling us that they're interested in, and with this generation we focused on the typical things you would expect, like how can we run workloads more effectively than the current generation of technology. However, as we look into the landscape as people drive digital transformation, the workloads are changing, right? There are a lot of new workloads. There's a lot of new technology that our customers need to sort out and figure out, where do I apply where in order to run things more effectively? And so we're focused on that in terms of delivering portfolio breadth so that our customers will have the capability when they need it to run their applications well. So that's one thing that is exciting and new. But aside from that, which is running our customers' applications, we're also focused on how can we make our customers more agile and effective through the automation tools that we've designed into this generation of servers? And then, lastly, security has been a big focus. And it's not bolted on security; it's integrated security built into the server throughout the supply chain and throughout the life cycle of the server. Those are the big things that have resonated with our customers as we've announced the next generation of servers. >> I was kind of kidding on the top there talking about the Node-O-Rama buttons. Both of you are wearing yours. So tell us what is that all about? What's Node-O-Rama going on there? >> So Janet Moore, who's actually in our product marketing group, came up with Node-O-Rama because as we were getting ready to launch 14G, awesome servers, Poweredge 14 Generation, we wanted to be ready for VSAN ready nodes 'cause customers really wanted to take storage and take that software-defined storage and ensuring when you take software-defined storage you want to really run it on a server platform to drive the next generation of IT transformation and digital transformation eventually. But we also wanted to the same thing with Microsoft Spaces Direct. We also wanted to do the same thing with our ScaleIO, software-defined scale out storage capability. But then not just stop there. We also have SAP HANA ready node, which is our SAP HANA for commercial and midsize customers. So that's where Node-O-Rama really came in. We've got a lot of nodes. So right now we're launching our Microsoft Spaces Direct ready node that got launched on Monday. So we're totally excited. We have the most ready nodes in the industry right now. >> So we were talking in our intro this morning on our other set, David Floyer, analyst at Wikibon, and Keith Townsend, another analyst. We were kind of looking at this announcement here. The big takeaways were really, really strong hyper-converged ACI message. Seeing that across the board. VMware is the glue layer between all this. And then finally, reality of hybrid cloud. So we were just talking about the ready systems. How does this all work? Because now, those are three nice areas developing. How does Node-O-Rama fit in that? How should they think about ready nodes, the context of that scene? >> Well, one thing that I mentioned a moment ago is just this idea of complexity that customers are dealing with. We still have, through our ready systems, we're able to offer simplicity for customers that want to buy a full system-level solution, but not everyone is, for a variety of reason, is ready to do that. However, they're left with saying, "Okay, I can buy servers from Dell, Poweredge Servers "and go run my workload, "but what do I pick? "I want to move to a software-defined storage. "I want to run something like SAP HANA. "Can somebody simplify that process for me?" And that's where ready nodes come in. It really streamlines the selection of technology where we've done the testing. We've done the validation to figure out what's going to run well and then we can point customers in that direction. And we can also streamline the services, the service offering around that. So it's really about making it simpler for out customers throughout the lifecycle of picking the technology and then deploying and managing. >> What about operational support? Efficiency, ease of use there? What's your position on that? >> Absolutely, operational support is streamlined and then if you have an issue with a ready node and you call up Dell services, they're going to immediately recognize what you have and be able to get you back up and running and working more effectively, more quickly. >> So where's the Nexus here, alliances and then what you're doing there? How's that coming together? >> Yes, so I lead our solutions business unit that is powered by our technology alliance partners, so VMware VSAN ready node, Microsoft Spaces Direct ready node. ScaleIO happens to be our own IP, so that's a ready node, and then SAP. So those are the alliance partnerships. And then what my group does is we work very close with Brian Payne and Ashley Gorakhpurwalla, whose at GM, for our server division, and Robbie Penaganti. That server division, it's all about the server right in the center of it so if you are going to drive a software-defined data center, you have to get a server right in the middle and make sure that server's not only scalable, it's intelligent, but it's also secure. So what we do is we actually take that server that's ready from their side and they certify it. We then take that in my group. We validate it, we make sure that the firmware that needs to be changed, the buyout that needs to be changed. The service capability, the sales enablement that we have to put out there. So it becomes a ready node, right? >> So tell me about the old days. I'm just kind of going, "Wow! "That sounds really easy" but it's not. They, in essence, have to build a server that's going to be ready for whatever composed solution you put together, whether it's VMware, Edge, or whatever. >> Armughan: Yeah. >> They have to then make the enablement happen. >> Armughan: Yeah. >> So in the old days, what was it like? Compare and contrast what it was in the old days. Go to the server guy and say, "I need these servers to support this, this and this" and then they go do it. >> Brian: Yeah. >> And months later. Take us through why is this different for the customer? >> It actually starts very early in the process as we look at the technology landscape, working with Armughan's team to figure out what technologies are going to change and transform the efficiency of how we run applications. It starts with defining the servers arm-in-arm with the team that's responsible for delivering those applications, figuring out what's going to work, develop it, and then bring it to market. And then it's really about streamlining that selection process for our customers. How can we make it easy for them to pick the right things and then quickly procure that and deploy that in their environment and start getting the business results that they're after? >> So time to market for the solution is optimized in that scenario? >> Brian: Oh yeah. >> You call in for the server, 14G. (finger snap) You have it all prepared, ready for you to go. >> So John, in the past, let's go back a few years, right? Our 13G servers at that time, or any other servers in the industry, were really developed for multi-workloads. They weren't developed for specific workloads. What we have now done at Dell EMC, and this is the synergy that Marius was talking about earlier that you were mentioning, which is we take our server group, we work hand-in-hand in our server group right up front, so that's 14G, as our 14th generation of Poweredge servers were being designed, Brian Payne and I, and our teams work very close together to say, "Okay, what are the top workload orientations "that we want to go after?" So software-defined storage, definitely top priority. Now, who should we be working with? VMware VSAN, of course. Microsoft Hyper-v Spaces Direct. Our ScaleIO business, because we know a lot of the customers want to do that. But then, in addition to that, we said, "Okay, ready nodes is good. "That's fantastic." But we know customers go from build to buy continue. So they'll be customers who would want SAP workload orientation, they would want Oracle workload orientation. They want Sequel workload orientation. But then those are your traditional apps. But now you're moving into the next generation apps of machine learning, AI, which is starting with high-due clusters and analytics clusters. So our partnership between server product group and our solutions product group. My product group does not exist without server product group. We have to ensure, and by the way, same thing goes for storage product group, our data protection product group, and our networking product group, as well as our CI and ACI product group. What we do is we, essentially, work right up front and make sure that that workload orientation is start through right in the beginning. >> John: What's the customer reaction? >> You want to take that. >> Yeah, sure, I was just going to add one piece and I'll address that. Conversely, the server isn't going to do anything without the application running on top of it. So that's where we go hand-in-glove here. Customers are very pleased with it. The adoption rates have been very strong of what's been in the market and then as we're bringing a breath of fresh air with the next generation technology, customers are very eager to begin adopting. >> John: What's the reaction to this announcement because the 14G had the fanfare yesterday when it was talked about, but what is the reaction to the 14G and the ready server nodes now? >> I'll give you an example, first of all, on our revenue growth. So we actually picked some major workload so VSAN ready node. We'd announced that about six months ago and our VSAN ready node business is through the roof right now on 13G. 14G launches as soon as the summer. Ashley Gorakhpurwalla mentioned on stage sometime this summer. As soon as that launches, we will be ready with 14G. But right now we have ready nodes already in the market on our 13th generation platforms. And as soon as we started launching these solutions we're finding that our customers, more importantly our channel partners as well, because they find that it's much easier, John, for them to deploy that. We're also seeing that same 13G to now 14G migration related to high-performance computing. A lot of customers are taking that on and the growth has been really fabulous. >> Yeah, I think if you rewind the clock before ready nodes and say, "What was the world like?" We had customers that were deploying and trying to deploy things like VSAN or other software-defined storage, and they were running into problems and us, VMware, we're trying to help customers navigate that, but what we found was there were dependencies in that stack in the underlying infrastructure, and so the ready nodes really came out of that how can we improve that customer experience and make sure that what we deliver is going to be trusted and reliable. >> And shipping around the summer, which is right around the corner. >> That is 14G is going to ship but right at the same time, our ready nodes for VSAN ready node and Microsoft Spaces Direct ready node and ScaleIO ready node will ship at the exact same time 14G Poweredge servers ship, right? But keep in mind, we're already selling all of the 13G-based platforms for ready nodes, ready bundles, and ready systems. >> John: I tell you, just knowing the channel partners, they're going to love this. >> Oh yeah. >> Because it's so peaked and not a lot of training involved and they can pick up the training and services (finger snap) right out of the gate, target workloads, good engagement of customers. Makes a lot of sense. Hangs together in my mind. Congratulations. >> Brian: Thank you. >> All right, so Node-O-Rama, this is the button here. >> Armughan: It's right here. >> Check out the ready nodes. It just sounds great. Ready, alert, fire jets go. (laughter) Take off in the aircraft carrier. >> There is nothing like being an honorary Node-O-Rama. So thank you very much for the pleasure. >> Getting ready to Rama. >> Always good seeing you guys. >> Thanks for being with us. >> Armughan: Thank you. >> Back with more coming up here. Dell EMC World 2017 Live from Las Vegas. You're watching The Cube. (techno music)

>> Announcer: From Boston, Massachusetts, it's The Cube. Covering Red Hat summit 2017. Brought to you by Red Hat. >> Welcome back to The Cube's coverage of the Red Hat summit here in Boston, Massaachusetts. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We are joined by Armughan Ahmad, he is the senior vice president and general manager solutions and alliances at Dell EMC. Thanks so much for joining us. >> It's my pleasure, good to see you, Rebecca. >> So we've had you on the program before, but your role has changed a bit at Dell EMC since then. Tell us what you're doing now. >> Sure, I have the pleasure to now lead our solutions business unit that we have under infrastructure solutions group. What we drive is focus areas of customer outcomes. Work load orientation around high performance computing. Driving data analytics, business critical applications, software defined solutions, and then also hybrid cloud. So those are our five big priorities. >> It's a big mandate. >> It is a big mandate, right? And as you know, Dell EMC is Number one in everything. That's all we talk about. You'll hear this at Dell EMC World next week. But you know, at Red Hat summit, we're really having this discussion, right, Red Hat open stack summit, which is really around our differentiation, how we're driving human progress forward, social innovation forward. So that's exciting. So as we take our applications and partner with our alliance partners, that's the differentiation we're excited to share with customers and partners here at Red Hat summit as well. >> So Dell EMC, as you said, is uniquely suited to do these things and lead in this way. But how do you make deployment easier? I mean, that's the big question that customers and partners need to know. >> Yeah, absolutely. So you as you know, being number One in everything, when I joked about this, not joking about this, if you really think about our market share in compute or servers, if you look at our market share in storage, external storage, internal storage, you look at our market share in converge infrastructure, hyperconverge infrastructure, if you see our market share in data protection, or our market share in open networking, right, so we're all the way to the far top right of the Gartner magic quadrants, number one in market shares and revenue. That's all interesting, but what's fascinating for the customers is really more about how do you make all of this real? If you envision like a pyramid almost, and you think that the bottom is all of these infrastructure layers, the next one above that is virtualization, the next one above that is orchestration, but really on the top, is a platform, top of the pyramid, that's where the business sits. Business wants a platform, and what we're doing is trying to make all of that easy. We know that customers will build and they would want to do a DIY solution. And we obviously have that, we've been doing it for decades. But we're really trying to move to that top end of the pyramid with our hybrid could solutions, our converge solutions, but more the solutions that my organization leads is the blueprint solutions. And the whole idea about blueprint solutions is that how can we offer ready offerings to customers so that they don't have to really worry about the bottom of the pyramid, but the top of a platform so that's it's easy to deploy. >> And customized for their business. >> Absolutely. >> Armughan, in the keynote on day one, we heard that one of the top priorities for customers is figuring out their cloud strategy. Now, at Dell EMC, you have a number of offerings, can you bring us up to date, where does open stack fit into that, and of course, we're going to want to talk about the Red Hat joint solution that you're after. >> Yeah, absolutely. You know, open stack, let me take it even a step back, you know Michael, 31 years ago, since he founded Dell, has always stood for choice for customers, open ecosystems for customers. And even though we have Dell technologies now, the acquisition of so many of the other assets that are under Dell technologies, we're really delighted to partner and ensure that we have the right kind of choice that we're offering to our customers. So open stack, Stu, puts a very big differentiation forward. You know, I'm here with our Dell EMC team at Red Hat open stack summit and our customers are telling us in a very, very clear way, and the channel partners who are here, is that they're looking for Dell EMC to really provide open source based solutions in telecom markets, in, you know, when you take a look at telecom and it's moving from 3G to 4G to now 5G coming on, it's really going to be the applications and how those applications become scaled out versus just infrastructure becoming scaled out. So now the evolution of open stack and how Dell EMC contributes to it, we never really wanted to build our own ecosystem of open stack like some of our other competitors have done. We've always stood by Red Hat open stack based solutions to say hey, if they're number one in open stack markets and they're already tuning that, why can't we tune our infrastructure solutions the exact same way so that one plus one equals five for the customers, and it becomes much easier for them to deploy that. >> Great, so absolutely, you mentioned some of the telecoms. NFV was probably the most talked about use case for open stack at last year's summit. We've got the open stack summit here in Boston next week, we'll be covering it. Is that a top use case for your solution with Red Hat, what are the real business drivers for people doing open stack, is it just private cloud solutions that they offer that you said mentioned the open source, people are still trying to figure out where this open stack fits compared to some of the other options that they have. >> Stu, what I'm finding, and you and I have had these discussions several times across the stack of server storage networking and others, the largest cost associated with deploying or consuming IT is really your OPEX cost. So if you envision for a second a pie chart and you look at a customer spend, a capital spend, about 25% of that is CAPEX oriented, which is how much you pay for infrastructure or software. About 75% of that is OPEX oriented, which is your human cost of managing it, your serviceability and others. The whole idea about us talking about this Dell EMC ready bundle solution that we're taking to market, so we announced yesterday our opportunity to really go out and simplify all of this for customers, for cloud solutions, or for their NFV or NFVI solutions, as we're seeing NFVI-- >> And for our audience that doesn't know NFVI, what's the differentiation there? >> Our opportunity to take network function virtualization, then taking VNF capabilities, and then also making sure that we're virtualizing a lot of those aspects on NFVI so that our customers are driving service provider opportunities to then containerize these opportunities as part of open shift and others. And we feel that our differentiation at Dell EMC really, then, ends up becoming our tested validated offerings so that customers don't really have to worry about the infrastructure layer, or even the software layer for that matter, and we can just give them a platform that I was referring to earlier. So that ready bundle for open stack that we have offered, and I will be taking about it in my keynote today, that whole ready bundle at Dell EMC solution has been validated, tested. It's got not just reference architectures, but deployment guides, run books. But we've also taken it one step forward, we actually internally called it jetstream. And the whole idea of jetstream internal codename was, if you guys are familiar with jetstreams around the world, and you catch one of those jetstreams, they usually go from west to east. And if you go from Boston to London, you can get there pretty quickly if you hit one of those because it's 160 miles an hour. That's why we selected the name jetstream. And the whole idea is if you actually imagine if you put a concord in that jetstream, you can actually do that trip now in three hours, or you could've done it in concords around at the time. So if we can actually create that concord-like style of a ready bundle solution that is running open stack platform, we can not only get the customers to deploy much faster and reduce their OPEX, but there's a tooling that's required. So for example, the customer wants to deploy an open stack solution. We actually created a jetpack, jetstream, jetpack, and the whole idea of a jetpack is very quickly us providing sizing tools and deployment tools for customers so that they can get to their destination very, very fast. >> And how fast are we talking here? >> So we're talking, I'll actually have a customer, East Carolina University, on stage with me. Something that would take three weeks, they've got it done in three days using this jetpack solution. So us creating these ready bundles and deploying open stack much faster, either for cloud environments or environments for NFV and eventually for NFVI. And then we're also working with our Dell EMC code group, which is now looking at containerization solutions as well. So that's sort of the differentiations that we're talking about. >> And Armughan, I know, we're really good usually at quantifying that kind of deployment, that shrinking months to days or days to hours, that operational efficiency though, once it's in there, do you have any metrics or cost savings that your customers in general are seeing of rolling this out versus the old kind of putting it together themselves. >> Great question, Stu, so we all measured, Rebecca, you know this, you've written for HBR, which is really about ROI, TCOs for customers, what is your return on investment and your total cost of ownership. And really, what we're finding is that we can do this about 30% more effective. I'd love to say it's 80% more effective where we can take your OPEX down and others. But realistically, if you really look at East Carolina University or many of the other customers who are deploying this, they're seeing on average about 30% improvement in their operating costs. Now, it's not just related to cloud or it's not just related to NFV and NFVI. We're also seeing a huge use case of open stack now as part of high performance computing. So as high performance computing is evolving from traditional research and moving more into machine learning and AI frameworks, we're also seeing customers leverage open stack in that environment as well. >> and I wonder also, I mean, just talking about the difficulties with calculating ROI, but talking about how it's having this big impact on high performance computing, what about high performance teams, the people who are actually doing the work? >> Absolutely, and so talking about high performance team, right, the web tech, it started in Silicon Valley, now it's in Dublin, Ireland, or it's in China or all of these other places, they've really figured out, right, how do you drive efficiency. I mean, at Facebook, I think one server admin manages 50,000 physical servers or something like that. That's a scale out ways. >> And the thing we always say, it's that person's job is varied, it's not just that their doing three orders of magnitude more than the poor guy running around the data center, they've changed really how they focus on the application, and that job is very different. So they don't really even have server admins, they just have the number of head count that they need. >> The number of head count that's required. >> Hyperscale model, very different from what we have in the enterprise world. >> Absolutely, absolutely. But there are lessons to be learned from the hyperscale model. And if you can drive, I mean, according to IDC, one server admin manages about 40 physical servers, somewhere between 30 to 40 physical servers versus the number that I just shared with you, right, from these big web tech providers. So if we can even improve that to 100 or 1,000 to one admin. I think sys admins still should continue to exist even though this whole public cloud is coming in. But the rise of edge computing for us is also a big, big phenomenon. And we want to ensure that the rise of edge computing, Dell EMC is at the forefront of ensuring that we're providing analytic solutions to our customers. And a lot of the analytics are really happening at the edge 'cause you need to make those analytics decisions very quick 'cant really have a lot of latency back to public cloud for that. So our hybrid cloud solutions, working very closely with open stack to drop OPEX costs down, all of that really matters to customer right now. >> Armughan, I want to go back to something you talked about in the very beginning, which is this element of human progress. It's a professional and personal passion of yours to use technology for good, to solve some of the world's most complex problems, educating young women, working in developing countries, curing cancer. Talk a little bit about what you're doing. >> You know, Rebecca, that's a huge passion of not just mine, but Michael, and all of our executive leadership team at Dell EMC. We were talking earlier before this interview started, it's a passion of yours and Stu's. We all love to, as human beings, contribute to society. And human progress is really technologies impacting human progress in different ways. Right, if you talk about manufacturing jobs versus what automation is. But at the same time, technology is also helping in many different areas. So if you look at developing countries, now I'm personally involved in girls' education in third world countries where they're not prioritized, and what can technology do at schools to really get them to learn coding and get a differentiation out very, very quickly. But at the same time, our Dell initiatives, we call it the legacy for good. The Dell initiatives are really, not just about diversity and inclusion, it's also about improving the human progress. I'll give you an example. We have a great customer, T-Gen. And T-Gen is in the healthcare field and they drive genome sequencing solutions, so they have scientists who drive genome sequencing. Now, if you think about genome sequencing before technology, how long it would take somebody to sequence certain genomes for the purpose of cancer research, that would take you years. Now, if you can get that done in minutes, and that technology will learn, and then next time you do it, it would be even seconds for the same platform. So we actually developed a life sciences genome sequencing high performance computing cluster for this customer. And now they're able to very quickly help young girls and young kids improve their longevity with their cancer treatment that they're going through. So those are the things that really matter to our teams. And I know it matters to our customers and our partners. Because now we're not talking about just open stack or Dell EMC and our great number one in everything solutions we have. Those are fantastic, but how do you relate that social innovation, how do you relate that to human progress. To me, that is really the differentiation that we all collectively need to continue to drive and talk about this a little bit more. But we do need to find more connection points that we know that technology can help, but it's really those medical professionals and those researchers, they're really the brainiacs who use our technology, our opportunity as tech geeks, or I call myself a geek, at least, is how do we take that and then take that out to them and then real researchers can build their platforms on top of it to cure cancer. Or to go drive manufacturing jobs for social innovation purposes in middle America or around the world. That's the difference and those are the solutions that my team, along with many others at Dell EMC, along with our partners with Red Hat, we're focused on, we talk about that a lot. And Jim Witers talked about social innovation and how Red Hat is also making that a priority this morning in his keynote. >> Armughan, it sounds like your team is quite busy. And I know you've got your big event coming up next week, so you finish the keynote here, you'll be jetting our to Las Vegas. Rebecca, a big set of our Cube team will all be out in Vegas to cover the show. So give our audience a little bit of a preview of what you can about what we should expect for the new Dell EMC world as kind of taking together what EMC world has been doing for many years and Dell world in the past. >> You know, we're really excited, Stu, about Dell EMC world because this is the first time Dell world and EMC world comes together in Vegas. So we'll look forward to having you guys there. We have great speakers lined up, it's really focused for customers and technical audiences. We've got lots of partners there. But more importantly, we're showcasing all the solutions and the culmination of Dell EMC merger that has happened along with our Dell technologies group of companies like Pivotal along with VMWare along with Secureworks along with Virtustream. And how do we differentiate not just the Dell brand, which is our client computing group that we have, but also our Dell EMC, that's server storage networking, and then with VMWare and Pivotal and others. What you'll see is not just great keynotes, but some great speakers, great entertainment. I don't know if that's been released, I think it's been released. Gwen Stefani, I think she's-- >> Andy Grammar, and yeah, Gwen Stefani. >> Gwen Stefani, yeah, so that's going to be pretty cool, so we're excited about that. But the speakers that we have lined up on main stage along with, I'm more excited, I geek out, I'm a nerd, I love going into these technical breakouts where we've got lab equipment set up where people can actually get to enjoy and, I call it enjoyment, which is really geek out with understanding what are all of those solutions that we have, kind of, you know, put together. And those blueprint solutions, what are they. We have obviously, our server storage networking and data protection. But then how do you get into those labs and run some demos and proof of concepts, that makes it easy for the customers. So we're excited about that as you can see. >> Well, we're looking forward to it, we'll see you there. >> Yeah, we look forward to hosting you there. >> Armughan, thank you so much for joining us. >> Thank you, my pleasure. >> This has been Rebecca Knight and Stu Miniman, we will return with more from Red Hat summit after this.

(gentle music) >> Hey! Welcome back to theCUBE's coverage of WiDS 2023, the eighth Annual Women In Data Science Conference. I'm your host, Lisa Martin. We are at Stanford University, as you know we are every year, having some wonderful conversations with some very inspiring women and men in data science and technical roles. I'm very pleased to introduce Tracy Zhang, my co-host, who is in the Data Journalism program at Stanford. And Tracy and I are pleased to welcome our next guest, Rhonda Crate, Principal Data Scientist at Boeing. Great to have you on the program, Rhonda. >> Tracy: Welcome. >> Hey, thanks for having me. >> Were you always interested in data science or STEM from the time you were young? >> No, actually. I was always interested in archeology and anthropology. >> That's right, we were talking about that, anthropology. Interesting. >> We saw the anthropology background, not even a bachelor's degree, but also a master's degree in anthropology. >> So you were committed for a while. >> I was, I was. I actually started college as a fine arts major, but I always wanted to be an archeologist. So at the last minute, 11 credits in, left to switch to anthropology. And then when I did my master's, I focused a little bit more on quantitative research methods and then I got my Stat Degree. >> Interesting. Talk about some of the data science projects that you're working on. When I think of Boeing, I always think of aircraft. But you are doing a lot of really cool things in IT, data analytics. Talk about some of those intriguing data science projects that you're working on. >> Yeah. So when I first started at Boeing, I worked in information technology and data analytics. And Boeing, at the time, had cored up data science in there. And so we worked as a function across the enterprise working on anything from shared services to user experience in IT products, to airplane programs. So, it has a wide range. I worked on environment health and safety projects for a long time as well. So looking at ergonomics and how people actually put parts onto airplanes, along with things like scheduling and production line, part failures, software testing. Yeah, there's a wide spectrum of things. >> But I think that's so fantastic. We've been talking, Tracy, today about just what we often see at WiDS, which is this breadth of diversity in people's background. You talked about anthropology, archeology, you're doing data science. But also all of the different opportunities that you've had at Boeing. To see so many facets of that organization. I always think that breadth of thought diversity can be hugely impactful. >> Yeah. So I will say my anthropology degree has actually worked to my benefit. I'm a huge proponent of integrating liberal arts and sciences together. And it actually helps me. I'm in the Technical Fellowship program at Boeing, so we have different career paths. So you can go into management, you can be a regular employee, or you can go into the Fellowship program. So right now I'm an Associate Technical Fellow. And part of how I got into the Fellowship program was that diversity in my background, what made me different, what made me stand out on projects. Even applying a human aspect to things like ergonomics, as silly as that sounds, but how does a person actually interact in the space along with, here are the actual measurements coming off of whatever system it is that you're working on. So, I think there's a lot of opportunities, especially in safety as well, which is a big initiative for Boeing right now, as you can imagine. >> Tracy: Yeah, definitely. >> I can't go into too specifics. >> No, 'cause we were like, I think a theme for today that kind of we brought up in in all of our talk is how data is about people, how data is about how people understand the world and how these data can make impact on people's lives. So yeah, I think it's great that you brought this up, and I'm very happy that your anthropology background can tap into that and help in your day-to-day data work too. >> Yeah. And currently, right now, I actually switched over to Strategic Workforce Planning. So it's more how we understand our workforce, how we work towards retaining the talent, how do we get the right talent in our space, and making sure overall that we offer a culture and work environment that is great for our employees to come to. >> That culture is so important. You know, I was looking at some anitab.org stats from 2022 and you know, we always talk about the number of women in technical roles. For a long time it's been hovering around that 25% range. The data from anitab.org showed from '22, it's now 27.6%. So, a little increase. But one of the biggest challenges still, and Tracy and I and our other co-host, Hannah, have been talking about this, is attrition. Attrition more than doubled last year. What are some of the things that Boeing is doing on the retention side, because that is so important especially as, you know, there's this pipeline leakage of women leaving technical roles. Tell us about what Boeing's, how they're invested. >> Yeah, sure. We actually have a publicly available Global Diversity Report that anybody can go and look at and see our statistics for our organization. Right now, off the top of my head, I think we're hovering at about 24% in the US for women in our company. It has been a male majority company for many years. We've invested heavily in increasing the number of women in roles. One interesting thing about this year that came out is that even though with the great resignation and those types of things, the attrition level between men and women were actually pretty close to being equal, which is like the first time in our history. Usually it tends on more women leaving. >> Lisa: That's a good sign. >> Right. >> Yes, that's a good sign. >> And we've actually focused on hiring and bringing in more women and diversity in our company. >> Yeah, some of the stats too from anitab.org talked about the increase, and I have to scroll back and find my notes, the increase in 51% more women being hired in 2022 than 2021 for technical roles. So the data, pun intended, is showing us. I mean, the data is there to show the impact that having females in executive leadership positions make from a revenue perspective. >> Tracy: Definitely. >> Companies are more profitable when there's women at the head, or at least in senior leadership roles. But we're seeing some positive trends, especially in terms of representation of women technologists. One of the things though that I found interesting, and I'm curious to get your thoughts on this, Rhonda, is that the representation of women technologists is growing in all areas, except interns. >> Rhonda: Hmm. >> So I think, we've got to go downstream. You teach, I have to go back to my notes on you, did my due diligence, R programming classes through Boeings Ed Wells program, this is for WSU College of Arts and Sciences, talk about what you teach and how do you think that intern kind of glut could be solved? >> Yeah. So, they're actually two separate programs. So I teach a data analytics course at Washington State University as an Adjunct Professor. And then the Ed Wells program is a SPEEA, which is an Aerospace Union, focused on bringing up more technology and skills to the actual workforce itself. So it's kind of a couple different audiences. One is more seasoned employees, right? The other one is our undergraduates. I teach a Capstone class, so it's a great way to introduce students to what it's actually like to work on an industry project. We partner with Google and Microsoft and Boeing on those. The idea is also that maybe those companies have openings for the students when they're done. Since it's Senior Capstone, there's not a lot of opportunities for internships. But the opportunities to actually get hired increase a little bit. In regards to Boeing, we've actually invested a lot in hiring more women interns. I think the number was 40%, but you'd have to double check. >> Lisa: That's great, that's fantastic. >> Tracy: That's way above average, I think. >> That's a good point. Yeah, it is above average. >> Double check on that. That's all from my memory. >> Is this your first WiDS, or have you been before? >> I did virtually last year. >> Okay. One of the things that I love, I love covering this event every year. theCUBE's been covering it since it's inception in 2015. But it's just the inspiration, the vibe here at Stanford is so positive. WiDS is a movement. It's not an initiative, an organization. There are going to be, I think annually this year, there will be 200 different events. Obviously today we're live on International Women's Day. 60 plus countries, 100,000 plus people involved. So, this is such a positive environment for women and men, because we need everybody, underrepresented minorities, to be able to understand the implication that data has across our lives. If we think about stripping away titles in industries, everybody is a consumer, not everybody, most of mobile devices. And we have this expectation, I was in Barcelona last week at a Mobile World Congress, we have this expectation that we're going to be connected 24/7. I can get whatever I want wherever I am in the world, and that's all data driven. And the average person that isn't involved in data science wouldn't understand that. At the same time, they have expectations that depend on organizations like Boeing being data driven so that they can get that experience that they expect in their consumer lives in any aspect of their lives. And that's one of the things I find so interesting and inspiring about data science. What are some of the things that keep you motivated to continue pursuing this? >> Yeah I will say along those lines, I think it's great to invest in K-12 programs for Data Literacy. I know one of my mentors and directors of the Data Analytics program, Dr. Nairanjana Dasgupta, we're really familiar with each other. So, she runs a WSU program for K-12 Data Literacy. It's also something that we strive for at Boeing, and we have an internal Data Literacy program because, believe it or not, most people are in business. And there's a lot of disconnect between interpreting and understanding data. For me, what kind of drives me to continue data science is that connection between people and data and how we use it to improve our world, which is partly why I work at Boeing too 'cause I feel that they produce products that people need like satellites and airplanes, >> Absolutely. >> and everything. >> Well, it's tangible, it's relatable. We can understand it. Can you do me a quick favor and define data literacy for anyone that might not understand what that means? >> Yeah, so it's just being able to understand elements of data, whether that's a bar chart or even in a sentence, like how to read a statistic and interpret a statistic in a sentence, for example. >> Very cool. >> Yeah. And sounds like Boeing's doing a great job in these programs, and also trying to hire more women. So yeah, I wanted to ask, do you think there's something that Boeing needs to work on? Or where do you see yourself working on say the next five years? >> Yeah, I think as a company, we always think that there's always room for improvement. >> It never, never stops. >> Tracy: Definitely. (laughs) >> I know workforce strategy is an area that they're currently really heavily investing in, along with safety. How do we build safer products for people? How do we help inform the public about things like Covid transmission in airports? For example, we had the Confident Traveler Initiative which was a big push that we had, and we had to be able to inform people about data models around Covid, right? So yeah, I would say our future is more about an investment in our people and in our culture from my perspective >> That's so important. One of the hardest things to change especially for a legacy organization like Boeing, is culture. You know, when I talk with CEO's or CIO's or COO's about what's your company's vision, what's your strategy? Especially those companies that are on that digital journey that have no choice these days. Everybody expects to have a digital experience, whether you're transacting an an Uber ride, you're buying groceries, or you're traveling by air. That culture sounds like Boeing is really focused on that. And that's impressive because that's one of the hardest things to morph and mold, but it's so essential. You know, as we look around the room here at WiDS it's obviously mostly females, but we're talking about women, underrepresented minorities. We're talking about men as well who are mentors and sponsors to us. I'd love to get your advice to your younger self. What would you tell yourself in terms of where you are now to become a leader in the technology field? >> Yeah, I mean, it's kind of an interesting question because I always try to think, live with no regrets to an extent. >> Lisa: I like that. >> But, there's lots of failures along the way. (Tracy laughing) I don't know if I would tell myself anything different because honestly, if I did, I wouldn't be where I am. >> Lisa: Good for you. >> I started out in fine arts, and I didn't end up there. >> That's good. >> Such a good point, yeah. >> We've been talking about that and I find that a lot at events like WiDS, is women have these zigzaggy patterns. I studied biology, I have a master's in molecular biology, I'm in media and marketing. We talked about transportable skills. There's a case I made many years ago when I got into tech about, well in science you learn the art of interpreting esoteric data and creating a story from it. And that's a transportable skill. But I always say, you mentioned failure, I always say failure is not a bad F word. It allows us to kind of zig and zag and learn along the way. And I think that really fosters thought diversity. And in data science, that is one of the things we absolutely need to have is that diversity and thought. You know, we talk about AI models being biased, we need the data and we need the diverse brains to help ensure that the biases are identified, extracted, and removed. Speaking of AI, I've been geeking out with ChatGPT. So, I'm on it yesterday and I ask it, "What's hot in data science?" And I was like, is it going to get that? What's hot? And it did it, it came back with trends. I think if I ask anything, "What's hot?", I should be to Paris Hilton, but I didn't. And so I was geeking out. One of the things I learned recently that I thought was so super cool is the CTO of OpenAI is a woman, Mira Murati, which I didn't know until over the weekend. Because I always think if I had to name top females in tech, who would they be? And I always default to Sheryl Sandberg, Carly Fiorina, Susan Wojcicki running YouTube. Who are some of the people in your history, in your current, that are really inspiring to you? Men, women, indifferent. >> Sure. I think Boeing is one of the companies where you actually do see a lot of women in leadership roles. I think we're one of the top companies with a number of women executives, actually. Susan Doniz, who's our Chief Information Officer, I believe she's actually slotted to speak at a WiDS event come fall. >> Lisa: Cool. >> So that will be exciting. Susan's actually relatively newer to Boeing in some ways. A Boeing time skill is like three years is still kind of new. (laughs) But she's been around for a while and she's done a lot of inspiring things, I think, for women in the organization. She does a lot with Latino communities and things like that as well. For me personally, you know, when I started at Boeing Ahmad Yaghoobi was one of my mentors and my Technical Lead. He came from Iran during a lot of hard times in the 1980s. His brother actually wrote a memoir, (laughs) which is just a fun, interesting fact. >> Tracy: Oh my God! >> Lisa: Wow! >> And so, I kind of gravitate to people that I can learn from that's not in my sphere, that might make me uncomfortable. >> And you probably don't even think about how many people you're influencing along the way. >> No. >> We just keep going and learning from our mentors and probably lose sight of, "I wonder how many people actually admire me?" And I'm sure there are many that admire you, Rhonda, for what you've done, going from anthropology to archeology. You mentioned before we went live you were really interested in photography. Keep going and really gathering all that breadth 'cause it's only making you more inspiring to people like us. >> Exactly. >> We thank you so much for joining us on the program and sharing a little bit about you and what brought you to WiDS. Thank you so much, Rhonda. >> Yeah, thank you. >> Tracy: Thank you so much for being here. >> Lisa: Yeah. >> Alright. >> For our guests, and for Tracy Zhang, this is Lisa Martin live at Stanford University covering the eighth Annual Women In Data Science Conference. Stick around. Next guest will be here in just a second. (gentle music)

>> Hello, everyone. I'm Khalil Ahmad, Senior Director, Architecture at S&P Global. I have been working with S&P Global for six years now. Previously, I worked for Citigroup and Prudential. Overall, I have been part of IT industry for 30 years, and most of my professional career has been within financial sector in New York City metro area. I live in New Jersey with my wife and son, Daniel Khalil. I have a Master degree in software engineering from the University of Scranton, and Master in mathematics University of Punjab, Lahore. And currently I am pursuing TRIUM global Executive MBA. A joint program from the NYU Stern, LSE and HEC Paris. So today, I'm going to talk about building multi-cluster scalable container platform, supporting on-prem hybrid and multicloud use cases, how we leverage that with an S&P Global and what was our best story. As far as the agenda is concerned, I will go over, quickly the problem statement. Then I will mention the work of our core requirements, how we get solutioning, how Docker Enterprise helped us. And at the end, I will go over the pilot deployment for a proof of concept which we leverage. So, as far as the problem statement is concerned. Containers, as you all know, in the enterprise are becoming mainstream but expertise remains limited and challenges are mounting as containers enter production. Some companies are building skills internally and someone looking for partners that can help catalyze success, and choosing more integrated solutions that accelerate deployments and simplify the container environment. To overcome the challenges, we at S&P Global started our journey a few years back, taking advantage of both options. So, first of all, we met with all the stakeholder, application team, Product Manager and we define our core requirements. What we want out of this container platform, which supports multicloud and hybrid supporting on-prem as well. So, as you see my core requirements, we decided that we need first of all a roadmap or container strategy, providing guidelines on standards and specification. Secondly, with an S&P Global, we decided to introduce Platform as a Service approach, where we bring the container platform and provide that as a service internally to our all application team and all the Product Managers. Hosting multiple application on-prem as well as in multicloud. Third requirement was that we need Linux and Windows container support. In addition to that, we would also require hosted secure image registry with role based access control and image security scanning. In addition to that, we also started DevOps journey, so we want to have a full support of CI/CD pipeline. Whatever the solution we recommend from the architecture group, it should be easily integrated to the developer workstation. And developer workstation could be Windows, Mac or Linux. Orchestration, performance and control were few other parameter which we'll want to keep in mind. And the most important, dynamic scaling of container clusters. That was something we were also want to achieve, when we introduce this Platform as a Service. So, as far as the standard specification are concerned, we turn to the Open Container Initiative, the OCI. OCI was established in June 2015 by Docker and other leaders in the technology industry. And OCI operates under Linux Foundation, and currently contains two specification, runtime specification and image specification. So, at that time, it was a no brainer, other than to just stick with OCI. So, we are following the industry standard and specifications. Now the next step was, okay, the container platform. But what would be our runtime engine? What would be orchestration? And how we support, in our on-prem as well as in the multicloud infrastructure? So, when it comes to runtime engine, we decided to go with the Docker. Which is by default, runtime engine and Kubernetes. And if I may mention, DataDog in one of their public report, they say Docker is probably the most talked about infrastructure technology for the past few years. So, sticking to Docker runtime engine was another win-win game and we saw in future not bringing any challenge or issues. When it comes to orchestration. We prefer Kubernetes but that time there was a challenge, Kubernetes did not support Windows container. So, we wanted something which worked with a Linux container, and also has the ability or to orchestrate Windows containers. So, even though long term we want to stick to Kubernetes, but we also wanted to have a Docker swarm. When it comes to on-prem and multicloud, technically you could only support as of now, technology may change in future, but as of now, you can only support if you bring your own orchestration too. So, in our case, if we have control over orchestration control and not locked in with one cloud provider, that was the ideal situation. So, with all that, research, R&D and finding, we found Docker Enterprise. Which is securely built, share and run modern applications anywhere. So, when we come across Docker Enterprise, we were pleased to see that it meets our most of the core requirements. Whether it is coming on the developer machine, to integrating their workstation, building the application. Whether it comes to sharing those application, in a secure way and collaborating with our pipeline. And the lastly, when it comes to the running. If we run in hybrid or multicloud or edge, in Kubernetes, Docker Enterprise have the support all the way. So, three area one I just call up all the Docker Enterprise, choice, flexibility and security. I'm sure there's a lot more features in Docker Enterprise as a suite. But, when we looked at these three words very quickly, simplified hybrid orchestration. Define application centric policies and boundaries. Once you define, you're all set. Then you just maintain those policies. Manage diverse application across mixed infrastructure, with secure segmentation. Then it comes to secure software supply chain. Provenance across the entire lifecycle of apps and infrastructure through enforceable policy. Consistently manage all apps and infrastructure. And lastly, when it comes to infrastructure independence. It was easily forever lift and shift, because same time, our cloud journey was in the flight. We were moving from on-prem to the cloud. So, support for lift and shift application was one of our wishlist. And Docker Enterprise did not disappoint us. It also supported both traditional and micro services apps on any infrastructure. So, here we are, Docker Enterprise. Why Docker Enterprise? Some of the items in previous slides I mentioned. But in addition to those industry-leading platform, simplifying the IT operations, for running modern application at scale, anywhere. Docker Enterprise also has developer tools. So, the integration, as I mentioned earlier was smooth. In addition to all these tools, the main two components, the Universal Control Plane and the Docker Trusted Registry, solve lot of our problems. When it comes to the orchestration, we have our own Universal Control Plane. Which under the hood, manages Kubernetes and Docker swarm both clusters. So, guess what? We have a Windows support, through Docker swarm and we have a Linux support through Kubernetes. Now that paradigm has changed, as of today, Kubernetes support Windows container. So, guess what? We are well after the UCP, because we have our own orchestration tool, and we start managing Kubernetes cluster in Linux and introduce now, Windows as well. Then comes to the Docker Trusted Registry. Integrated Security and role based access control, made a very smooth transition from our RT storage to DTR. In addition to that, binary level scanning was another good feature from the security point of view. So that, these all options and our R&D landed the Docker Enterprise is the way to go. And if we go over the Docker Enterprise, we can spin up multiple clusters on-prem and in the cloud. And we have a one centralized location to manage those clusters. >> Khalil: So, with all that, now let's talk about how what was our pilot deployment, for proof of concept. In this diagram, you can see we, on the left side is our on-prem Data Center, on the right side is AWS, US East Coast. We picked up one region three zones. And on-prem, we picked up our Data Center, one of the Data Center in the United States of America, and we started the POC. So, our Universal Control Plane had a five nodes cluster. Docker Trusted Registry, also has a five node cluster. And the both, but in our on-prem Data Center. When it comes to the worker nodes, we have started with 18 node cluster, on the Linux side and the four node cluster on the Windows side. Because the major footprint which we have was on the Linux side, and the Windows use cases were pretty small. Also, this is just a proof of concept. And in AWS, we mimic the same web worker nodes, virtual to what we have on-prem. We have a 13 nodes cluster on Linux. And we started with four node cluster of Windows container. And having the direct connect from our Data Center to AWS, which was previously existing, so we did not have any connectivity or latency issue. Now, if you see in this diagram, you have a centralized, Universal Control Plane and your trusted registry. And we were able to spin up a cluster, on-prem as well as in the cloud. And we made this happen, end to end in record time. So later, when we deploy this in production, we also added another cloud provider. So, what you see the box on the right side, we just duplicate test that box in another cloud platform. So, now other orchestration tool, managing on-prem and multicloud clusters. Now, in your use case, you may find this little, you know, more in favor of on-prem. But that fit in our use case. Later, we did have expanded the cluster of Universal Control Plane and DTR in the cloud as well. And the clusters have gone and hundreds and thousands of worker nodes span over two cloud providers, third being discussed. And this solution has been working so far, very good. We did not see any downtime, not a single instance. And we were able to provide multicloud platform, container Platform as a Service for our S&P Global. Thank you for your time. If any questions, I have put my LinkedIn and Twitter account holder, you're welcome to ask any question

>>from the Cube Studios >>in Palo Alto and >>Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hey, welcome back here. Ready? Jeff Frick here with the Cube. We're doing a special presentation today really talking about AI and making ai really with two companies that are right in the heart of the Dell EMC as well as Intel. So we're excited to have a couple Cube alumni back on the program. Haven't seen him in a little while. First off from Intel. Lisa Spelman. She is the corporate VP and GM for the Xeon Group in Jersey on and Memory Group. Great to see you, Lisa. >>Good to see you again, too. >>And we've got Ravi Pinter. Conte. He is the SBP server product management, also from Dell Technologies. Ravi, great to see you as well. >>Good to see you on beast. Of course, >>yes. So let's jump into it. So, yesterday, Robbie, you guys announced a bunch of new kind of ai based solutions where if you can take us through that >>Absolutely so one of the things we did Jeff was we said it's not good enough for us to have a point product. But we talked about hope, the tour of products, more importantly, everything from our workstation side to the server to these storage elements and things that we're doing with VM Ware, for example. Beyond that, we're also obviously pleased with everything we're doing on bringing the right set off validated configurations and reference architectures and ready solutions so that the customer really doesn't have to go ahead and do the due diligence. Are figuring out how the various integration points are coming for us in making a solution possible. Obviously, all this is based on the great partnership we have with Intel on using not just their, you know, super cues, but FPG's as well. >>That's great. So, Lisa, I wonder, you know, I think a lot of people you know, obviously everybody knows Intel for your CPU is, but I don't think they recognize kind of all the other stuff that can wrap around the core CPU to add value around a particular solution. Set or problems. That's what If you could tell us a little bit more about Z on family and what you guys are doing in the data center with this kind of new interesting thing called AI and machine learning. >>Yeah. Um, so thanks, Jeff and Ravi. It's, um, amazing. The way to see that artificial intelligence applications are just growing in their pervasiveness. And you see it taking it out across all sorts of industries. And it's actually being built into just about every application that is coming down the pipe. And so if you think about meeting toe, have your hardware foundation able to support that. That's where we're seeing a lot of the customer interest come in. And not just a first Xeon, but, like Robbie said on the whole portfolio and how the system and solution configuration come together. So we're approaching it from a total view of being able to move all that data, store all of that data and cross us all of that data and providing options along that entire pipeline that move, um, and within that on Z on. Specifically, we've really set that as our cornerstone foundation for AI. If it's the most deployed solution and data center CPU around the world and every single application is going to have artificial intelligence in it, it makes sense that you would have artificial intelligence acceleration built into the actual hardware so that customers get a better experience right out of the box, regardless of which industry they're in or which specialized function they might be focusing on. >>It's really it's really wild, right? Cause in process, right, you always move through your next point of failure. So, you know, having all these kind of accelerants and the ways that you can carve off parts of the workload part of the intelligence that you can optimize betters is so important as you said Lisa and also Rocket and the solution side. Nobody wants General Ai just for ai sake. It's a nice word. Interesting science experiment. But it's really in the applied. A world is. We're starting to see the value in the application of this stuff, and I wonder you have a customer. You want to highlight Absalon, tell us a little bit about their journey and what you guys did with them. >>Great, sure. I mean, if you didn't start looking at Epsilon there in the market in the marketing business, and one of the crucial things for them is to ensure that they're able to provide the right data. Based on that analysis, there run on? What is it that the customer is looking for? And they can't wait for a period of time, but they need to be doing that in the near real time basis, and that's what excellent does. And what really blew my mind was the fact that they actually service are send out close to 100 billion messages. Again, it's 100 billion messages a year. And so you can imagine the amount of data that they're analyzing, which is in petabytes of data, and they need to do real time. And that's all possible because of the kind of analytics we have driven into the power It silver's, you know, using the latest of the Intel Intel Xeon processor couple with some of the technologies from the BGS side, which again I love them to go back in and analyze this data and service to the customers very rapidly. >>You know, it's funny. I think Mark Tech is kind of an under appreciated ah world of ai and, you know, in machine to machine execution, right, That's the amount of transactions go through when you load a webpage on your site that actually ideas who you are you know, puts puts a marketplace together, sells time on that or a spot on that ad and then lets people in is a really sophisticated, as you said in massive amounts of data going through the interesting stuff. If it's done right, it's magic. And if it's done, not right, then people get pissed off. You gotta have. You gotta have use our tools. >>You got it. I mean, this is where I talked about, you know, it can be garbage in garbage out if you don't really act on the right data. Right. So that is where I think it becomes important. But also, if you don't do it in a timely fashion, but you don't service up the right content at the right time. You miss the opportunity to go ahead and grab attention, >>right? Right. Lisa kind of back to you. Um, you know, there's all kinds of open source stuff that's happening also in the in the AI and machine learning world. So we hear things about tense or flow and and all these different libraries. How are you guys, you know, kind of embracing that world as you look at ai and kind of the development. We've been at it for a while. You guys are involved in everything from autonomous vehicles to the Mar Tech. Is we discussed? How are you making sure that these things were using all the available resources to optimize the solutions? >>Yeah, I think you and Robbie we're just hitting on some of those examples of how many ways people have figured out how to apply AI now. So maybe at first it was really driven by just image recognition and image tagging. But now you see so much work being driven in recommendation engines and an object detection for much more industrial use cases, not just consumer enjoyment and also those things you mentioned and hit on where the personalization is a really fine line you walk between. How do you make an experience feel good? Personalized versus creepy personalized is a real challenge and opportunity across so many industries. And so open source like you mentioned, is a great place for that foundation because it gives people the tools to build upon. And I think our strategy is really a stack strategy that starts first with delivering the best hardware for artificial intelligence and again the other is the foundation for that. But we also have, you know, Milat type processing for out of the Edge. And then we have all the way through to very custom specific accelerators into the data center, then on top about the optimized software, which is going into each of those frameworks and doing the work so that the framework recognizes the specific acceleration we built into the CPU. Whether that steel boost or recognizes the capabilities that sit in that accelerator silicon, and then once we've done that software layer and this is where we have the opportunity for a lot of partnership is the ecosystem and the solutions work that Robbie started off by talking about. So Ai isn't, um, it's not easy for everyone. It has a lot of value, but it takes work to extract that value. And so partnerships within the ecosystem to make sure that I see these are taking those optimization is building them in and fundamentally can deliver to customers. Reliable solution is the last leg of that of that strategy, but it really is one of the most important because without it you get a lot of really good benchmark results but not a lot of good, happy customer, >>right? I'm just curious, Lee says, because you kind of sit in the catbird seat. You guys at the core, you know, kind of under all the layers running data centers run these workloads. How >>do you see >>kind of the evolution of machine learning and ai from kind of the early days, where with science projects and and really smart people on mahogany row versus now people are talking about trying to get it to, like a citizen developer, but really a citizen data science and, you know, in exposing in the power of AI to business leaders or business executioners. Analysts, if you will, so they can apply it to their day to day world in their day to day life. How do you see that kind of evolving? Because you not only in it early, but you get to see some of the stuff coming down the road in design, find wins and reference architectures. How should people think about this evolution? >>It really is one of those things where if you step back from the fundamentals of AI, they've actually been around for 50 or more years. It's just that the changes in the amount of computing capability that's available, the network capacity that's available and the fundamental efficiency that I t and infrastructure managers and get out of their cloud architectures as allowed for this pervasiveness to evolve. And I think that's been the big tipping point that pushed people over this fear. Of course, I went through the same thing that cloud did where you had maybe every business leader or CEO saying Hey, get me a cloud and I'll figure out what for later give me some AI will get a week and make it work, But we're through those initial use pieces and starting to see a business value derived from from those deployments. And I think some of the most exciting areas are in the medical services field and just the amount, especially if you think of the environment we're in right now. The amount of efficiency and in some cases, reduction in human contact that you could require for diagnostics and just customer tracking and ability, ability to follow their entire patient History is really powerful and represents the next wave and care and how we scale our limited resource of doctors nurses technician. And the point we're making of what's coming next is where you start to see even more mass personalization and recommendations in that way that feel very not spooky to people but actually comforting. And they take value from them because it allows them to immediately act. Robbie reference to the speed at which you have to utilize the data. When people get immediately act more efficiently. They're generally happier with the service. So we see so much opportunity and we're continuing to address across, you know, again that hardware, software and solution stack so we can stay a step ahead of our customers, >>Right? That's great, Ravi. I want to give you the final word because you guys have to put the solutions together, it actually delivering to the customer. So not only, you know the hardware and the software, but any other kind of ecosystem components that you have to bring together. So I wonder if you can talk about that approach and how you know it's it's really the solution. At the end of the day, not specs, not speeds and feeds. That's not really what people care about. It's really a good solution. >>Yeah, three like Jeff, because end of the day I mean, it's like this. Most of us probably use the A team to retry money, but we really don't know what really sits behind 80 and my point being that you really care at that particular point in time to be able to put a radio do machine and get your dollar bills out, for example. Likewise, when you start looking at what the customer really needs to know, what Lisa hit upon is actually right. I mean what they're looking for. And you said this on the whole solution side house. To our our mantra to this is very simple. We want to make sure that we use the right basic building blocks, ensuring that we bring the right solutions using three things the right products which essentially means that we need to use the right partners to get the right processes in GPU Xen. But then >>we get >>to the next level by ensuring that we can actually do things we can either provide no ready solutions are validated reference architectures being that you have the sausage making process that you now don't need to have the customer go through, right? In a way. We have done the cooking and we provide a recipe book and you just go through the ingredient process of peering does and then off your off right to go get your solution done. And finally, the final stages there might be helped that customers still need in terms of services. That's something else Dell technology provides. And the whole idea is that customers want to go out and have them help deploying the solutions. We can also do that we're services. So that's probably the way we approach our data. The way we approach, you know, providing the building blocks are using the right technologies from our partners, then making sure that we have the right solutions that our customers can look at. And finally, they need deployment. Help weaken due their services. >>Well, Robbie, Lisa, thanks for taking a few minutes. That was a great tee up, Rob, because I think we're gonna go to a customer a couple of customer interviews enjoying that nice meal that you prepared with that combination of hardware, software, services and support. So thank you for your time and a great to catch up. All right, let's go and run the tape. Hi, Jeff. I wanted to talk about two examples of collaboration that we have with the partners that have yielded Ah, really examples of ah put through HPC and AI activities. So the first example that I wanted to cover is within your AHMAD team up in Canada with that team. We collaborated with Intel on a tuning of algorithm and code in order to accelerate the mapping of the human brain. So we have a cluster down here in Texas called Zenith based on Z on and obtain memory on. And we were able to that customer with the three of us are friends and Intel the norm, our team on the Dell HPC on data innovation, injuring team to go and accelerate the mapping of the human brain. So imagine patients playing video games or doing all sorts of activities that help understand how the brain sends the signal in order to trigger a response of the nervous system. And it's not only good, good way to map the human brain, but think about what you can get with that type of information in order to help cure Alzheimer's or dementia down the road. So this is really something I'm passionate about. Is using technology to help all of us on all of those that are suffering from those really tough diseases? Yeah, yeah, way >>boil. I'm a project manager for the project, and the idea is actually to scan six participants really intensively in both the memory scanner and the G scanner and see if we can use human brain data to get closer to something called Generalized Intelligence. What we have in the AI world, the systems that are mathematically computational, built often they do one task really, really well, but they struggle with other tasks. Really good example. This is video games. Artificial neural nets can often outperform humans and video games, but they don't really play in a natural way. Artificial neural net. Playing Mario Brothers The way that it beats the system is by actually kind of gliding its way through as quickly as possible. And it doesn't like collect pennies. For example, if you play Mary Brothers as a child, you know that collecting those coins is part of your game. And so the idea is to get artificial neural nets to behave more like humans. So like we have Transfer of knowledge is just something that humans do really, really well and very naturally. It doesn't take 50,000 examples for a child to know the difference between a dog and a hot dog when you eat when you play with. But an artificial neural net can often take massive computational power and many examples before it understands >>that video games are awesome, because when you do video game, you're doing a vision task instant. You're also doing a >>lot of planning and strategy thinking, but >>you're also taking decisions you several times a second, and we record that we try to see. Can we from brain activity predict >>what people were doing? We can break almost 90% accuracy with this type of architecture. >>Yeah, yeah, >>Use I was the lead posts. Talk on this collaboration with Dell and Intel. She's trying to work on a model called Graph Convolution Neural nets. >>We have being involved like two computing systems to compare it, like how the performance >>was voting for The lab relies on both servers that we have internally here, so I have a GPU server, but what we really rely on is compute Canada and Compute Canada is just not powerful enough to be able to run the models that he was trying to run so it would take her days. Weeks it would crash, would have to wait in line. Dell was visiting, and I was invited into the meeting very kindly, and they >>told us that they started working with a new >>type of hardware to train our neural nets. >>Dell's using traditional CPU use, pairing it with a new >>type off memory developed by Intel. Which thing? They also >>their new CPU architectures and really optimized to do deep learning. So all of that sounds great because we had this problem. We run out of memory, >>the innovation lab having access to experts to help answer questions immediately. That's not something to gate. >>We were able to train the attic snatch within 20 minutes. But before we do the same thing, all the GPU we need to wait almost three hours to each one simple way we >>were able to train the short original neural net. Dell has been really great cause anytime we need more memory, we send an email, Dell says. Yeah, sure, no problem. We'll extended how much memory do you need? It's been really simple from our end, and I think it's really great to be at the edge of science and technology. We're not just doing the same old. We're pushing the boundaries. Like often. We don't know where we're going to be in six months. In the big data world computing power makes a big difference. >>Yeah, yeah, yeah, yeah. The second example I'd like to cover is the one that will call the data accelerator. That's a publisher that we have with the University of Cambridge, England. There we partnered with Intel on Cambridge, and we built up at the time the number one Io 500 storage solution on. And it's pretty amazing because it was built on standard building blocks, power edge servers until Xeon processors some envy me drives from our partners and Intel. And what we did is we. Both of this system with a very, very smart and elaborate suffering code that gives an ultra fast performance for our customers, are looking for a front and fast scratch to their HPC storage solutions. We're also very mindful that this innovation is great for others to leverage, so the suffering Could will soon be available on Get Hub on. And, as I said, this was number one on the Iot 500 was initially released >>within Cambridge with always out of focus on opening up our technologies to UK industry, where we can encourage UK companies to take advantage of advanced research computing technologies way have many customers in the fields of automotive gas life sciences find our systems really help them accelerate their product development process. Manage Poor Khalidiya. I'm the director of research computing at Cambridge University. Yeah, we are a research computing cloud provider, but the emphasis is on the consulting on the processes around how to exploit that technology rather than the better results. Our value is in how we help businesses use advanced computing resources rather than the provision. Those results we see increasingly more and more data being produced across a wide range of verticals, life sciences, astronomy, manufacturing. So the data accelerators that was created as a component within our data center compute environment. Data processing is becoming more and more central element within research computing. We're getting very large data sets, traditional spinning disk file systems can't keep up and we find applications being slowed down due to a lack of data, So the data accelerator was born to take advantage of new solid state storage devices. I tried to work out how we can have a a staging mechanism for keeping your data on spinning disk when it's not required pre staging it on fast envy any stories? Devices so that can feed the applications at the rate quiet for maximum performance. So we have the highest AI capability available anywhere in the UK, where we match II compute performance Very high stories performance Because for AI, high performance storage is a key element to get the performance up. Currently, the data accelerated is the fastest HPC storage system in the world way are able to obtain 500 gigabytes a second read write with AI ops up in the 20 million range. We provide advanced computing technologies allow some of the brightest minds in the world really pushed scientific and medical research. We enable some of the greatest academics in the world to make tomorrow's discoveries. Yeah, yeah, yeah. >>Alright, Welcome back, Jeff Frick here and we're excited for this next segment. We're joined by Jeremy Raider. He is the GM digital transformation and scale solutions for Intel Corporation. Jeremy, great to see you. Hey, thanks for having me. I love I love the flowers in the backyard. I thought maybe you ran over to the Japanese, the Japanese garden or the Rose Garden, Right To very beautiful places to visit in Portland. >>Yeah. You know, you only get him for a couple. Ah, couple weeks here, so we get the timing just right. >>Excellent. All right, so let's jump into it. Really? And in this conversation really is all about making Ai Riel. Um, and you guys are working with Dell and you're working with not only Dell, right? There's the hardware and software, but a lot of these smaller a solution provider. So what is some of the key attributes that that needs to make ai riel for your customers out there? >>Yeah, so, you know, it's a it's a complex space. So when you can bring the best of the intel portfolio, which is which is expanding a lot, you know, it's not just the few anymore you're getting into Memory technologies, network technologies and kind of a little less known as how many resources we have focused on the software side of things optimizing frameworks and optimizing, and in these key ingredients and libraries that you can stitch into that portfolio to really get more performance in value, out of your machine learning and deep learning space. And so you know what we've really done here with Dell? It has started to bring a bunch of that portfolio together with Dell's capabilities, and then bring in that ai's V partner, that software vendor where we can really take and stitch and bring the most value out of that broad portfolio, ultimately using using the complexity of what it takes to deploy an AI capability. So a lot going on. They're bringing kind of the three legged stool of the software vendor hardware vendor dental into the mix, and you get a really strong outcome, >>right? So before we get to the solutions piece, let's stick a little bit into the Intel world. And I don't know if a lot of people are aware that obviously you guys make CPUs and you've been making great CPIs forever. But there's a whole lot more stuff that you've added, you know, kind of around the core CPU. If you will in terms of of actual libraries and ways to really optimize the seond processors to operate in an AI world. I wonder if you can kind of take us a little bit below the surface on how that works. What are some of the examples of things you can do to get more from your Gambira Intel processors for ai specific applications of workloads? >>Yeah, well, you know, there's a ton of software optimization that goes into this. You know that having the great CPU is definitely step one. But ultimately you want to get down into the libraries like tensor flow. We have data analytics, acceleration libraries. You know, that really allows you to get kind of again under the covers a little bit and look at it. How do we have to get the most out of the kinds of capabilities that are ultimately used in machine learning in deep learning capabilities, and then bring that forward and trying and enable that with our software vendors so that they can take advantage of those acceleration components and ultimately, you know, move from, you know, less training time or could be a the cost factor. But those are the kind of capabilities we want to expose to software vendors do these kinds of partnerships. >>Okay. Ah, and that's terrific. And I do think that's a big part of the story that a lot of people are probably not as aware of that. There are a lot of these optimization opportunities that you guys have been leveraging for a while. So shifting gears a little bit, right? AI and machine learning is all about the data. And in doing a little research for this, I found actually you on stage talking about some company that had, like, 350 of road off, 315 petabytes of data, 140,000 sources of those data. And I think probably not great quote of six months access time to get that's right and actually work with it. And the company you're referencing was intel. So you guys know a lot about debt data, managing data, everything from your manufacturing, and obviously supporting a global organization for I t and run and ah, a lot of complexity and secrets and good stuff. So you know what have you guys leveraged as intel in the way you work with data and getting a good data pipeline. That's enabling you to kind of put that into these other solutions that you're providing to the customers, >>right? Well, it is, You know, it's absolutely a journey, and it doesn't happen overnight, and that's what we've you know. We've seen it at Intel on We see it with many of our customers that are on the same journey that we've been on. And so you know, this idea of building that pipeline it really starts with what kind of problems that you're trying to solve. What are the big issues that are holding you back that company where you see that competitive advantage that you're trying to get to? And then ultimately, how do you build the structure to enable the right kind of pipeline of that data? Because that's that's what machine learning and deep learning is that data journey. So really a lot of focus around you know how we can understand those business challenges bring forward those kinds of capabilities along the way through to where we structure our entire company around those assets and then ultimately some of the partnerships that we're gonna be talking about these companies that are out there to help us really squeeze the most out of that data as quickly as possible because otherwise it goes stale real fast, sits on the shelf and you're not getting that value out of right. So, yeah, we've been on the journey. It's Ah, it's a long journey, but ultimately we could take a lot of those those kind of learnings and we can apply them to our silicon technology. The software optimization is that we're doing and ultimately, how we talk to our enterprise customers about how they can solve overcome some of the same challenges that we did. >>Well, let's talk about some of those challenges specifically because, you know, I think part of the the challenge is that kind of knocked big data, if you will in Hadoop, if you will kind of off the rails. Little bit was there's a whole lot that goes into it. Besides just doing the analysis, there's a lot of data practice data collection, data organization, a whole bunch of things that have to happen before. You can actually start to do the sexy stuff of AI. So you know, what are some of those challenges. How are you helping people get over kind of these baby steps before they can really get into the deep end of the pool? >>Yeah, well, you know, one is you have to have the resource is so you know, do you even have the resource is if you can acquire those Resource is can you keep them interested in the kind of work that you're doing? So that's a big challenge on and actually will talk about how that fits into some of the partnerships that we've been establishing in the ecosystem. It's also you get stuck in this poc do loop, right? You finally get those resource is and they start to get access to that data that we talked about. It start to play out some scenarios, a theorize a little bit. Maybe they show you some really interesting value, but it never seems to make its way into a full production mode. And I think that is a challenge that has faced so many enterprises that are stuck in that loop. And so that's where we look at who's out there in the ecosystem that can help more readily move through that whole process of the evaluation that proved the r a y, the POC and ultimately move that thing that capability into production mode as quickly as possible that you know that to me is one of those fundamental aspects of if you're stuck in the POC. Nothing's happening from this. This is not helping your company. We want to move things more quickly, >>right? Right. And let's just talk about some of these companies that you guys are working with that you've got some reference architectures is data robot a Grid dynamics H 20 just down the road in Antigua. So a lot of the companies we've worked with with Cube and I think you know another part that's interesting. It again we can learn from kind of old days of big data is kind of generalized. Ai versus solution specific. Ai and I think you know where there's a real opportunity is not AI for a sake, but really it's got to be applied to a specific solution, a specific problem so that you have, you know, better chatbots, better customer service experience, you know, better something. So when you were working with these folks and trying to design solutions or some of the opportunities that you saw to work with some of these folks to now have an applied a application slash solution versus just kind of AI for ai's sake. >>Yeah. I mean, that could be anything from fraud, detection and financial services, or even taking a step back and looking more horizontally like back to that data challenge. If if you're stuck at the AI built a fantastic Data lake, but I haven't been able to pull anything back out of it, who are some of the companies that are out there that can help overcome some of those big data challenges and ultimately get you to where you know, you don't have a data scientist spending 60% of their time on data acquisition pre processing? That's not where we want them, right? We want them on building out that next theory. We want them on looking at the next business challenge. We want them on selecting the right models, but ultimately they have to do that as quickly as possible so that they can move that that capability forward into the next phase. So, really, it's about that that connection of looking at those those problems or challenges in the whole pipeline. And these companies like data robot in H 20 quasi. Oh, they're all addressing specific challenges in the end to end. That's why they've kind of bubbled up as ones that we want to continue to collaborate with, because it can help enterprises overcome those issues more fast. You know more readily. >>Great. Well, Jeremy, thanks for taking a few minutes and giving us the Intel side of the story. Um, it's a great company has been around forever. I worked there many, many moons ago. That's Ah, that's a story for another time, but really appreciate it and I'll interview you will go there. Alright, so super. Thanks a lot. So he's Jeremy. I'm Jeff Frick. So now it's time to go ahead and jump into the crowd chat. It's crowdchat dot net slash make ai real. Um, we'll see you in the chat. And thanks for watching

>> from Bahrain. It's the Q recovery AWS Public sector Bahrain brought to you by Amazon Web service is >> Welcome back. Everyone's Cube coverage here in my reign Middle East, part of A W s Amazon Web services Summit, sir. Second year covering the evolution and the Revolution to cloud computing. This year, the big news is a Davis has a region spurring innovation and entrepreneurship in the Middle East region or next gases. Ahmad Hamadan, CEO and CO. Front of Uniform Nick, a super hot company. Congratulations on your success. Welcome to the Cube. Thank you, Joe. So we just talked before we came on camera. This ap I economy that we've been covering in death you been living? This is your world. You have a really big business. Not a lot of employees. Less than 200 employees, billions of transactions. Ap I transactions. This is This is the successful man we've been seeing in the public companies truly, among others. Messaging application integration. This is cloud now, right? This is happening. What's your story? >> Okay, so >> you know, these times I would say it's a golden age for the technology in the region on for the cloud specifically when we started back in 2006. Uh, you know, we're very lonely. We're we're not even in that time in the club. So we started just tow, solve the issue off bulk messaging through writing a script or software that allow us to broadcast a message to a group of people. But along the journey, we realized that businesses need tools, especially a B I's that will allow them tow, tow each toe a wider, I would say audience with a seamless integration. And this is how the cloud communication industry emerged. So we avoid our baby eyes for businesses from all around TV region, especially with segments or sectors that have a mass communication need, like banks in government retailers on the E businesses, >> the data is the data in this business is a fascinating. Before we get into some of those questions about the origination story, how did it all start? >> Okay, so I wasn't at the university a teenage off 22 probably on I leave the one of the student clubs. Andi. I wanted to communicate a message Tau 400 people on, you know, the limitation of the mobile. Back then, I couldn't do it. It's terrible experience. You cannot send two more than 10 people. The text is not full. You know all these complications. So being a software engineer on Dhe, you know, I had an idea. There should be a solution that you can write code, publish it online, and then it will do the magic for you. For months later, I apartment with my brother was a software, you know, geek more than I on dhe. You know, already >> older or younger, brother. >> Younger brother. Okay. Yeah. Hey, was at high school on then four months later, we're life sending thousands of messages over the Internet. It was like magic friends and family like it. It's really making money on, you know, for us, you know, You know, it's like when you have 4005 thousand's a big money for us. That way, any each month, Andre, Like moving forward 2008. I decided this is the dream we need to scale this and, ah, venture out of this small, you know, experiment on. Then I left the job and dedicated my time to scale that business. And I moved the business toward the business and the cloud and communication. Our first move to the cloud was, Ah, 2010. We used aws toe move most of our infrastructure to the cloud on By 2013 we completely divert it into the cloud communication business where the focus is into the FBI. The integration with the applications at the customer systems on Ben allow them tow, communicate to, you know, 100 of millions off >> and then mobile phones, obviously GPS built in application. Tsunamis happened. Exactly. People want to interface with the companies. The other phone? >> Exactly. I will give you an example. You know, you come to my mind while you're talking. We used to have customers back in 2010 descend on Lee along the year like maximum one million transaction the same customers nowadays, like nine years later, they send at least 200 million transactions, so you can imagine the growth in the use cases on the adoption from the customers. Use it now for engagement for notification, for awareness for security and authentication for personalized marketing content, like hundreds of fuse cases like we do some analysis in the behavior of the customers and the consumer on. We realised that in a modern society on individual interact digitally with at least 50 grands and a day. This is huge. You can do the math if you multiply this by 100 million population than there is a massively huge number of transaction and data's being >> percent. What are you guys doing now? Is mainly targeted application developers or businesses as a turnkey solution? What's the What's the value proposition? >> So, >> UH, >> two years like nature, we realized that we cannot target or the market and serve, or the customers we need to focus into the sick man that has hypertension. Then we identified five segment where we tell her our solution, our value proposition toward those segments on it's aligned with the trends in the region. Maybe it's not applicable to other regions. Eso number one for us is the online banking segment. I would see the financial industry with all the, you know, evolution off the authentic and the online and mobile banking. So those are number one. We do integrate our system with their current systems out off the shelf. We don't do much of a cast immunization. We usually provide really integral components toe toward their system, and then they hook up their system two hours, and then they have the dashboard and blood form to orchestrate the communication. The number one is the M government. It's also a, you know, an industry that is evolving in the region. The number three for us is the businesses, and they're very hot, very high potential growth. I would say the number one in terms of growth a business include the e commerce on demand delivery, the food delivery applications you name it on then. The fourth industry for us is the retailer who are moving now toward the reality and the engagement. More to them prison share themselves in this stuff word for them. And the last one is the I would say the hospitality and the, you know, the, you know, hotels and, you know, travel agents. >> I think anyone building an app would want this of their mobile. So what's what's your take of the ecosystem? Entrepreneurship now much different in one year. You have an Amazon region here. What do you think's gonna happen? It's gonna be like you and your brother all over again with other entrepreneur. >> Exactly. You know, when I you know, see, photo interpreters usually approach me for, you know, kind of mentorship and coaching. You know, we're at the stage little bit, you know, being fruit difficult. You know, >> the situation's got the scar >> tissue. Yeah, So I usually told them guys, it's like being so easy for you. You know, at this time, I know that with all the luck, I would say support the barrier to entry had become much less. But at least there are many things you don't need to think how you figure out. It's already there. Just need to have the badge and dedication, and then you'll find many people to support you. Especially, I would say there is only one areas not yet will, you know, covered in the region, which is the access to the talents. I think this is a worldwide problem, even for Forks in the Silicon Valley. But in terms of funding thes of doing business, sitting up ventures, access to the technology platforms like the cloud infrastructure in terms, off advice, mentorship and coaching there is, I would say, an abundant off that available today for for interpreters. And I can tell the next five years you will see a huge value being created out of this. >> Yeah, instead of riding, waves will be running s curves. So it's easier now, Still hard to build a company. But you're right. I mean, go back 10 years ago. You to put it all together, >> Takes us six months to set up the company. You know, legally, back in 2006 >> to get the infrastructure legally, get servers, get some funding, prototype it, get it launched its customers. Now they have a partner network. These kids are spoiled. >> But you know, it's difficult >> today to differentiate yourself because you will find tons of people are either doing or planning to do the same. >> They gotta build some smart intellectual property. This one machine learning is gonna be a great opportunity. That's gonna be a domain expertise kind of thing. You guys have a nice niche, and broad market is growing good. Calm, surround it. Got all kinds of systems out there that need this >> Exactly. You know, the question today is not if the tools and support is available or not. The question is, how you gonna use those tools to create something unique? >> I'm a great to See you. Thanks for coming on and sharing your experiences. You're an inspiration to the other entrepreneurs out there again. Remember Entrepreneurship like a family. Took a team, sport. Pay it forward. The other generations coming online. Absolutely. Congratulations on your success Cube coverage here by rain talking to start ups. This is going to be a hot market for entrepreneurship If the capital markets conform around it. The Cube is here covering it here and by rain. Stay with us for more at a debate summit. If this trip