(light upbeat music) >> Hey, everyone. Welcome back to theCUBE's day long coverage of Women in Data Science 2023. Live from Stanford University, I'm Lisa Martin. We've had some amazing conversations today with my wonderful co-host, as you've seen. Tracy Zhang joins me next for a very interesting and inspiring conversation. I know we've been bringing them to you, we're bringing you another one here. Dr. Irene Dankwa-Mullan joins us, the Chief Medical Officer at Marti Health, and a speaker at WIDS. Welcome, Irene, it's great to have you. >> Thank you. I'm delighted to be here. Thank you so much for this opportunity. >> So you have an MD and a Master of Public Health. Covid must have been an interesting time for you, with an MPH? >> Very much so. >> Yeah, talk a little bit about you, your background, and Marti Health? This is interesting. This is a brand new startup. This is a digital health equity startup. >> Yes, yes. So, I'll start with my story a little bit about myself. So I was actually born in Ghana. I finished high school there and came here for college. What would I say? After I finished my undergraduate, I went to medical school at Dartmouth and I always knew I wanted to go into public health as well as medicine. So my medical education was actually five years. I did the MPH and my medical degree, at the same time, I got my MPH from Yale School of Public Health. And after I finished, I trained in internal medicine, Johns Hopkins, and after that I went into public health. I am currently living in Maryland, so I'm in Bethesda, Maryland, and that's where I've been. And really enjoyed public health, community health, combining that aspect of sort of prevention and wellness and also working in making sure that we have community health clinics and safety net clinics. So a great experience there. I also had the privilege, after eight years in public health, I went to the National Institute of Health. >> Oh, wow. >> Where I basically worked in clinical research, basically on minority health and health disparities. So, I was in various leadership roles and helped to advance the science of health equity, working in collaboration with a lot of scientists and researchers at the NIH, really to advance the science. >> Where did your interest in health equity come from? Was there a defining moment when you were younger and you thought "There's a lot of inequities here, we have to do something about this." Where did that interest start? >> That's a great question. I think this influence was basically maybe from my upbringing as well as my family and also what I saw around me in Ghana, a lot of preventable diseases. I always say that my grandfather on my father's side was a great influence, inspired me and influenced my career because he was the only sibling, really, that went to school. And as a result, he was able to earn enough money and built, you know, a hospital. >> Oh wow. >> In their hometown. >> Oh my gosh! >> It started as a 20 bed hospital and now it's a 350 bed hospital. >> Oh, wow, that's amazing! >> In our hometown. And he knew that education was important and vital as well for wellbeing. And so he really inspired, you know, his work inspired me. And I remember in residency I went with a group of residents to this hospital in Ghana just to help over a summer break. So during a summer where we went and helped take care of the sick patients and actually learned, right? What it is like to care for so many patients and- >> Yeah. >> It was really a humbling experience. But that really inspired me. I think also being in this country. And when I came to the U.S. and really saw firsthand how patients are treated differently, based on their background or socioeconomic status. I did see firsthand, you know, that kind of unconscious bias. And, you know, drew me to the field of health disparities research and wanted to learn more and do more and contribute. >> Yeah. >> Yeah. So, I was curious. Just when did the data science aspect tap in? Like when did you decide that, okay, data science is going to be a problem solving tool to like all the problems you just said? >> Yeah, that's a good question. So while I was at the NIH, I spent eight years there, and precision medicine was launched at that time and there was a lot of heightened interest in big data and how big data could help really revolutionize medicine and healthcare. And I got the opportunity to go, you know, there was an opportunity where they were looking for physicians or deputy chief health officer at IBM. And so I went to IBM, Watson Health was being formed as a new business unit, and I was one of the first deputy chief health officers really to lead the data and the science evidence. And that's where I realized, you know, we could really, you know, the technology in healthcare, there's been a lot of data that I think we are not really using or optimizing to make sure that we're taking care of our patients. >> Yeah. >> And so that's how I got into data science and making sure that we are building technologies using the right data to advance health equity. >> Right, so talk a little bit about health equity? We mentioned you're with Marti Health. You've been there for a short time, but Marti Health is also quite new, just a few months old. Digital health equity, talk about what Marti's vision is, what its mission is to really help start dialing down a lot of the disparities that you talked about that you see every day? >> Yeah, so, I've been so privileged. I recently joined Marti Health as their Chief Medical Officer, Chief Health Officer. It's a startup that is actually trying to promote a value-based care, also promote patient-centered care for patients that are experiencing a social disadvantage as a result of their race, ethnicity. And were starting to look at and focused on patients that have sickle cell disease. >> Okay. >> Because we realize that that's a population, you know, we know sickle cell disease is a genetic disorder. It impacts a lot of patients that are from areas that are endemic malaria. >> Yeah. >> Yeah. >> And most of our patients here are African American, and when, you know, they suffer so much stigma and discrimination in the healthcare system and complications from their sickle cell disease. And so what we want to do that we feel like sickle cell is a litmus test for disparities. And we want to make sure that they get in patient-centered care. We want to make sure that we are leveraging data and the research that we've done in sickle cell disease, especially on the continent of Africa. >> Okay. >> And provide, promote better quality care for the patients. >> That's so inspiring. You know, we've heard so many great stories today. Were you able to watch the keynote this morning? >> Yes. >> I loved how it always inspires me. This conference is always, we were talking about this all day, how you walk in the Arrillaga Alumni Center here where this event is held every year, the vibe is powerful, it's positive, it's encouraging. >> Inspiring, yeah. >> Absolutely. >> Inspiring. >> Yeah, yeah. >> It's a movement, WIDS is a movement. They've created this community where you feel, I don't know, kind of superhuman. "Why can't I do this? Why not me?" We heard some great stories this morning about data science in terms of applications. You have a great application in terms of health equity. We heard about it in police violence. >> Yes. >> Which is an epidemic in this country for sure, as we know. This happens too often. How can we use data and data science as a facilitator of learning more about that, so that that can stop? I think that's so important for more people to understand all of the broad applications of data science, whether it's police violence or climate change or drug discovery or health inequities. >> Irene: Yeah. >> The potential, I think we're scratching the surface. But the potential is massive. >> Tracy: It is. >> And this is an event that really helps women and underrepresented minorities think, "Why not me? Why can't I get involved in that?" >> Yeah, and I always say we use data to make an make a lot of decisions. And especially in healthcare, we want to be careful about how we are using data because this is impacting the health and outcomes of our patients. And so science evidence is really critical, you know? We want to make sure that data is inclusive and we have quality data. >> Yes. >> And it's transparent. Our clinical trials, I always say are not always diverse and inclusive. And if that's going to form the evidence base or data points then we're doing more harm than good for our patients. And so data science, it's huge. I mean, we need a robust, responsible, trustworthy data science agenda. >> "Trust" you just brought up "trust." >> Yeah. >> I did. >> When we talk about data, we can't not talk about security and privacy and ethics but trust is table stakes. We have to be able to evaluate the data and trust in it. >> Exactly. >> And what it says and the story that can be told from it. So that trust factor is, I think, foundational to data science. >> We all see what happened with Covid, right? I mean, when the pandemic came out- >> Absolutely. >> Everyone wanted information. We wanted data, we wanted data we could trust. There was a lot of hesitancy even with the vaccine. >> Yeah. >> Right? And so public health, I mean, like you said, we had to do a lot of work making sure that the right information from the right data was being translated or conveyed to the communities. And so you are totally right. I mean, data and good information, relevant data is always key. >> Well- >> Is there any- Oh, sorry. >> Go ahead. >> Is there anything Marti Health is doing in like ensuring that you guys get the right data that you can put trust in it? >> Yes, absolutely. And so this is where we are, you know, part of it would be getting data, real world evidence data for patients who are being seen in the healthcare system with sickle cell disease, so that we can personalize the data to those patients and provide them with the right treatment, the right intervention that they need. And so part of it would be doing predictive modeling on some of the data, risk, stratifying risk, who in the sickle cell patient population is at risk of progressing. Or getting, you know, they all often get crisis, vaso-occlusive crisis because the cells, you know, the blood cell sickles and you want to avoid those chest crisis. And so part of what we'll be doing is, you know, using predictive modeling to target those at risk of the disease progressing, so that we can put in preventive measures. It's all about prevention. It's all about making sure that they're not being, you know, going to the hospital or the emergency room where sometimes they end up, you know, in pain and wanting pain medicine. And so. >> Do you see AI as being a critical piece in the transformation of healthcare, especially where inequities are concerned? >> Absolutely, and and when you say AI, I think it's responsible AI. >> Yes. >> And making sure that it's- >> Tracy: That's such a good point. >> Yeah. >> Very. >> With the right data, with relevant data, it's definitely key. I think there is so much data points that healthcare has, you know, in the healthcare space there's fiscal data, biological data, there's environmental data and we are not using it to the full capacity and full potential. >> Tracy: Yeah. >> And I think AI can do that if we do it carefully, and like I said, responsibly. >> That's a key word. You talked about trust, responsibility. Where data science, AI is concerned- >> Yeah. >> It has to be not an afterthought, it has to be intentional. >> Tracy: Exactly. >> And there needs to be a lot of education around it. Most people think, "Oh, AI is just for the technology," you know? >> Yeah, right. >> Goop. >> Yes. >> But I think we're all part, I mean everyone needs to make sure that we are collecting the right amount of data. I mean, I think we all play a part, right? >> We do. >> We do. >> In making sure that we have responsible AI, we have, you know, good data, quality data. And the data sciences is a multi-disciplinary field, I think. >> It is, which is one of the things that's exciting about it is it is multi-disciplinary. >> Tracy: Exactly. >> And so many of the people that we've talked to in data science have these very non-linear paths to get there, and so I think they bring such diversity of thought and backgrounds and experiences and thoughts and voices. That helps train the AI models with data that's more inclusive. >> Irene: Yes. >> Dropping down the volume on the bias that we know is there. To be successful, it has to. >> Definitely, I totally agree. >> What are some of the things, as we wrap up here, that you're looking forward to accomplishing as part of Marti Health? Like, maybe what's on the roadmap that you can share with us for Marti as it approaches the the second half of its first year? >> Yes, it's all about promoting health equity. It's all about, I mean, there's so much, well, I would start with, you know, part of the healthcare transformation is making sure that we are promoting care that's based on value and not volume, care that's based on good health outcomes, quality health outcomes, and not just on, you know, the quantity. And so Marti Health is trying to promote that value-based care. We are envisioning a world in which everyone can live their full life potential. Have the best health outcomes, and provide that patient-centered precision care. >> And we all want that. We all want that. We expect that precision and that personalized experience in our consumer lives, why not in healthcare? Well, thank you, Irene, for joining us on the program today. >> Thank you. >> Talking about what you're doing to really help drive the volume up on health equity, and raise awareness for the fact that there's a lot of inequities in there we have to fix. We have a long way to go. >> We have, yes. >> Lisa: But people like you are making an impact and we appreciate you joining theCUBE today and sharing what you're doing, thank you. >> Thank you. >> Thank you- >> Thank you for having me here. >> Oh, our pleasure. For our guest and Tracy Zhang, this is Lisa Martin from WIDS 2023, the eighth Annual Women in Data Science Conference brought to you by theCUBE. Stick around, our show wrap will be in just a minute. Thanks for watching. (light upbeat music)

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

(upbeat music) >> Hey everyone. Welcome back to theCUBE's live coverage of Women in Data Science, WiDS 2022, coming to live from Stanford University. I'm Lisa Martin. My next guest is here. Nandi Leslie, Doctor Nandi Leslie, Senior Engineering Fellow at Raytheon Technologies. Nandi, it's great to have you on the program. >> Oh it's my pleasure, thank you. >> This is your first WiDS you were saying before we went live. >> That's right. >> What's your take so far? >> I'm absolutely loving it. I love the comradery and the community of women in data science. You know, what more can you say? It's amazing. >> It is. It's amazing what they built since 2015, that this is now reaching 100,000 people 200 online event. It's a hybrid event. Of course, here we are in person, and the online event going on, but it's always an inspiring, energy-filled experience in my experience of WiDS. >> I'm thoroughly impressed at what the organizers have been able to accomplish. And it's amazing, that you know, you've been involved from the beginning. >> Yeah, yeah. Talk to me, so you're Senior Engineering Fellow at Raytheon. Talk to me a little bit about your role there and what you're doing. >> Well, my role is really to think about our customer's most challenging problems, primarily at the intersection of data science, and you know, the intersectional fields of applied mathematics, machine learning, cybersecurity. And then we have a plethora of government clients and commercial clients. And so what their needs are beyond those sub-fields as well, I address. >> And your background is mathematics. >> Yes. >> Have you always been a math fan? >> I have, I actually have loved math for many, many years. My dad is a mathematician, and he introduced me to, you know mathematical research and the sciences at a very early age. And so, yeah, I went on, I studied in a math degree at Howard undergrad, and then I went on to do my PhD at Princeton in applied math. And later did a postdoc in the math department at University of Maryland. >> And how long have you been with Raytheon? >> I've been with Raytheon about six years. Yeah, and before Raytheon, I worked at a small to midsize defense company, defense contracting company in the DC area, systems planning and analysis. And then prior to that, I taught in a math department where I also did my postdoc, at University of Maryland College Park. >> You have a really interesting background. I was doing some reading on you, and you have worked with the Navy. You've worked with very interesting organizations. Talk to the audience a little bit about your diverse background. >> Awesome yeah, I've worked with the Navy on submarine force security, and submarine tracking, and localization, sensor performance. Also with the Army and the Army Research Laboratory during research at the intersection of machine learning and cyber security. Also looking at game theoretic and graph theoretic approaches to understand network resilience and robustness. I've also supported Department of Homeland Security, and other government agencies, other governments, NATO. Yeah, so I've really been excited by the diverse problems that our various customers have you know, brought to us. >> Well, you get such great experience when you are able to work in different industries and different fields. And that really just really probably helps you have such a much diverse kind of diversity of thought with what you're doing even now with Raytheon. >> Yeah, it definitely does help me build like a portfolio of topics that I can address. And then when new problems emerge, then I can pull from a toolbox of capabilities. And, you know, the solutions that have previously been developed to address those wide array of problems, but then also innovate new solutions based on those experiences. So I've been really blessed to have those experiences. >> Talk to me about one of the things I heard this morning in the session I was able to attend before we came to set was about mentors and sponsors. And, you know, I actually didn't know the difference between that until a few years ago. But it's so important. Talk to me about some of the mentors you've had along the way that really helped you find your voice in research and development. >> Definitely, I mean, beyond just the mentorship of my my family and my parents, I've had amazing opportunities to meet with wonderful people, who've helped me navigate my career. One in particular, I can think of as and I'll name a number of folks, but Dr. Carlos Castillo-Chavez was one of my earlier mentors. I was an undergrad at Howard University. He encouraged me to apply to his summer research program in mathematical and theoretical biology, which was then at Cornell. And, you know, he just really developed an enthusiasm with me for applied mathematics. And for how it can be, mathematics that is, can be applied to epidemiological and theoretical immunological problems. And then I had an amazing mentor in my PhD advisor, Dr. Simon Levin at Princeton, who just continued to inspire me, in how to leverage mathematical approaches and computational thinking for ecological conservation problems. And then since then, I've had amazing mentors, you know through just a variety of people that I've met, through customers, who've inspired me to write these papers that you mentioned in the beginning. >> Yeah, you've written 55 different publications so far. 55 and counting I'm sure, right? >> Well, I hope so. I hope to continue to contribute to the conversation and the community, you know, within research, and specifically research that is computationally driven. That really is applicable to problems that we face, whether it's cyber security, or machine learning problems, or others in data science. >> What are some of the things, you're giving a a tech vision talk this afternoon. Talk to me a little bit about that, and maybe the top three takeaways you want the audience to leave with. >> Yeah, so my talk is entitled "Unsupervised Learning for Network Security, or Network Intrusion Detection" I believe. And essentially three key areas I want to convey are the following. That unsupervised learning, that is the mathematical and statistical approach, which tries to derive patterns from unlabeled data is a powerful one. And one can still innovate new algorithms in this area. Secondly, that network security, and specifically, anomaly detection, and anomaly-based methods can be really useful to discerning and ensuring, that there is information confidentiality, availability, and integrity in our data >> A CIA triad. >> There you go, you know. And so in addition to that, you know there is this wealth of data that's out there. It's coming at us quickly. You know, there are millions of packets to represent communications. And that data has, it's mixed, in terms of there's categorical or qualitative data, text data, along with numerical data. And it is streaming, right. And so we need methods that are efficient, and that are capable of being deployed real time, in order to detect these anomalies, which we hope are representative of malicious activities, and so that we can therefore alert on them and thwart them. >> It's so interesting that, you know, the amount of data that's being generated and collected is growing exponentially. There's also, you know, some concerning challenges, not just with respect to data that's reinforcing social biases, but also with cyber warfare. I mean, that's a huge challenge right now. We've seen from a cybersecurity perspective in the last couple of years during the pandemic, a massive explosion in anomalies, and in social engineering. And companies in every industry have to be super vigilant, and help the people understand how to interact with it, right. There's a human component. >> Oh, for sure. There's a huge human component. You know, there are these phishing attacks that are really a huge source of the vulnerability that corporations, governments, and universities face. And so to be able to close that gap and the understanding that each individual plays in the vulnerability of a network is key. And then also seeing the link between the network activities or the cyber realm, and physical systems, right. And so, you know, especially in cyber warfare as a remote cyber attack, unauthorized network activities can have real implications for physical systems. They can, you know, stop a vehicle from running properly in an autonomous vehicle. They can impact a SCADA system that's, you know there to provide HVAC for example. And much more grievous implications. And so, you know, definitely there's the human component. >> Yes, and humans being so vulnerable to those social engineering that goes on in those phishing attacks. And we've seen them get more and more personal, which is challenging. You talking about, you know, sensitive data, personally identifiable data, using that against someone in cyber warfare is a huge challenge. >> Oh yeah, certainly. And it's one that computational thinking and mathematics can be leveraged to better understand and to predict those patterns. And that's a very rich area for innovation. >> What would you say is the power of computational thinking in the industry? >> In industry at-large? >> At large. >> Yes, I think that it is such a benefit to, you know, a burgeoning scientist, if they want to get into industry. There's so many opportunities, because computational thinking is needed. We need to be more objective, and it provides that objectivity, and it's so needed right now. Especially with the emergence of data, and you know, across industries. So there are so many opportunities for data scientists, whether it's in aerospace and defense, like Raytheon or in the health industry. And we saw with the pandemic, the utility of mathematical modeling. There are just so many opportunities. >> Yeah, there's a lot of opportunities, and that's one of the themes I think, of WiDS, is just the opportunities, not just in data science, and for women. And there's obviously even high school girls that are here, which is so nice to see those young, fresh faces, but opportunities to build your own network and your own personal board of directors, your mentors, your sponsors. There's tremendous opportunity in data science, and it's really all encompassing, at least from my seat. >> Oh yeah, no I completely agree with that. >> What are some of the things that you've heard at this WiDS event that inspire you going, we're going in the right direction. If we think about International Women's Day tomorrow, "Breaking the Bias" is the theme, do you think we're on our way to breaking that bias? >> Definitely, you know, there was a panel today talking about the bias in data, and in a variety of fields, and how we are, you know discovering that bias, and creating solutions to address it. So there was that panel. There was another talk by a speaker from Pinterest, who had presented some solutions that her, and her team had derived to address bias there, in you know, image recognition and search. And so I think that we've realized this bias, and, you know, in AI ethics, not only in these topics that I've mentioned, but also in the implications for like getting a loan, so economic implications, as well. And so we're realizing those issues and bias now in AI, and we're addressing them. So I definitely am optimistic. I feel encouraged by the talks today at WiDS that you know, not only are we recognizing the issues, but we're creating solutions >> Right taking steps to remediate those, so that ultimately going forward. You know, we know it's not possible to have unbiased data. That's not humanly possible, or probably mathematically possible. But the steps that they're taking, they're going in the right direction. And a lot of it starts with awareness. >> Exactly. >> Of understanding there is bias in this data, regardless. All the people that are interacting with it, and touching it, and transforming it, and cleaning it, for example, that's all influencing the veracity of it. >> Oh, for sure. Exactly, you know, and I think that there are for sure solutions are being discussed here, papers written by some of the speakers here, that are driving the solutions to the mitigation of this bias and data problem. So I agree a hundred percent with you, that awareness is you know, half the battle, if not more. And then, you know, that drives creation of solutions >> And that's what we need the creation of solutions. Nandi, thank you so much for joining me today. It was a pleasure talking with you about what you're doing with Raytheon, what you've done and your path with mathematics, and what excites you about data science going forward. We appreciate your insights. >> Thank you so much. It was my pleasure. >> Good, for Nandi Leslie, I'm Lisa Martin. You're watching theCUBE's coverage of Women in Data Science 2022. Stick around, I'll be right back with my next guest. (upbeat flowing music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards Brought to you by Amazon Web Services. >> Hi and welcome to a special production of theCUBE. We're talking to the Amazon web services public sector, their partner awards program. I'm your host Stu Miniman, and we're digging in on education. It's one of the sectors, of course, public sector looks at nonprofits, it looks at the government sectors and the education, and of course, when we talk about remote learning is such a huge, important topic, especially right now in 2020 with the global pandemic. So happy to welcome to the program, we have two guests. First of all, representing the award winning-company, Mohammad Haque. He is the co-founder and senior vice president of architecture and engineering with eLumin. And joining is one of his customers, Damian Doyle, who is the associate vice president of enterprise infrastructure solutions at the University of Maryland Baltimore County, or UMBC, as it's known. Gentlemen, thanks so much for joining us. >> Thank you. Thanks for having us here. >> All right, first of all, Mohammad, congratulations. As I said in my intro, such an important topic. I have two children that are dealing with remote learning. I have lots of friends that work in higher education and new in the technology space. So your company is the 2020 AWS public sector award winner for best remote learning. I'm sure there is a space that has a lot of competition. And of course, leveraging public cloud is a great way to be able to ramp this sort of thing up rather fast. Give us a little bit, you know, you are the co=founder, so we'd love to hear a little bit of the origin story, your background, and tell us about what differentiates eLumin. >> Sure. eLumin, we provide managed products and services around end user compute with a focus on education for providing access to applications and other technology resources, course content, course applications in the public cloud, so that users are able to use whatever device they have wherever they are, and have access to those applications that are required for completing that course coursework. They can be in, at home, in their dorms, at a corner coffee shop, on the side of a mountain in the middle East, wherever they may be, but leveling that playing field so that they can access and have access to any of the demanding applications on any device is what we're, what our goal is, is to make sure that we're not having technology be a barrier to their learning. >> Fantastic. Damian, if we could turn to you, then. At UMBC, maybe if you could give our audience a thumbnail of the university, and I have some idea of the challenge that was put in front of you when you talk about e-learning, but maybe you could give us a little bit of the pre-COVID and what you were faced and what you were looking at when it came to dealing with the current situation. >> Sure. Be happy to. So we're, UMBC is a midsize public institution. We're sort of suburban, about 14,000 students, and we have undergrad, graduate, and doctoral programs, and we have a heavy focus on a lot of the STEM disciplines. And so pre-COVID, very based in collaborative environments, active learning, but hands-on, so a lot of our programs really do have a lot of that, and we leverage technology very heavily, even if it's in, whether it's in engineering, biology, any of those kinds of programs. As you said, the challenge became how do you very quickly pivot into an entirely online model when you sort of scattershot all of your students and you don't really have a great sense of what they're going to have access to and the abilities and connectivity they're going to have. So this kind of thing was really critical for us as we made that transition. >> Excellent. Mohammad, were you working with UMBC before the current move to go remote? Give us a little bit about the relationship and how that started. >> I believe actually that the pandemic was the impetus to kind of drive this forward. Damian and his team reached out to eLumin looking for a solution that would allow them to kind of have students access the applications that they normally would have access to in their physical computer labs, but with the change and not having access to those labs anymore, needed a remote learning solution, a remote access solution for being able to access those high compute, high graphics processing, memory-intensive applications through the cloud and taking into account the fact that students won't have the highest end computer laptop. They'll probably be working on a Chromebook or a lower-end machine, but need that compute power. And then we had to kind of provide a solution pretty quickly because it was, schools were shutting down, essentially, physically shutting down and needing to continue on with their coursework. >> Yeah, Damian, I'd like to understand from your side. Can you share with us a little bit the timeframes? How fast did you go from, oh my gosh, we need this, we need proposals, we need to roll this out, and we need to have students and teachers back up and running? >> Well, I think the one thing from our side, we had already known of eLumin and we had been looking at that pre-COVID. We knew we needed product that provided us this kind of agility and really gave the students some better access to the computing tools that they needed. So once we identified that, the thing that was amazing to me is we moved from our existing system over to production eLumin in, I think it was about two and a half weeks sort of start to finish, and to get all the images, to get all the technology running, tested, and everything up and running in two and a half weeks for a full solution for a campus is, was pretty amazing. And that was one of the real benefits we saw as going to the cloud. We also looked at this outside of COVID as something that really provided a major benefit to the students so that they could work from anywhere at any time, rather than be sort of tethered to that physical lab. >> Well, I'm glad you raised that. So if you could, Damian, a little bit help us understand how much were you using a cloud before? And it sounds like you believe that in the, I guess if we say post-COVID world, you will probably have some hybrid model. Would that be fair to say? >> Yeah, I think before we did have a different solution that was still cloud-based. It was part of our business continuity. So we still had some semblance of a virtual computing solution in the cloud, but it wasn't that extensive, and a lot of our individual programs, chemical engineering, geography, and others were using physical labs that the students would sort of schedule times and be able to work in as part of their coursework. Coming out of this, we fully expect if we're going an extended period of time where students are able to access these materials and these demanding software packages at any time from any kind of device coming out of COVID, they're not going to want to go back to that model where they're asking, they have to get permission and go in in limited hours into a physical lab and sit there. This is going to be the expectation going forward is that they have this kind of access and this kind of flexibility from now on. >> Yeah, this is, I mean, they've gotten a taste, essentially, and so they see how easy it is to complete their coursework without actually having to trek across campus into a lab and kind of fight with the population to find a seat. This basically will become an expectation of an offering. >> Yeah, Mohammad, what I'd love if you could drill in a little bit for us there. Architecturally speaking, of course, the cloud is built to be able to scale and move fast. So if you need capacity and need to scale up fast, that's great. If in the future, you still want to leverage this solution, but you can scale down, that should be possible. So maybe give us a little bit of a how AWS architecturally supports what you're doing, and just from a pricing solution standpoint, how you'll be able to support the customer in today's environment and however that path goes down the road, you'll be able to support that too. >> Right, I mean, so with AWS cloud, we're able to, as you said, scale up or down as demand is needed, but we've taken that even a little bit further where we're scaling based off of student scheduling. So if we've got a course that we know that is running from >> 10: 00 AM to 11:00 AM, prior to that course starting, we'll scale the environment up so that it's available for those students if it's more of a in course lab session and then spin things back down after the course is done so that we don't have those many, many machines sitting there running and burning the hours and running up the bill. Physical environment, once you've installed it, it's there. It's always running. You cannot do that. But with the power of the cloud, we're able to go up and down. We're able to take things, scale things down off hours. If we look at the patterns for student usage, off hours, overnight, take things down because you don't need those machines sitting there running all the time. >> And this is one of the biggest differentiators. So many times in higher ed, we struggle to have to explain to companies and vendors and providers what our needs are and how we're very different from corporations and other verticals. With the eLumin solution and the capabilities in AWS, we're really having this tailor to our students' schedules, to the class schedules, and that kind of flexibility makes the product economically viable for us, but it also means that we don't get nearly the kind of pushback from the academic side, because it is really tailored to meet their needs versus just something we're kind of shoehorning in. So that makes a huge difference in terms of adoption and the way it's perceived from a marketing and acceptance standpoint. >> Yeah, Damian, I'm curious, once you did that initial rollout, how much of an on ramp is there for both the education, the educator side, as well as the student side? And you talked about having some flexibility as to how and when students use things. That sounds great, but do you have to change office hours or the hours that the staff are leveraging that? I'm just trying to understand the ripple effect of what you're doing. >> No, it's a fair point. We have done fairly extensive training. The students picked it up very quickly. What we, with students, if there's a tool that they can use to do their work more effectively, they're going to use it, whether it's something we provide or something they find through other means. But what we've done is reached out to all of our faculty that we're training, that we're teaching in our physical labs and tried to work with them to understand what this solution is, how they can sort of rethink some of their classes. And a couple of our departments have actually taken an approach of rather than sit everybody in a virtual lab the same way they would sit people in a physical lab, they're moving some of this to more asynchronous so that the students can sort of work at their own pace and sort of rethink how they structure some of those classes because of the flexibility being provided. But it does take a lot of training from the instructional side and some rethinking of this, but the end solution is something that reaches the students where they are and the way they want to learn, which is a really powerful thing we're always trying to do. >> Excellent. Mohammad, I'm wondering just broadly learnings that you have from what's been happening. Obviously I'm sure you've been quite busy in responding to things. What's been the impact on your business? How has AWS been as a partner to be able to support the needs of what you're doing? >> Well, as you can imagine, things have just really blown up in terms of demand and being able to, again, through the power of the cloud, just being able to scale up and rapid deployment. As we spoke about earlier, this deployment was two and a half weeks from start to finish, being able to do that, being able to do that with AWS tools have been critical in moving things forward. >> Excellent. Damian, back to you on this. Obviously if you had had more time to be able to plan this out, there might be some things that you would do differently. But what have your learnings been with this? And if you've been talking to your peers, any advice that you would give as you've moved through this rapid acceleration of the move to remote? >> Certainly, I think we would've certainly done some things differently, but we had been talking about this move for three or four months ahead of COVID, so for us, it wasn't quite as rushed as the actual deployment wound up being. I think the big thing is having a vendor and having a partner where you can understand all the options. So the good and bad of the cloud is there's 100 different ways to do almost anything you want to accomplish, and taking the time to understand what the different features and the ramifications of how you deploy and how you think through that. For us, we deployed one way because we could do it very quickly, and then we took the rest of the semester and part of this summer to do some more thorough evaluations to really ask our constituents, do you like this method, or do you like some of the other possibilities, and see which user experience they liked more, and then we're able to work with eLumin, and they've been able to be very nimble in adjusting the services to meet what we've gotten our feedback on. So I think if I had to do it again, I would've done that testing ahead of time, but that's a very minor thing. These are really sort of small tweaks to just make life a little easier, not fundamental differences in what we're providing. >> Yeah, Damian, one last question, if I could. Sorry, Mohammad. Just, I'm curious from the financial standpoint how much you felt that you understood what costs would be and some of the levers as to what you were using and the impact there. We've seen great maturation over the last handful of years as to transparency and understanding how cloud actually is built. But just curious if you have any final comments on the financial piece of things, seeing that it probably wasn't something that was in your budget for the last quarter. >> It wasn't, that's very true, but we also knew that it was essential. So what we realized was we didn't know how often a lot of our physical labs and these classes were being used. So we knew there was going to be some unknowns. We'd move to this, we'd have to see what adoption was. But being able to get the reporting out and working with Mohammad and others to really start customizing in the cloud. That's the beauty of it is we recognize, we saw some really fascinating patterns where during the week people would use this sort of as you'd expect, but on the weekends, it was in the evenings. Nobody's logging on Saturday or Sunday morning, but boy, at eight p.m., there's a good bit of usage. So we could tailor and do some of that off-hours work and really slows things down. Having that visibility has made the economic piece much more viable, and really being able to tweak the computing power with two different needs of the different classes. So it's actually been fairly easy to understand, but it was a ramp up where we had to sort of guess at first and then understand our own processes. But that's more sort of the, if you don't have good data coming in, it's hard to get it out. >> Excellent. And Mohammad, I want to let you kind of give your lessons learned. Obviously it's a technology space you've been in and it's just been an acceleration of some of the things you're working on. So lessons learned, advice you would give to other companies, other universities and educational facilities out there. >> Right, and this is, again, speaking to the power of the cloud, right? Some of the, one of the biggest lessons learned here is you don't necessarily need to get it right the first time. As Damian was saying, we went back, kind of analyzed what we were seeing, and after the initial deployment, took a look at the actual usage and kind of adjusted based off of that, according to that, taking in feedback from faculty members on how they were using the system and tweaking the presentation or tweaking applications on the back end for accommodating those needs. That's the power of the cloud, being able to adjust on the fly. You're not, you don't have to be committed to every single bit there, and being able to change it on the fly is just something that is kind of natural in the cloud these days. >> Excellent. Well, thank you both so much for joining us, Damian, thank you for joining and moving forward, sharing your story, wish you the best of luck going forward. And Mohammad, big congratulations on winning. Super important category, especially here in 2020. Congratulations to you and the team. >> Thank you. >> Yeah, thank you. >> All right, stay tuned for more coverage here from the AWS public sector. It's their partner awards program. I'm Stu Miniman, and thank you for watching theCUBE. (bright music)

>>from the Cube Studios in Palo Alto and Boston, connecting with thought leaders >>all around the world. >>This is a cube conversation. Hi, and welcome to a special production of the Cube. We're talking to the Amazon Web services, public sector, their partner awards program. I'm your host stew minimum, and we're digging in on education is one of the sectors. Of course, public sector looks at non profits. It looks at the government sectors. Education, Of course, when we talk about remote learning is such a huge, important topic, especially right now in 2020 with a global pandemic so happy to welcome to the program. We have two guests. First of all, we're representing the award winning company Mohammad. He is the co founder and senior vice president of architecture and engineering with Lumen and joining his one of his customers, Damien Doyle, who is the associate vice president of Enterprise Infrastructure Solutions at the University of Maryland, Baltimore County, or UMBC. As it's known, gentlemen, thanks so much for joining us. >>Thank you. Thanks for having us. >>Alright. First of all, Mohammed, congratulations. As I said in my intro, you know, such an important topic and I have two Children that are, you know, dealing with remote learning have lots of friends that were in higher education and, you know, in the technology space. So your company is the 2020 AWS Public Sector Award winner for best remote learning. I'm sure there is a space that has a lot of competition on. Of course, leveraging public cloud is a great way to be able to ramp this sort of thing up rather fast. Give us a little bit. You know, you are the co founder. So would love to hear a little bit of the origin story, your background and Ellis about what differentiates the looming >>sure loom in we provide ah manage products and services around end user compute with a focus on education for providing access to applications and other technology. Resource is, of course, content course applications in the public cloud, so that users are able to use, you know, whatever device they have wherever they are, um so and have access to those applications that are required for completing that force work they could be in, you know, in at home, in their dorms, at a corner coffee shop on the side of a mountain in the Middle East wherever they may be. But leveling that playing fears playing field so that they could access, um, have access to any of the demanding applications on any device is what we're You know, What our goal is is to make sure that we're not having technology be a barrier to their learning. >>Fantastic. Damien, If if we could turn to you, then atyou NBC, maybe if you could give our audience Ah, thumbnail of you know, the university and I have some idea of the challenge that was put in front of you when you talk about the learning. But maybe you could give us a little bit of the pre cove it and, uh, you know what? What you were faced in and what you were looking at when it came to dealing with the current situation. >>Sure be happy to So where you? NBC is a mid sized public institution. We're sort of suburban, about 14,000 students, and we have undergrad, graduate and doctoral programs, and we have a heavy focus on a lot of the stem disciplines. And so pre cove, it very based in collaborative environments, active learning but but hands on. So a lot of our programs really do have a lot of that. We leverage technology very heavily, even if it's in whether it's an engineering biology, any of those kinds of programs. Uh huh. As you said that the challenge became how do you very quickly pivot into an entirely online model when you sort of scatter shot all of your students and you don't really have a great sense of what they're gonna have access to and, um, and the abilities and connectivity they're gonna have. So this this kind of thing was really critical for us as we made that transition. >>Excellent. Mohammed, Were you working with you, NBC before the current move toe Go, go remote. Give us a little bit about the relationship and how that started. >>I believe, actually that the pandemic was the impetus to kind of drive this forward. Damien and his team reached out to loom in looking for a solution that would allow them to kind of have students access the applications that they normally would have access to in their physical computer labs. But with ah the change and not having to access those labs anymore needed a remote learning solution. A remote access solution for being able to access those high compute high graphics processing or memory intensive applications through the cloud. Taking into account the fact that you know, students won't have you know, that the highest end computer laptop, you know, they probably be working on a chromebook or a lower and machine, but need that compute power on. And then we had to kind of provide a solution pretty quickly because it was, you know, schools were shutting down, essentially physically started shutting down and needing to continue on with their coursework. Coursework? >>Yeah, Dave and I like to understand from your side. Can you share with us a little bit that time frames, you know, how fast did you go from? Oh, my gosh, We need this. We need proposals. We need to roll this out, and we need to have students. Ah, in teachers back up and running. >>Well, you know, I think the one thing from our side we had already known of element and we've been looking at that pre cove it. We knew we needed a product that that provided us this kind of agility and really gave the students some better access to the computing tools that they need it. So once we identify that, the thing that was amazing to me is is we moved from our existing system over to production illumination. It was about 2.5 weeks sort of start to finish and, you know, to get all the images to get all the technology running tested and everything up and running in 2.5 weeks for a full solution for a campus is was pretty amazing. And that was one of the real benefits we saw was going to the cloud. We also looked at this outside of code as something that really provided a major benefit to the students so that they could work from anywhere at any time rather than be sort of tethered to that physical lab. >>Well, I'm glad you raised that. So if you could Damien a little bit, you know, help us understand. How much are you using A cloud before? And it sounds like you believe that, you know, in the you know, I guess if we say postcode world, you would probably have some hybrid model. Would that be fair to say, >>Yeah, I think before we did have a different solution that was still cloud based. It was part of our business continuity. So we still had some semblance of virtual computing solution in the cloud. But it wasn't that extensive. And a lot of our individual programs chemical engineering, geography and others were using physical labs that the students would sort of scheduled times and be able to work in as part of their coursework. Uh, coming out of this, we fully expect if, if we're going on extended period of time where students are able to access these materials and these demanding software packages at any time from any kind of device coming out of cove it they're not gonna want to go back to that model where they're asking, you know, they have to get permission and go in and limited hours into a physical lab and sit there. This is going to be the expectation going forward is that they have this kind of access and this kind of flexibility from now. >>Yeah, this is I mean, they've gotten a taste essentially, and so, you know, they they see how easy it is to complete their coursework without actually having to trek across campus into a lab and kind of fight with the population to find a seat. This basically will become an expectation of an offering. >>Mohammed, what I'd love if you could drill in a little bit for us there, Architecturally speaking, of course, the cloud is built to be able to scale and move fast. So if you need capacity and need to scale up fast, that's great if in the future you still want to leverage the solution. But you can scale down, that should be possible. So maybe give us a little bit of you know how aws arc. It actually supports what you're doing and, you know, just from a pricing solution standpoint, how you'll be able to support the customer in today's environment. And however that path goes down the road, you'll be able to support that, >>right? I mean, so, you know, with the AWS cloud, we're able to, as you said, scale up or down as demand is needed. But we we've taken that even a little bit further where we're scaling based off of, um, students scheduling. So if we've got, of course, that we know that is running from 10 AM to 11 AM Your prior to that core starting will scale the environment up so that it's available for those students. If it's not, you know, more of, ah, in course, lab session, um, and then spin things back down after the course is done so that we don't have that those many, many machines sitting there running and burning the hours and running up the bill. You know, physical environment. You know, once you've installed it, it's there. It's always running. You cannot do that. But with the power of the cloud, we're able to go up and down. We're able to take things. Uh, you know, scale things down off hours. If we look at the patterns for a student usage, you know, off hours overnight take things down because you don't need those machines sitting there running, running all the time. >>And this is one of the biggest differentiators so many times in higher ed. We struggle to have to explain to companies and vendors and providers what our needs are and how we're very. We're very different from corporations and other other verticals with the bloomin solution and the capabilities in AWS. But we're really having this Taylor to our students schedules to the class schedules, and that kind of flexibility makes the product economically viable for us. But it also means that we don't get nearly the kind of push back from the academic side because it is really Taylor to meet their needs versus just something we're kind of shoehorning in. So that makes a huge difference in terms of adoption and the way it's perceived from a marketing, marketing and acceptance standpoint. Yeah, >>Dave and I'm curious. Once you did that initial rollout, how much of an on ramp is there for both the education, the educators side as well as student side? And you talked about having some flexibility as to how and when students use thing. That sounds great, but do you have to change, you know, office hours or the hours that the staff are leveraging that I'm just trying to understand the you know, the ripple effect of what you're doing? >>No, it's It's a fair point. We have done fairly extensive training. The students picked it up very quickly. What we with students? If there is a tool that they can use to do their work more effectively. They're going to use it, whether it's something we provide or something they find through other means. But what we've done is is reached out to all of our faculty that were training, that we're teaching in our physical labs and try to work with them to understand what the solution is, how they can sort of rethink some of their classes. And a couple of our departments have actually taking a approach of rather than said everybody in a virtual lab the same way they would sit people in a physical lab. They're moving some of this team or a synchronous so that the students can serve, work at their own pace and rethink how they structure some of those classes because of the flexibility being provided. But it does take a lot of training from the instructional side and some rethinking off this. But it the end solution is something that reaches the students where they are and the way they want to learn, which is a really powerful thing. We're always trying to do >>excellent, Mohammed. I'm wondering just broadly learnings that you have from what what's been happening Obviously, I'm sure you've been quite busy and responding to things. You know, what's been the impact on your business, how as a ws been as a partner to support the needs of what you're doing. >>Well, as you can imagine, the other things that just really blown up, Um, in terms of demand and being able to again through the plant power of the cloud, just being able to scale up and rapid deployment, you know, as we talk about earlier this deployment was, you know, 2 2.5 weeks from start to finish. Being able to do that, being able to do that with AWS tools have been, um, critical and moving things forward. >>Excellent. Uh, Damien, it's a sit back to you on this. You know, obviously, if you had had, you know, more time be able to plan this out if there might be some things that you would do differently. But what have your learnings been with this? And if you've been talking to your peers, any advice that you would give, uh, you know, as you've moved through this this rapid acceleration of the move to remote >>you Certainly. I think we would have certainly done some things differently. But we have been talking about this move for three or four months ahead of Covitz. So for us it wasn't. It wasn't quite as rushed as the actual deployment wound up being. I think the big thing is having having a vendor and having a partner where you can understand all the options. So the good and bad of the cloud is there's 100 different ways to do almost anything you want to accomplish and taking the time to understand what the different features and the ramifications of how you how you deploy and how you think. Think through that for us. We deployed one way because we could do it very quickly. And then we took the rest of the semester and part of this summer to do some more thorough evaluations to really ask our constituents you like this method or do you like some of the other, possibly some of the other possibilities and see which user experience they liked more? And then we're able to work with illumination, and they've been ableto very nimble in adjusting the services to meet what we've gotten our feedback on. So I think if I had to do it again, I would have done that testing ahead of time. But that's a very minor thing. These air really sort of small tweaks to just make life a little easier. Not fundamental differences in the what we're providing. >>Yeah, I'm Damien. What? One last question if I could, um sorry. Sorry, Mohammed. Just I'm curious from the financial standpoint, you know how much you felt that you understood what costs would be in some of the levers as to what are you using in the impact there? We've seen, you know, great maturation over the last handful of years. As toe. Yeah, you know, transparency and understanding how cloud actually is build. But I'm just curious if you have any final comments on the financial piece things, seeing that, it probably wasn't something that was in your budget for the last quarter. Yeah, >>it wasn't. That's very true. But we also knew that it was essential so that what we realized was we didn't know how often a lot of our physical labs and these classes were being used. So we knew there was going to be some unknowns. We've moved to this would have to see what adoption was but be able to get the reporting out and working with Mohammed and others to really start customizing in the cloud. That's the beauty of it is we recognize we saw some really fascinating patterns where during the week people would use this sort of as you'd expect. But on the weekends it was in the evenings. Nobody, nobody is logging on Saturday or Sunday morning. But boy at eight PM there's a good bit of usage so we could tailor and do some of that off hours work and really slows things down. Having that visibility has made the economic piece much more viable and really being able to tweak the computing power with two different needs of the different classes. So it's actually been fairly easy to understand, but it was a ramp up where we have to sort of guess at first and then understand our own processes. But that's more sort of the If you don't have good data coming in, it's hard to get it. Get it out. Excellent. Mohammad, I >>want you to kind of give your lessons learned. Obviously, it's a technology space. You've been in. Ah, and it's just been an acceleration of some of the things you're working on. So lessons learned advice you would give Teoh, you know, other companies of the universities and education No facilities out there, >>Right? And, you know, this is again speaking to the power of the cloud, right? Some of that one of the biggest lessons learned here is you don't necessarily need to get it right the first time. It's name and saying was saying, You know, we went back kind of analyze what we were staying in after the initial deployment, took a look at the actual usage and kind of adjusted, based based off of that. According to that, taking and feedback from faculty members on how they were using a system in tweaking the presentation or tweaking applications on the back end for accommodating those needs. That's the power of the cloud being able to adjust on the fly. You're not. You don't have to be committed to every single bit there. Uh, and being able to change it on the fly is is just something that is kind of natural in the cloud these days. >>Excellent. Well, thank you both. So much for joining us, Damien. Thank you for joining and moving forward. Sharing your story. I wish you the best of luck going forward. And Mohammed Big. Congratulations on winning. You know, super important category. Especially here in 20. Funny congratulations to you and the team. >>Thank you. >>Yeah, Thank you. Alright, stay tuned for more coverage here from the AWS public sector is their partner awards program. I'm Stew men a man And thank you for watching the Cube. Yeah, yeah, yeah, yeah.

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. In Cambridge, Massachusetts. Everybody. You're watching the cube. The leader in live tech coverage. This is M I t CDO I Q the chief data officer and information quality conference. Someday Volonte with my co host, Paul Galen. Professor Dr Stewart, Mad Nick is here. Longtime Cube alum. Ah, long time professor at M i. T soon to be retired, but we're really grateful that you're taking your time toe. Come on. The Cube is great to see you again. >> It's great to see you again. It's been a long time. She worked together and I really appreciate the opportunity to share our spirits. Hear our mighty with your audience. Well, it's really been fun >> to watch this conference evolved were full and it's really amazing. We have to move to a new venue >> next year. I >> understand. And data we talk about the date explosion all the time, But one of the areas that you're focused on and you're gonna talk about today is his ethics and privacy and data causes so many concerns in those two areas. But so give us the highlight of what you're gonna discuss with the audience today. We'll get into >> one of things that makes it so challenging. It is. Data has so many implications. Tow it. And that's why the issue of ethics is so hard to get people to reach agreement on it. We're talking people regarding medicine and the idea big data and a I so know, to be able to really identify causes you need mass amounts of data. That means more data has to be made available as long as it's Elsa data, not mine. Well, not my backyard. If he really So you have this issue where on the one hand, people are concerned about sharing the data. On the other hand, there's so many valuable things would gain by sharing data and getting people to reach agreement is a challenge. Well, one of things >> I wanted to explore with you is how things have changed you back in the day very familiar with Paul you as well with Microsoft, Department of Justice, justice, FTC issues regarding Microsoft. And it wasn't so much around data was really around browsers and bundling things today. But today you see Facebook and Google Amazon coming under fire, and it's largely data related. Listen, Liz Warren, last night again break up big tech your thoughts on similarities and differences between sort of the monopolies of yesterday and the data monopolies of today Should they be broken up? What do you thought? So >> let me broaden the issue a little bit more from Maryland, and I don't know how the demographics of the audience. But I often refer to the characteristics that millennials the millennials in general. I ask my students this question here. Now, how many of you have a Facebook account in almost every class? Facebook. You realize you've given away a lot of nation about yourself. It it doesn't really occurred to them. That may be an issue. I was told by someone that in some countries, Facebook is very popular. That's how they cordoned the kidnappings of teenagers from rich families. They track them. They know they're going to go to this basketball game of the soccer match. You know exactly what I'm going after it. That's the perfect spot to kidnap them, so I don't know whether students think about the fact that when they're putting things on Facebook than making so much of their life at risk. On the other hand, it makes their life richer, more enjoyable. And so that's why these things are so challenging now, getting back to the issue of the break up of the big tech companies. One of the big challenges there is that in order to do the great things that big data has been doing and the things that a I promises do you need lots of data. Having organizations that can gather it all together in a relatively systematic and consistent manner is so valuable breaking up the tech companies. And there's some reasons why people want to do that, but also interferes with that benefit. And that's why I think it's gonna be looked at real Kim, please, to see not only what game maybe maybe breaking up also what losses of disadvantages we're creating >> for ourselves so example might be, perhaps it makes United States less competitive. Visa VI China, in the area of machine intelligence, is one example. The flip side of that is, you know Facebook has every incentive to appropriate our data to sell ads. So it's not an easy, you know, equation. >> Well, even ads are a funny situation for some people having a product called to your attention that something actually really want. But you never knew it before could be viewed as a feature, right? So, you know, in some case of the ads, could be viewed as a feature by some people. And, of course, a bit of intrusion by other people. Well, sometimes we use the search. Google, right? Looking >> for the ad on the side. No longer. It's all ads. You know >> it. I wonder if you see public public sentiment changing in this respect. There's a lot of concerns, certainly at the legislative level now about misuse of data. But Facebook user ship is not going down. Instagram membership is not going down. Uh, indication is that that ordinary citizens don't really care. >> I know that. That's been my I don't have all the data. Maybe you may have seen, but just anecdotally and talking to people in the work we're doing, I agree with you. I think most people maybe a bit dramatic, but at a conference once and someone made a comment that there has not been the digital Pearl Harbor yet. No, there's not been some event that was just so onerous. Is so all by the people. Remember the day it happened kind of thing. And so these things happen and maybe a little bit of press coverage and you're back on your Facebook. How their instagram account the next day. Nothing is really dramatic. Individuals may change now and then, but I don't see massive changes. But >> you had the Equifax hack two years ago. 145,000,000 records. Capital one. Just this week. 100,000,000 records. I mean, that seems pretty Pearl Harbor ish to me. >> Well, it's funny way we're talking about that earlier today regarding different parts of the world. I think in Europe, the general, they really seem to care about privacy. United States that kind of care about privacy in China. They know they have no privacy. But even in us where they care about privacy, exactly how much they care about it is really an issue. And in general it's not enough to move the needle. If it does, it moves it a little bit about the time when they show that smart TVs could be broken into smart. See, TV sales did not Dutch an inch. Not much help people even remember that big scandal a year ago. >> Well, now, to your point about expects, I mean, just this week, I think Equifax came out with a website. Well, you could check whether or not your credentials were. >> It's a new product. We're where we're compromised. And enough in what has been >> as head mind, I said, My wife says it's too. So you had a choice, you know, free monitoring or $125. So that way went okay. Now what? You know, life goes >> on. It doesn't seem like anything really changes. And we were talking earlier about your 1972 book about cyber security, that many of the principles and you outlined in that book are still valid today. Why are we not making more progress against cybercriminals? >> Well, two things. One thing is you gotta realize, as I said before, the Cave man had no privacy problems and no break in problems. But I'm not sure any of us want to go back to caveman era because you've got to realize that for all these bad things. There's so many good things that are happening, things you could now do, which a smartphone you couldn't even visualize doing a decade or two ago. So there's so much excitement, so much for momentum, autonomous cars and so on and so on that these minor bumps in the road are easy to ignore in the enthusiasm and excitement. >> Well and now, as we head into 2020 affection it was. It was fake news in 2016. Now we've got deep fakes. Get the ability to really use video in new ways. Do you see a way out of that problem? A lot of people looking a Blockchain You wrote an article recently, and Blockchain you think it's on hackable? Well, think again. >> What are you seeing? I think one of things we always talk about when we talk about improving privacy and security and organizations, the first thing is awareness. Most people are really small moment of time, aware that there's an issue and it quickly pass in the mind. The analogy I use regarding industrial safety. You go into almost any factory. You'll see a sign over the door every day that says 520 days, his last industrial accident and then a sub line. Please do not be the one to reset it this year. And I often say, When's the last time you went to a data center? And so assign is at 50 milliseconds his last cyber data breach. And so it needs to be something that is really front, the mind and people. And we talk about how to make awareness activities over companies and host household. And that's one of our major movements here is trying to be more aware because we're not aware that you're putting things at risk. You're not gonna do anything about it. >> Last year we contacted Silicon Angle, 22 leading security experts best in one simple question. Are we winning or losing the war against cybercriminals? Unanimously, they said, we're losing. What is your opinion of that question? >> I have a great quote I like to use. The good news is the good guys are getting better than a firewall of cryptographic codes. But the bad guys are getting batter faster, and there's a lot of reasons for that well on all of them. But we came out with a nautical talking about the docking Web, and the reason why it's fascinating is if you go to most companies if they've suffered a data breach or a cyber attack, they'll be very reluctant to say much about unless they really compelled to do so on the dock, where they love to Brent and reputation. I'm the one who broke in the Capital One. And so there's much more information sharing that much more organized, a much more disciplined. I mean, the criminal ecosystem is so much more superior than the chaotic mess we have here on the good guys side of the table. >> Do you see any hope for that? There are service's. IBM has one, and there are others in a sort of anonymous eyes. Security data enable organizations to share sensitive information without risk to their company. You see any hope on the collaboration, Front >> said before the good guys are getting better. The trouble is, at first I thought there was an issue that was enough sharing going on. It turns out we identified over 120 sharing organizations. That's the good news. And the bad news is 120. So IBM is one and another 119 more to go. So it's not a very well coordinated sharing. It's going just one example. The challenges Do I see any hope in the future? Well, in the more distant future, because the challenge we have is that there'll be a cyber attack next week of some form or shape that we've never seen before and therefore what? Probably not well prepared for it. At some point, I'll no longer be able to say that, but I think the cyber attackers and creatures and so on are so creative. They've got another decade of more to go before they run out of >> Steve. We've got from hacktivists to organized crime now nation states, and you start thinking about the future of war. I was talking to Robert Gates, aboutthe former defense secretary, and my question was, Why don't we have the best cyber? Can't we go in the oven? It goes, Yeah, but we also have the most to lose our critical infrastructure, and the value of that to our society is much greater than some of our adversaries. So we have to be very careful. It's kind of mind boggling to think autonomous vehicles is another one. I know that you have some visibility on that. And you were saying that technical challenges of actually achieving quality autonomous vehicles are so daunting that security is getting pushed to the back burner. >> And if the irony is, I had a conversation. I was a visiting professor, sir, at the University of Niece about a 12 14 years ago. And that's before time of vehicles are not what they were doing. Big automotive tele metrics. And I realized at that time that security wasn't really our top priority. I happen to visit organization, doing really Thomas vehicles now, 14 years later, and this conversation is almost identical now. The problems we're trying to solve. A hider problem that 40 years ago, much more challenging problems. And as a result, those problems dominate their mindset and security issues kind of, you know, we'll get around him if we can't get the cot a ride correctly. Why worry about security? >> Well, what about the ethics of autonomous vehicles? Way talking about your programming? You know, if you're gonna hit a baby or a woman or kill your passengers and yourself, what do you tell the machine to Dio, that is, it seems like an unsolvable problem. >> Well, I'm an engineer by training, and possibly many people in the audience are, too. I'm the kind of person likes nice, clear, clean answers. Two plus two is four, not 3.94 point one. That's the school up the street. They deal with that. The trouble with ethic issues is they don't tend to have a nice, clean answer. Almost every study we've done that has these kind of issues on it. And we have people vote almost always have spread across the board because you know any one of these is a bad decision. So which the bad decision is least bad. Like, what's an example that you used the example I use in my class, and we've been using that for well over a year now in class, I teach on ethics. Is you out of the design of an autonomous vehicle, so you must program it to do everything and particular case you have is your in the vehicle. It's driving around the mountain and Swiss Alps. You go around a corner and the vehicle, using all of senses, realize that straight ahead on the right? Ian Lane is a woman in a baby carriage pushing on to this onto the left, just entering the garage way a three gentlemen, both sides a road have concrete barriers so you can stay on your path. Hit the woman the baby carriage via to the left. Hit the three men. Take a shop, right or shot left. Hit the concrete wall and kill yourself. And trouble is, every one of those is unappealing. Imagine the headline kills woman and baby. That's not a very good thing. There actually is a theory of ethics called utility theory that says, better to say three people than to one. So definitely doing on Kim on a kill three men, that's the worst. And then the idea of hitting the concrete wall may feel magnanimous. I'm just killing myself. But as a design of the car, shouldn't your number one duty be to protect the owner of the car? And so people basically do. They close their eyes and flip a coin because they don't want anyone. Those hands, >> not an algorithmic >> response, doesn't leave. >> I want to come back for weeks before we close here to the subject of this conference. Exactly. You've been involved with this conference since the very beginning. How have you seen the conversation changed since that time? >> I think I think it's changing to Wei first. As you know, this record breaking a group of people are expecting here. Close to 500 I think have registered s o much Clea grown kind of over the years, but also the extent to which, whether it was called big data or call a I now whatever is something that was kind of not quite on the radar when we started, I think it's all 15 years ago. He first started the conference series so clearly has become something that is not just something We talk about it in the academic world but is becoming main stay business for corporations Maur and Maur. And I think it's just gonna keep increasing. I think so much of our society so much of business is so dependent on the data in any way, shape or form that we use it and have >> it well, it's come full circle. It's policy and I were talking at are open. This conference kind of emerged from the ashes of the back office information quality and you say the big date and now a I guess what? It's all coming back to information. >> Lots of data. That's no good. Or that you don't understand what they do with this. Not very healthy. >> Well, doctor Magic. Thank you so much. It's a >> relief for all these years. Really Wanna thank you. Thank you, guys, for joining us and helping to spread the word. Thank you. Pleasure. All right, keep it right, everybody. Paul and >> I will be back at M I t cdo right after this short break. You're watching the cue.

>> live from Boston, Massachusetts. It's the Cube covering A W s reinforce 2019. Brought to you by Amazon Web service is and its ecosystem partners. >> Well, welcome back. Everyone's Cube Live coverage here in Boston, Massachusetts, for AWS. Reinforce Amazon Web sources. First inaugural conference around security. It's not Osama. It's a branded event. Big time ecosystem developing. We have returning here. Cube Alumni Bill Jeff for VP of strategy and the partnerships that Iron Net Cyber Security Company. Welcome back. Thanks. General Keith Alexander, who was on a week and 1/2 ago. And it was public sector summit. Good to see you. Good >> to see you. Thanks for >> having my back, but I want to get into some of the Iran cyber communities. We had General Qi 1000. He was the original commander of the division. So important discussions that have around that. But don't get your take on the event. You guys, you're building a business. The minute cyber involved in public sector. This is commercial private partnership. Public relations coming together. Yeah. Your models are sharing so bringing public and private together important. >> Now that's exactly right. And it's really great to be here with eight of us were really close partner of AWS is we'll work with them our entire back in today. Runs on AWS really need opportunity. Get into the ecosystem, meet some of the folks that are working that we might work with my partner but to deliver a great product, right? And you're seeing a lot of people move to cloud, right? And so you know some of the big announcement that are happening here today. We're willing. We're looking to partner up with eight of us and be a first time provider for some key new Proactiv elves. AWS is launching in their own platform here today. So that's a really neat thing for us to be partnered up with this thing. Awesome organization. I'm doing some of >> the focus areas around reinforcing your party with Amazon shares for specifics. >> Yes. So I don't know whether they announced this capability where they're doing the announcement yesterday or today. So I forget which one so I'll leave that leave that leave that once pursued peace out. But the main thing is, they're announcing couple of new technology plays way our launch party with them on the civility place. So we're gonna be able to do what we were only wanted to do on Prem. We're gonna be able to do in the cloud with AWS in the cloud formation so that we'll deliver the same kind of guy that would deliver on prime customers inside their own cloud environments and their hybrid environment. So it's a it's a it's a sea change for us. The company, a sea change for a is delivering that new capability to their customers and really be able to defend a cloud network the way you would nonpregnant game changer >> described that value, if you would. >> Well, so you know, one of the key things about about a non pregnant where you could do you could look at all the flows coming past you. You look at all the data, look at in real time and develop behavior. Lana looks over. That's what we're doing our own prime customers today in the cloud with his world who looked a lox, right? And now, with the weight of your capability, we're gonna be able to integrate that and do a lot Maur the way we would in a in a in a normal sort of on Prem environment. So you really did love that. Really? Capability of scale >> Wagon is always killed. The predictive analytics, our visibility and what you could do. And too late. Exactly. Right. You guys solve that with this. What are some of the challenges that you see in cloud security that are different than on premise? Because that's the sea, So conversation we've been hearing. Sure, I know on premise. I didn't do it on premises for awhile. What's the difference between the challenge sets, the challenges and the opportunities they provide? >> Well, the opportunities air really neat, right? Because you've got that even they have a shared responsibility model, which is a little different than you officially have it. When it's on Prem, it's all yours essential. You own that responsibility and it is what it is in the cloud. Its share responsible to cloud provider the data holder. Right? But what's really cool about the cloud is you could deliver some really interesting Is that scale you do patch updates simultaneously, all your all your back end all your clients systems, even if depending how your provisioning cloud service is, you could deliver that update in real time. You have to worry about. I got to go to individual systems and update them, and some are updated. Summer passed. Some aren't right. Your servers are packed simultaneously. You take him down, you're bringing back up and they're ready to go, right? That's a really capability that for a sigh. So you're delivering this thing at scale. It's awesome now, So the challenge is right. It's a new environment so that you haven't dealt with before. A lot of times you feel the hybrid environment governed both an on Prem in sanitation and class sensation. Those have to talkto one another, right? And you might think about Well, how do I secure those those connections right now? And I think about spending money over here when I got all seduced to spend up here in the cloud. And that's gonna be a hard thing precisely to figure out, too. And so there are some challenges, but the great thing is, you got a whole ecosystem. Providers were one of them here in the AWS ecosystem. There are a lot here today, and you've got eight of us as a part of self who wants to make sure that they're super secure, but so are yours. Because if you have a problem in their cloud, that's a challenge. Them to market this other people. You talk about >> your story because your way interviews A couple weeks ago, you made a comment. I'm a recovering lawyer, kind of. You know, we all laughed, but you really start out in law, right? >> How did you end up here? Yeah, well, the truth is, I grew up sort of a technology or myself. My first computer is a trash 80 a trs 80 color computer. RadioShack four k of RAM on board, right. We only >> a true TRS 80. Only when I know what you're saying. That >> it was a beautiful system, right? Way stored with sword programs on cassette tapes. Right? And when we operated from four Keita 16 k way were the talk of the Rainbow Computer Club in Santa Monica, California Game changer. It was a game here for 16. Warning in with 60 give onboard. Ram. I mean, this is this is what you gonna do. And so you know, I went from that and I in >> trouble or something, you got to go to law school like you're right >> I mean, you know, look, I mean, you know it. So my dad, that was a chemist, right? So he loved computers, love science. But he also had an unrequited political boners body. He grew up in East Africa, Tanzania. It was always thought that he might be a minister in government. The Socialist came to power. They they had to leave you at the end of the day. And he came to the states and doing chemistry, which is course studies. But he still loved politics. So he raised at NPR. So when I went to college, I studied political science. But I paid my way through college doing computer support, life sciences department at the last moment. And I ran 10 based. He came on climate through ceilings and pulled network cable do punch down blocks, a little bit of fibrous placing. So, you know, I was still a murderer >> writing software in the scythe. >> One major, major air. And that was when when the web first came out and we had links. Don't you remember? That was a text based browser, right? And I remember looking to see him like this is terrible. Who would use http slash I'm going back to go for gophers. Awesome. Well, turns out I was totally wrong about Mosaic and Netscape. After that, it was It was it was all hands on >> deck. You got a great career. Been involved a lot in the confluence of policy politics and tech, which is actually perfect skill set for the challenge we're dealing. So I gotta ask you, what are some of the most important conversations that should be on the table right now? Because there's been a lot of conversations going on around from this technology. I has been around for many decades. This has been a policy problem. It's been a societal problem. But now this really focus on acute focus on a lot of key things. What are some of the most important things that you think should be on the table for techies? For policymakers, for business people, for lawmakers? >> One. I think we've got to figure out how to get really technology knowledge into the hands of policymakers. Right. You see, you watch the Facebook hearings on Capitol Hill. I mean, it was a joke. It was concerning right? I mean, anybody with a technology background to be concerned about what they saw there, and it's not the lawmakers fault. I mean, you know, we've got to empower them with that. And so we got to take technologist, threw it out, how to get them to talk policy and get them up on the hill and in the administration talking to folks, right? And one of the big outcomes, I think, has to come out of that conversation. What do we do about national level cybersecurity, Right, because we assume today that it's the rule. The private sector provides cyber security for their own companies, but in no other circumstance to expect that when it's a nation state attacker, wait. We don't expect Target or Wal Mart or any other company. J. P. Morgan have surface to air missiles on the roofs of their warehouses or their buildings to Vegas Russian bear bombers. Why, that's the job of the government. But when it comes to cyberspace, we expect Private Cummings defending us everything from a script kiddie in his basement to the criminal hacker in Eastern Europe to the nation state, whether Russia, China, Iran or North Korea and these nation states have virtually a limited resource. Your armies did >> sophisticated RND technology, and it's powerful exactly like a nuclear weaponry kind of impact for digital. >> Exactly. And how can we expect prices comes to defend themselves? It's not. It's not a fair fight. And so the government has to have some role. The questions? What role? How did that consist with our values, our principles, right? And how do we ensure that the Internet remains free and open, while still is sure that the president is not is not hampered in doing its job out there. And I love this top way talk about >> a lot, sometimes the future of warfare. Yeah, and that's really what we're talking about. You go back to Stuxnet, which opened Pandora's box 2016 election hack where you had, you know, the Russians trying to control the mean control, the narrative. As you pointed out, that that one video we did control the belief system you control population without firing a shot. 20 twenties gonna be really interesting. And now you see the U. S. Retaliate to Iran in cyberspace, right? Allegedly. And I was saying that we had a conversation with Robert Gates a couple years ago and I asked him. I said, Should we be Maur taking more of an offensive posture? And he said, Well, we have more to lose than the other guys Glasshouse problem? Yeah, What are your thoughts on? >> Look, certainly we rely intimately, inherently on the cyber infrastructure that that sort of is at the core of our economy at the core of the world economy. Increasingly, today, that being said, because it's so important to us all the more reason why we can't let attacks go Unresponded to write. And so if you're being attacked in cyberspace, you have to respond at some level because if you don't, you'll just keep getting punched. It's like the kid on the playground, right? If the bully keeps punching him and nobody does anything, not not the not the school administration, not the kid himself. Well, then the boy's gonna keep doing what he's doing. And so it's not surprising that were being tested by Iran by North Korea, by Russia by China, and they're getting more more aggressive because when we don't punch back, that's gonna happen. Now we don't have to punch back in cyberspace, right? A common sort of fetish about Cyrus is a >> response to the issue is gonna respond to the bully in this case, your eggs. Exactly. Playground Exactly. We'll talk about the Iran. >> So So if I If I if I can't Yeah, the response could be Hey, we could do this. Let them know you could Yes. And it's a your move >> ate well, And this is the key is that it's not just responding, right. So Bob Gates or told you we can't we talk about what we're doing. And even in the latest series of alleged responses to Iran, the reason we keep saying alleged is the U. S has not publicly acknowledged it, but the word has gotten out. Well, of course, it's not a particularly effective deterrence if you do something, but nobody knows you did it right. You gotta let it out that you did it. And frankly, you gotta own it and say, Hey, look, that guy punch me, I punch it back in the teeth. So you better not come after me, right? We don't do that in part because these cables grew up in the intelligence community at N S. A and the like, and we're very sensitive about that But the truth is, you have to know about your highest and capabilities. You could talk about your abilities. You could say, Here are my red lines. If you cross him, I'm gonna punch you back. If you do that, then by the way, you've gotta punch back. They'll let red lines be crossed and then not respond. And then you're gonna talk about some level of capabilities. It can't all be secret. Can't all be classified. Where >> are we in this debate? Me first. Well, you're referring to the Thursday online attack against the intelligence Iranian intelligence community for the tanker and the drone strike that they got together. Drone take down for an arm in our surveillance drones. >> But where are we >> in this debate of having this conversation where the government should protect and serve its people? And that's the role. Because if a army rolled in fiscal army dropped on the shores of Manhattan, I don't think Citibank would be sending their people out the fight. Right? Right. So, like, this is really happening. >> Where are we >> on this? Like, is it just sitting there on the >> table? What's happening? What's amazing about it? Hi. This was getting it going well, that that's a Q. What's been amazing? It's been happening since 2012 2011 right? We know about the Las Vegas Sands attack right by Iran. We know about North Korea's. We know about all these. They're going on here in the United States against private sector companies, not against the government. And there's largely been no response. Now we've seen Congress get more active. Congress just last year passed to pass legislation that gave Cyber command the authority on the president's surgery defenses orders to take action against Russia, Iran, North Korea and China. If certain cyber has happened, that's a good thing, right to give it. I'll be giving the clear authority right, and it appears the president willing to make some steps in that direction, So that's a positive step. Now, on the back end, though, you talk about what we do to harden ourselves, if that's gonna happen, right, and the government isn't ready today to defend the nation, even though the Constitution is about providing for the common defense, and we know that the part of defense for long. For a long time since Secretary Panetta has said that it is our mission to defend the nation, right? But we know they're not fully doing that. How do they empower private sector defense and one of keys That has got to be Look, if you're the intelligence community or the U. S. Government, you're Clinton. Tremendous sense of Dad about what you're seeing in foreign space about what the enemy is doing, what they're preparing for. You have got to share that in real time at machine speed with industry. And if you're not doing that and you're still count on industry to be the first line defense, well, then you're not empowered. That defense. And if you're on a pair of the defense, how do you spend them to defend themselves against the nation? State threats? That's a real cry. So >> much tighter public private relationship. >> Absolutely, absolutely. And it doesn't have to be the government stand in the front lines of the U. S. Internet is, though, is that you could even determine the boundaries of the U. S. Internet. Right? Nobody wants an essay or something out there doing that, but you do want is if you're gonna put the private sector in the in the line of first defense. We gotta empower that defense if you're not doing that than the government isn't doing its job. And so we gonna talk about this for a long time. I worked on that first piece of information sharing legislation with the House chairman, intelligence Chairman Mike Rogers and Dutch Ruppersberger from Maryland, right congressman from both sides of the aisle, working together to get a fresh your decision done that got done in 2015. But that's just a first step. The government's got to be willing to share classified information, scaled speed. We're still not seeing that. Yeah, How >> do people get involved? I mean, like, I'm not a political person. I'm a moderate in the middle. But >> how do I How do people get involved? How does the technology industry not not the >> policy budgets and the top that goes on the top tech companies, how to tech workers or people who love Tad and our patriots and or want freedom get involved? What's the best approach? >> Well, that's a great question. I think part of is learning how to talk policy. How do we get in front policymakers? Right. And we're I run. I run a think tank on the side at the National Institute at George Mason University's Anton Scalia Law School Way have a program funded by the Hewlett Foundation who were bringing in technologists about 25 of them. Actually. Our next our second event. This Siri's is gonna be in Chicago this weekend. We're trained these technologies, these air data scientists, engineers and, like talk Paul's right. These are people who said We want to be involved. We just don't know how to get involved And so we're training him up. That's a small program. There's a great program called Tech Congress, also funded by the U. A. Foundation that places technologists in policy positions in Congress. That's really cool. There's a lot of work going on, but those are small things, right. We need to do this, its scale. And so you know, what I would say is that their technology out there want to get involved, reach out to us, let us know well with our partners to help you get your information and dad about what's going on. Get your voice heard there. A lot of organizations to that wanna get technologies involved. That's another opportunity to get in. Get in the building is a >> story that we want to help tell on be involved in David. I feel passion about this. Is a date a problem? So there's some real tech goodness in there. Absolutely. People like to solve hard problems, right? I mean, we got a couple days of them. You've got a big heart problems. It's also for all the people out there who are Dev Ops Cloud people who like to work on solving heart problems. >> We got a lot >> of them. Let's do it. So what's going on? Iron? Give us the update Could plug for the company. Keith Alexander found a great guy great guests having on the Cube. That would give the quick thanks >> so much. So, you know, way have done two rounds of funding about 110,000,000. All in so excited. We have partners like Kleiner Perkins Forge point C five all supporting us. And now it's all about We just got a new co CEO in Bill Welshman. See Scaler and duo. So he grew Z scaler. $1,000,000,000 valuation he came in to do Oh, you know, they always had a great great exit. Also, we got him. We got Sean Foster in from from From Industry also. So Bill and Sean came together. We're now making this business move more rapidly. We're moving to the mid market. We're moving to a cloud platform or aggressively and so exciting times and iron it. We're coming toe big and small companies near you. We've got the capability. We're bringing advanced, persistent defense to bear on his heart problems that were threat analytics. I collected defence. That's the key to our operation. We're excited >> to doing it. I call N S A is a service, but that's not politically correct. But this is the Cube, so >> Well, look, if you're not, if you want to defensive scale, right, you want to do that. You know, ECE knows how to do that key down here at the forefront of that when he was in >> the government. Well, you guys are certainly on the cutting edge, riding that wave of common societal change technology impact for good, for defence, for just betterment, not make making a quick buck. Well, you know, look, it's a good business model by the way to be in that business. >> I mean, It's on our business cards. And John Xander means it. Our business. I'd say the Michigan T knows that he really means that, right? Rather private sector. We're looking to help companies to do the right thing and protect the nation, right? You know, I protect themselves >> better. Well, our missions to turn the lights on. Get those voices out there. Thanks for coming on. Sharing the lights. Keep covers here. Day one of two days of coverage. Eight of us reinforce here in Boston. Stay with us for more Day one after this short break.

>> Announcer: Live from Miami Beach, Florida It's theCUBE! Covering UiPathForward Americas. Brought to you by UiPath. >> Welcome back to South Beach everybody. You are watching theCUBE, the leader in live tech coverage. I'm Dave Vellante, Stu Miniman is here. This is UiPathForward Americas. UiPath does these shows all around the world and they've done, I don't know how many. But they've reached 14,000 customers this year. But Bobby Patrick knows, he's the CMO of UiPath. Bobby, great to see you again. >> It's great to be on again. >> So, how many of these events have you done in the last 12 months? >> We've probably done a dozen, all major cities. We still have Beijing and Dubai coming up. Over 14,000 people at our events alone. We go to a lot of other industry events obviously, but yeah, at our own events, every single event we break our records. We're always undersizing our events, it drives everyone nuts. >> You're always riding the wave, Bobby. You hit Cloud, right as the wave was building. How did you find this company? >> Yeah, so I was the HP of Cloud, they were, split assets off and took a little time, got a call and robotic process automation. Of course, I thought of physical robots. I look online and say wow that's interesting. I did some search terms on it and I saw RPA kind of sky rocketing in search and my background is actually in integration, data integration before Cloud. And then I met Daniel and I fell in love with Daniel and this was a year ago. I was employee 270, right? We'll have 2,000 by the end of the year. So, it's been everything I expected which was a rocket ship, has completely, constantly I've underestimated, it's amazing. >> So, you're the one who turned me onto this whole space. You sent me the Forrester Wave, >> Bobby: Right >> Where it was last year's and you guys were third this year, you leapfrogged into first. >> Bobby: Right. >> And then we said wow that's kind of cool. Let's download this and play with it. And we tried to download the other ones but we couldn't. You, know it was kind of too complicated. They wanted us to talk to resellers and, it was like, no no no. you guys were, like, really open. >> Bobby: It's part of our culture. >> And we found it super simple to use. It was, one of our guys wasn't a coder. Smart dude, but it was low code, no code type of situation. You were explaining to me at Legal Seafoods last week that you actually have written some automations. So, it's pretty simple to get started but there's a spectrum, right, and it's pretty powerful too. >> Yeah, it's an epiphany that hits everybody. This is the part where I see it, even in myself, when I realized every morning I was getting up and going to Google Trends and I was looking at us versus Automation Anywhere versus Blue Prism and we're pulling away. It's great, I'll get happy in the morning and I'll screen shot it and then I'll go to Slack and send it to the comp team. Why am I doing this? So, in 20 minutes now I have a robot everyday, every morning that does it for me. And I get a text and I get an email. We have, in marketing, a dozen of these. I've got one that does our Google Ad Words around the world. I've got one that takes all of our 30,000 inbound new contacts a month, in different languages, translates, finds out what country they are in, and routes them to the right country. These are simpler examples, but once you realize that anything you do that's routine and mundane that a robot can do for you. It brings, it makes you happy first of all, right? And you realize the vision we have for a robot for every person, its a very realistic vision and its two, three years out. >> Bobby, one on the things that has really interested me today is talking about what this means for jobs and careers. Dave and I were at Splunk earlier this week, talking about Splunkers, data is at the center of what they do and everybody comes to them, how do I leverage my data? I did operations for a bunch of my career and I'd spend lots of time with my team saying, what do you hate doing, what are you manually doing? What can you get rid of and there's a collaboration between, I hear, that your customers. It's not just oh some consultancy comes in and they cut something away and they took it away from you. Oh no wait, you're actually involved with this, it seems like an ongoing process and you're making people's jobs better. Can you talk a little about that dynamics of how this transforms a company? The vision for, I hear from UiPath, is that you're going to change the world. >> Yeah, so you have to sit in, you're talking about the future of work, or digital, you have to sit in a conference room and watch a bunch of workers sit around and I'll give you an example. At DISA, big federal government agency, federal government has lifetime workers, right? In the room, where 30 workers, who everyday download assets and then they compile them and then they analyze them. They have their best, fastest kind of human go against the UiPath robot that they automated. In 15 minutes, the human downloaded two assets or archives and the robot did 17. The entire room of 30 cheered! Cheered. No longer do we have to do that crap ever again. And this is, we see this in every industry. It's so much fun because you see just, people just radiating with excitement, right? Because, I was out with a customer today that says they can't even fulfill today with the humans they have, the 25% of the work they got. So, your robots are creating capacity, they're filling the void. You probably heard about Japan, right, and the aging population? And RPA and UiPath addressing suicide rates. This about making society better. This is about robots doing the work that we hate, right? One of our great customers, Holly Uhl from State Auto, said on stage that, you know, robots do the work nobody misses. And, I think that's trivial. Now what about job impacts, right? So, we worry everyday about what this means, right? So, we spend a lot of time on our academy, making it easier to train people, build digital era skills. We announced our academic alliance, right? We hired an amazing Chief of Learning Officer. You saw Tom Clancy. You know him and his team. We're going to train a million students in three years. You know, we're worried about the middle class. We're worried about people who are farther along in their careers and helping them re-skill. So, we take that as a part of our job as a company to figure out how to up-skill people and make them a part of this. And I'm really excited because a year ago when I joined, everybody said, the big problem you have is people going to worry about taking away jobs. I don't hear that from the 1500 customers in here today. >> Well, isn't a part of that re-skilling? Learning how to apply automation, maybe even learning how to apply RPA? Maybe even doing some automation? >> Yeah, so obviously there is-- World Economic Forum came out two weeks ago with a study that said, automation will add net 60 million jobs, I think that was for the people that losses, it will two x gains in jobs. Now those are different jobs in some cases. Some of those jobs are digital era skills, some of those jobs are AI, data science. So, I think that there's... But there are some cubicle jobs that will be affected, right? There are some swivel chair jobs that will be affected, but no different than when they automated toll booths, right? Or automated different parts of mundane work that we've all seen throughout our lives, right? So I think the speed at which this is happening is what worries people. Unlike, in the past, it took a little longer for automation or industrialization to impact jobs. But we're focused on this, right? We're going to put money towards this and we're just not seeing that today. Maybe it's because the economy is doing so great. People have a workforce shortage, but we're just not hearing it. >> Well, I mean, maybe a number of factors. I mean, there's no question, machines have always replaced humans. This is the first time in history of replacing humans in cognitive functions. >> Bobby: Augmenting >> Yes, absolutely, but It does suggest that there's opportunities for whether it's for education, you guys are investing there, training, and re-skilling whether it's around creativity and that's really where the discussion, in our view anyway, should be. Not about, okay lets protect our future, the past from the future. You don't want to just repave the cow path and use another bromide. You got to move forward and education is a key part of that. And you guys are putting your money where your mouth is. >> Yeah, we are and I think our academy that we launched a little over a year and a half ago has a quarter of a million people in it. They are already diplomas on LinkedIn. I watch everyday, people post their new diplomas, the different skills they've earned, right? Go through the courses, it's free. Democratization runs at the heart of this company, it's why we're growing so much faster than at automation anywhere, right? It's why we are a different kind of company. They're a very commercial minded kind of company. They're a marketplace, you have to be a customer. If your URL when you type in your email isn't a customer, you can't go to their store and do anything. We're free, open, share your automations and it's a very different mindset and community runs at our heart. If you're a small business, you know, under a million dollars, you get to use our software for free. And you can run your robots and we have one of our orchestrators run a manager. So, I think all of this is helping get companies and people more comfortable with our technology. There are kids and students now, we had University of Maryland up here. The professor, he's building whole classes now at the University of Maryland. All in the business school, all using our technology. Every student should have a robot, through their entire career, through their entire time at University of Maryland. That's every university, this is going to go so fast, Dave and Stu, so fast. And when I think back again, a year ago, I mean next year when we do this again, right? At our big flagship event, at three or four thousand people, you'll have felt that progression but the year I've been here, it's night and day already. >> Alright, so Bobby you know we're big fans of community. The open source stuff, you've for a long background in that. Help us put together some of these stats here. When I looked in your keynote, you said there's 114,000 certified RPA developers out there across the globe. 139 countries, 250,000 people have downloaded. You've only got at UiPath about 2,000 customers. So, you know, we talk business model and how your business grows, the industry grows, you know? Help us understand that dynamic. >> These are going to go exponential. So, we have large companies now that are committing to deploy UiPath to every employee. Every employee becomes a user then, so you're going to see that user number go like this. While the enterprise customer number goes like this. We're adding six new customers a day right now. The real opportunity for us is every one of our customers, very few are down their journey like an SMBC is. SMBC, RPA is in their annual reports, right? They say 500 million dollars already, right? It's a societal thing. They actually in Japan share together, to help each company. Here, in the U.S., we're a little competitive, right? Banks don't share with other banks typically, right? But, this is kind of what we're driving. It's, when you make an automation at UiPath. While we're not open source as a platform, the automation is open source. You put it on go, I can take that, you can take that. I had the same kind of problem. Put in the studio right away, modify it a bit and you're good to go. Now you've sped your implementation which is already fast by 70, 80, 90%. This is, we're just getting started. So, you're going to see companies adopting across HR, across supply chain, contact centers, you know. Today we're, for the most of our customers we're in one division. So, the opportunity to grow within a company, where we were barely 5% penetrated in our biggest client. >> And you've seen my prediction. A lot of the market forecast are under counting this space. >> Bobby: Right. >> There is a labor shortage, a skilled labor shortage There's more jobs than there are people to fill them. They don't have the right skills today. There is a productivity problem >> Bobby: Right. >> Productivity line is flat. RPA is going to become a fundamental component of digital transformations. It's about a billion dollar business today. I got it pegged at 10X by 2023. >> Craig at Forestry upped his guidance today, he may have told you all, to a 3.3 billion dollar market in 2021. Now I was a little disappointed, it was 2.9 before. I think he's still way under shooting it. But nevertheless, to grow 10% in one year, in his mind, is still pretty big. >> Yeah, a lot of those market forecasts are kind of linear. You're going to see, you know, an S curve, like growth in this market. I think there's no question about it. Just, in speaking to the customers today, we've seen this before in other major industry trends. We certainly saw it at ServiceNow, we saw it at Splunk, we saw it at Tableau. UiPath feels like a very similar vibe here. In Tenex, when we did the show here. I just feel an explosion coming, I already see it. It's palpable. >> One other reason for the explosion which is a little different than say most of the open source tech companies is that they were in IT sales. You don't have to use code to automate your tasks, right? The best developers for us are actually the subject matter experts in finance, in supply chain, in HR. So suddenly we've empowered them. Because IT everywhere is constrained, right? They're dealing with keeping systems current. So suddenly this these tools of software is available to any employee to go learn and automate what they do. The friction we've removed between business have to go to IT, IT be understaffed, IT have to get the requirements. All that's gone! So you create robots overnight, over the weekend. And make your life better. Again, most of the world still does not understand what's going on. I mean you can feel it now. But it's an epiphany for anyone when they see it. >> Well the open mindset that Daniel talked about today, he said, you know our competitors are doing what we do and that's okay. The rising tide lifts all boats kind of thing. That puts pressure on you guys to stay ahead of the pack. Big part of what Tom Clancy is doing is the training piece. That's huge. Free training. So you got to move faster than the market. You're confident you can do that. What gives you confidence? >> I think, one, is our product is simpler to use. So I think, you know, you go to Automation Anywhere and you need the code, right? You don't have to code with our design tool. We're told, we're about 40% faster to implement. And that's, look at the numbers. We shared our numbers again today. 100 million we announced in July 1st, for our first half of in ARR, 140 now, right? We are telling our numbers, we're open and transparent. Our competitors, well Blue Prism is public, right? We know they're growing slower. Another difference is the market, requirements are not created equal. Blue Prism only works in an unattended robot fashion, only in the back office. So, if you have front office automation, with call centers and customer service, they don't have the concept of an attended robot. You know, this idea of so, they lack the ability to serve all the requirements of a customer. I, think, it's just architecturally, I think what we're seeing in terms of simplicity and openness. And then market coverage very different then either Automation Anywhere or BluePrism. >> Alright Bobby, let me poke at something. So, if I look at, you came out this morning and said accelerate everything. One of the concerns I have is say okay, if I take existing processes, a lot of the time if you look at them, they're not ideal. They were manual in nature, it's great to do that but, how much do you need to wait and revisit and get consultants in to kind of fix things rather than just say oh okay. Faster is better for some things but not necessarily for all things unless you can make some adjustments first. >> You don't want to automate a bad process, right? So, we're not encouraging anyone to do that. So, you see a combination of... One thing about RPA is which great, is you don't have to go in and say, I'm going to go do procure to pay like Traditional IT guy. And so you can go into that process and say, oh look at all these errors, these tasks, these sub processes, these tasks. Where this huge friction and you can go automate that and get huge value. >> Almost like micro services. >> Yes, exactly. You're able to go in and that's really what people are doing. On the more ambitious projects, they're saying I'm also going to go optimize my process, think differently. But the reality is, people are going in, they're finding these few parts of a bigger process, automating it, getting immediate outcomes, immediate outcomes. And paying back that entire project in six months, including the fees on extension or PWC or other. That doesn't exist anywhere in technology. That kind of, you know, speed to an outcome and then payback period. It just doesn't exist. >> Well, the fact that the SIs are here. Yeah, we heard 15 day payback today. Super fast, ROI. The fact that the big SIs are here, especially given the relatively early days says a lot about the potential market size. I always joke, those guys like to eat at the trough. This is big business and it's important for you guys because they're strategic, they're at the board level. You need the top down support, at the same time, it sounds like there's a lot of bottom up activity. >> Bobby: Right. >> And that's where the innovations going to come from. What's next for you guys, you taking this show on the road again? >> Right, so the next Forward is in London. So, we had one in Europe and one in the U.S. We do what we call togethers, which is more intimate. Or all around the world, which are country specific or industry. I mean, we're going to go and call it the Automation First Tour. And we're going to go start our next tours up all through next year. Hit all the cities again, probably three times this size, each city. You know, I looked at Washington D.C. with federal government, we started federal government in January. Federal government for us next year should be a 60 million software business. For our partners, give them 6, 8, 10X on services on top of that. That's meaningful, that's why you see them here. That same calculation exists in every vertical and in every country. And so it's good for our partners. It's great, we want them to focus on building their skills though. Getting good skills and quality. So, we do a lot with them. We host a partner Forward yesterday with 500 partners, focusing on them. Look, we are investing in you, but you got to deliver quality, right? So, I think we amplify everything we did this year because it worked for us well. We amplify it big time and Forward in a year from now, whether it's Vegas or Orlando or we'll announce it soon, willl be substantially larger. >> Well, any company that's digitally transforming is going to put RPA as part of that digital transformation. It's not without its challenges but it's a tailwind. You better hop on that wave or you going to end up driftwood as Pat Gelsinger likes to say. Bobby, thanks so much. >> Bobby: Thank you Dave. >> Thanks for having us here. This has been a fantastic experience and congratulations and good luck going forward. >> Thank you. >> Alright guys, that's a wrap from here. This is theCUBE. Check out theCUBE.net Check out SiliconeANGLE.com for all the news. Cube.net's where all the videos are, wikimon.com for all the research. We are busy Stu, we're on the road a lot. So again, look at the upcoming events. Thanks for watching everybody. We'll see you next time.

>> Live from Washington DC, it's theCUBE. Covering the AWS Public Sector Summit 2018. Brought to you by Amazon Web Services and its ecosystem partners. >> Welcome back to to the home of the Stanley Cup Champion Washington Capitals. You're watching theCUBE's exclusive coverage of AWS Public Sector Summit 2018. I'm Stu Miniman, and my co-host John Furrier. Welcome to the program. Bret Dennis who's the head of product management with Helio campus. >> Thank you. >> Thanks so much for joining us. >> Go caps, thank you very much, appreciate it. >> Really bringing that [Inaudible] of having won the cup, lots of celebration, and there's a lot of energy here at this show. So we're heading into day two, what's your ... How do you feel about the show so far? >> It's good, it's been good. I did the Edstart program earlier in the week, and we did a sales pitch competition for startup Edtechs, so it's been really exciting, lot's of fun things going on. >> We've loved talking to startups here on theCUBE. I've talked to a number of companies, cyber-security, it's like, "Oh, okay, wait, which agency did you come out of." because of the NSA and the like. You have a similar story coming out of the University of Maryland >> Right. >> Give us a little bit of background on Helio campus. >> So we were spun out in 2016 from the University college. The Maryland board of regents had recognized the value that we'd brought to the University, over about six years of development in terms of the technology platform and the services we were bringing to the University and decided this would be really useful to other Universities, so let's spin it out into a company and go to market, and that's what we've been doing for the last two years. So it's been very exciting. >> Tell me about the product? What does it do? I mean obviously you guys incubate it in the college, so there's equity arrangements, you got a grant. Tell the story about the funding and then now, as you expand, what's that plan look like and how does Amazon fit into the whole mess? >> So we had an initial grant from the board of regions from the state of Maryland, and the idea was to assist colleges and Universities, to help them ask and answer their most pressing questions, but using data, and in order to effectively do that we wanted to bring a full solution that included platform technology as well as a services approach. So we're using Amazon Web Services and the Redshift database and platform to collect data from Universities, and then we have a services team that works with Tableau dashboards to not only help visualize data in meaningful ways, but also to explore how different data sets can be cross-seeded together across the student life cycle. >> Whose the user for you guys? Obviously big data analyst is awesome, we're seeing that clearly as one of those things where it's completely changing businesses >> Sure. And getting these kinds of insights that are actionable and different. Sometimes new questions can be answered. Who's the buyer, who's the user, how is that working? >> So institutional research is a key stakeholder for us. They are traditionally seen as the data owners of Universities and colleges, do most of the research, do most of the numbers crunching, but our idea is that we want to really democratize access to data to enrollment managers, to admissions managers, even to financial managers that want to have their own power to explore and interrogate the data, but do it in such a way that's a very intuitive process, so they don't have to be SQL query writers or really hardcore database developers. We're trying to get to those functional types of users to give the access to data >> So business users basically who don't have to be a data scientist to know Python and wrangle data, you're thinking about more of like turning them into analysts on the fly. >> We want them to be able to ask and answer their own questions without needing the technical skills. Now that's precisely why we bring the services in, so if they decide I really want to use a predictive, algorithmic approach to forecasting, or to admissions modeling, and we have data scientists available to provide that services level on top of the platform. >> Wondering if you might be able to give us an example, either generically, or if you can mention a specific company, just to help illustrate how they're transforming the use of data. >> So we work with the system at the system level for the University of North Carolina. So they had a need where they had done a lot of work on building up base data extracts of their own, but they needed a way to get that data out to campuses in a more effective way using rich visualizations. So we won an RFP with them and were able to help them, not only at the system level, but also at the campuses to make sure that the campuses and the board of regents and the board of governors are getting the data that they need, to again, understand what are my patterns and trends for success. What are specific student populations that we want to help, and we want to use data to help get to those insights. So that's been a real success story for us. >> Talk about the public sector impact of Amazon, obviously Amazon's well known in the startup community, you can spin up a server, that kind of changed the whole provisioning of a data center, now they got large enterprises doing all kinds of stuff, taking databases from big Oracle systems. But public sector, certainly education, we've seen community colleges, all the way up to premier institutions like the University of Maryland, this is now a game changer. So how are you seeing that evolve in other universities? What are your peers doing? What's their mindset? Where are they on the progress bar using cloud, if you will, cloud native, are they thinking microservices, are they thinking about [Inaudible], are they thinking about containers, where are they on the evolution? >> Yeah it is a game changer, and it is because scalability and security are probably two themes that I would bring up. So regardless of the amount of data that you want to use as part of the analysis, there's no limit in terms of using AWS and performance, from a performance perspective, if we want to bring in a new data set, test it, see if there is correlation, see if it's useful in helping answer their key questions, we can do that. But also it goes with out saying, the security, so we don't really have to do a lot of selling in terms of the security of AWS because the level of approvals and the level of certifications at AWS far exceeds beyond what any University could get on their own, or what any vendor individually could do on their own. So that's a natural benefit that comes with a platform. >> What other features or services in AWS are important for what you build, obviously, scalability, security, kind of a given when you talk about AWS. >> The Redshift platform has been really useful to us. The way that we architect our model is that we use Tableau on the front-end for BI, but also any user could have access at the database level and go into Redshift, now we supply security models so that only authorized users can get to that. So it's very helpful to have the security model on top of it, but the Redshift data structure really enables us to provide that experience at any level depending on what the need is of each user. So not many functional users would be going to that level, but Redshift really enables us to have the technical users and the traditional SQL query writers, and the ones that are doing the cross-seeding of the data to have access at that level. >> It's interesting you have a services model built in because it kind of makes sense because one of the benefits of the cloud, obviously, is speed. You get performance, just raw performance, but also speed to value, so you don't have to do a lot of heavy lifting to kind of understand where the value points are. So how does that change the services speed because Amazon's constantly introducing new services, how are you seeing that evolve? Because you can do some heavy lifting, okay here's a data set, is that the way the services are? How is the services changing with cloud? >> So our services model is really to hire individuals from Universities that have the subject matter expertise. So we have x directors of institutional research, x admission officers, so from our perspective we want to leave the technical, the platform, the architecture, the security services to the experts in that realm, that's not what our Universities are asking us for. They want to know how can you bring us subject matter expertise in the functional areas where we're struggling, we want to not have to worry about the technical piece at all. So I think that's where, from a cloud perspective, we're able to rely on the expertise at AWS and Amazon where, again, we're not having to worry about that and we can focus squarely on what the institutional needs are. >> So you're more efficient? >> I think so, yeah. >> You don't spend your time doing a lot wrangling of tech, standing up anything, just pretty much turnkey on the cloud side, focused on getting the users up and running with the tools that you guys have. >> Exactly, and we've had instenses where institutions have asked, "Oh, we want to do this research project, we need additional space." We can turn that up instantly through the value of the services provided through Amazon, which if we were to do that on our own it would be very expensive and a manual process. >> You can actually deliver services that values to the customer. I got to ask you a question, now looking forward, where's the head room? If you look at your business and how it's evolving, what's the head room that you see coming down the road that you're going towards, that you're going to bring to you customer base. >> Right, so with evolving technologies that we all know the buzzwords about, AI and machine learning, sort of taking the data science to the next level. I think that's what eventually we'll be asked to do, is to look at, "Well how can these be brought into education in a meaningful way? How can they provide us insight in ways that we're not doing today, again, more efficiently. We also value time or accelerating time to value, so again, I think right now we're moving data around and we're shifting data, and sometimes it can take a bit of time to do that. I think in the future we'll be able to turn up customers and start delivering that time to value in a much more accelerated way. >> So you said you attended some startup activity here at the show >> Yes. and also seen quite a few Universities here, so it sounds like you're learning to help build your business as well as from the customer standpoint, why don't you give us a little bit of insight as to the value that you get out of a show like this. >> Absolutely. So when the Universities attend we have meetings and we get an understandings of where they are now, what kinds of questions are they having, that's really what we want to get to, analytics is really nothing unless you understand what problem are you trying to solve. So being able to have those meaningful conversations in this type of environment is very helpful to us to understand, again, where are you now, what is your vision for where you want to go, how can we meet that at their point of need. >> What's the low-hanging fruit for these Universities use case wise? What are they using you guys for the most, if you had to look at the patterns? >> It can be arranged, so it can be I am not able to provide my stakeholders meaningful visualizations and insights and have them use data in a more meaningful way. So instead of giving you a table of lines and numbers, I can give you something that's actually actionable. That's really where we start at the dashboard level, the more advanced institutions, and everyone we work with has smart people on their teams but they may have other projects, they may not have time, they may not have the ability to hire expensive data scientists. So from that perspective on the advanced analytic side we can help with that advanced piece with our services team. >> They can get up to speed faster. Sometimes these projects can take months to stand up. >> It is, it's the acceleration that's huge. >> Great, what's the show vibe here? If you had to describe it for the folks that didn't make it. >> Yeah. >> What's the show about this year in your mind? What's the main big story here this year? >> It' a lot like last year for me, it is understanding, and I look at it from a data perspective of course, and it really is all about new technologies, and new vendors, and how we can understand, again, how these technologies can not only make us more efficient from a time perspective and cost perspective, but again, how can we more meaningfully answer the important questions that we have. >> Alright final question. Because you're a startup kind of within a cool environment at the University, which has got a lot of resources and access to some real use case data, what's the biggest thing you've learned over the past few years? Looking at the cloud, you're right in the middle of it, cloud native is super hot, there's people born in the cloud, people migrating the cloud, all kind of different levels of cloudifying businesses, some PurePlay cloud. What is the things that you learn the most? Looking back and saying, "Okay, these are the top three things that we learned." >> So I've worked for a foreign institution as well as for a number of different vendors in this space and I think the theme that I see is I want to go buy technology, "Oh I heard I need predictive analytics, Oh I heard that I need to have machine learning", well that's great that you know that, but have you really refined what your challenges and what you're trying to solve, and that goes for any technology whether it's cloud or a new server or a new application, really need to understand what is that core challenge and that's where we always start. Like any good product manager as we spoke about earlier, you've got to start with what problem you're trying to solve and then apply your solution in a meaningful way. So I think that would be my answer for that. >> Bret, thank for coming on theCUBE, thanks for sharing your story >> Thank you. Appreciate it, alright >> It was a pleasure. >> Bret Dennis here, spin out from University of Maryland, great startup doing big data analyst, obviously the clouds perfect for that and obviously creating more value. It's theCUBE bringing you the action here live in Washington D.C. I'm John Furrier and Stu Miniman. We'll be back with more coverage after this short break. (light electronic music)

>> Live from Washington, DC, it's TheCube, covering AWS Public Sector Summit 2018, brought to you by Amazon Web Services and its ecosystem partners. >> Welcome to TheCube, Silicon Angle Media's Production here at the NWS Public Sector show in Washington DC, I'm Stu Miniman, my host for this week will also be Dave Vellante and John Furrier, doing a day-and-a-half worth of programming, I've covered lots of Amazon ecosystem shows, happy to welcome to the program first-time guest, and first-time on the program Dan Fallon, who's the director of Public Sector Systems Engineers at Nutanix, Dan, great to see ya. >> Thank you, Stu, happy to be here. >> Alright, so you know, you and I have known each other for a number of years. I've been at every .NEXT actually that Nutanix has has, really most of the time at Nutanix, you know, we're talking about people's data centers, but you know, we've been watching how Nutanix really went from, you know, that hyper-converged term that we through out, but now you know, the messaging is around Enterprise Cloud, the portfolio has definitely expanded, as have the partnerships. Give us, Dan, why Nutanix is at the show, and a little bit about your role at the company. >> Yeah, yeah. So, I lead our public sector technical groups systems engineering, so we have all our government business, state, local, and federal, rolled up into one group. So, local show for me in the DC area, and this is our second year attending the Public Sector Summit, so you know, last year it was after our Calm acquisition, we're really starting to step into the space of, I'd say, solving the cloud problem for organizations, and blending your on-prem environment into your public cloud. So, that was you know, kind of our focus last year when the marketing team and we kind of get together, and figure out what shows we're at, we're like "Let's do, you know, AWS", it was kind of a new one, we're like "Alright, we'll be good." I would say it was a hit last year, and then this year, you know, we made some additional acquisitions, and now it's at our large .NEXT conference, and really focusing on Beam and cost optimization. >> Dan, I remember back a couple of years ago, people would, you know, knock on Nutanix, they're like "Ah, they're just VDI, and really, they only work on the government sector." You know, it's like federal is like a big thing, cause they can get to a certain price point that, you know, some person can sign off on, and we're like "Um, government's pretty, you know, pretty impressive segment." You know, you look at this show, I hear we're expecting about 10,000 people, which is typical for these regional shows, but this is more than that, the Public Sector, so tell us a little bit about your customers, and love to hear you talk about what use cases they are, and how they think about cloud, and look at Amazon, and look at Nutanix and how that fits for them. >> Yeah, and I actually just heard from our director of marketing here that it's approaching 14,000, so they're blowing up the attendance. Yeah, and I mean, definitely government is unique, that's kind of why we have it divided into a vertical, and Nutanix was very early on in the federal, and unlike a lot of startup small companies, instead of running away from the additional security burden, the compliance requirements, the leadership, Dheeraj, leaned into it. They said "Alright, let's build out our federal team, let's go our and do common criteria compliance.", some certifications that cost a lot of money. So they really, you know, leaned into that, and helped the organization grow in federal, and that kind became our beach head, and then obviously Nutanix has just grown around the world since then, but across public sector, really a couple different verticals. They actually combined the government units about a year ago, now, so I'm getting more and more familiar with the state and local business, as well as the education, and you can kind of look at those as three separate verticals, and then my kind of background is federal, I've been here doing contracting consulting work for the federal government, and now Nutanix. So, they all kind of have a different spin. In the federal government, since we're in DC, start there first. Really big focus on data center optimization, and Cloud First mandates, so you know, I get into discussions, cause there's really a larger conversation to be had on, like, what is cloud. A lot of people see it as a destination, but really they have scorecards that they need to close, consolidate data centers, and part of that involves moving to the cloud, part of that involves just refactoring their on-prem, and you know, could be hyper-converged, just really getting to a better optimized state in their on-prem data centers. >> Yeah, and one thing I like is when you talk to customers, they don't get into these arguments over, like, "Well, what is a private cloud? How do I measure these public clouds?" They're like "Yes, I have a cloud strategy", and you're right, the government has certain, here's the criteria we need to follow, here's the services you can buy, you know, I'm sure they've got GSA contracts for lots of different things that they can buy off of, but Nutanix has a tool that you're talking about at the show called Beam, why don't you explain how that fits into helping customers understand, you know, what applications they put where, and how they manage their entire infrastructure. >> Yeah, and I think whenever I get into those conversations with cloud, I always like to understand "Alright, why cloud, why are you moving into cloud?", and a lot of times it is higher-level mandates, you know, that there's a presidential memo, there's a new, you know, so there are laws they have to follow in terms of optimization of the data center, but if you peel it back, there are, you know, agility, and getting rapid time to market, but the cost is a big thing, and a lot of times because of those mandates, the cost kind of has to be a second factor, and so it might end up being more expensive because they're not really taking that into consideration. Cause, they're being told to go, so when Nutanix launched Beam at .NEXT, I really see it as a very good play in the public sector space, because I hear agencies kind of get the bill after the fact, and then they have this shock of like "Well our budget for cloud spend this year is going to be eaten up in our first couple months, you know, based on this first bill." So, with Beam, we have a lot of governance and cost control, but also the budgeting aspect, which I think will be huge in government, cause they have a fixed budget, they're not as used to doing things opex, they're very capex minded, so the cloud spend, they kind of have to change how they're thinking, and beam gives them that budget analysis so they can say "Alright, I'm going to spend this much a month", and do the allocation and break it down. >> Yeah, it's funny, for people that don't work with the government, they always hear like "Oh, well they've spent, you know, $100 for a hammer, they're overspending", and on my career, I've worked with government, and you get the calls at the end of the quarter, which is like "Oh my gosh, I haven't actually used up my budget, and I better use it now or I won't get it next quarter, or next year", so, you know, cost absolutely a key concern. Maybe drill us in one down level as to, you know, what kind of things, how does Beam help them, you said understand, optimize what they have, as well as plan for the future. >> Yeah, yeah, so you know, Beam hooks into the public cloud providers, as well as your on-prem staff. There are a couple different views, we've already refactored it into the nice Nutanix UI, so that you have the same look and feel. But, you have a couple different views, you have the cost visibility view, so your spend per day, per month, per year, and then you have an analyze view. So, there's a spend efficiency view, so you can actually get a quick visualization of "Am I getting the best value out of my cloud contract?", and this is, you know, really common in government. They'll cut some type of ELA or longer-term contract, but if you're not using all those credits, or taking the best benefit, you're not getting your RLI. So the spend efficiency will help in that aspect. You know, Beam goes beyond just visibility, so you have ability to do one-click cost controls. So maybe, you know, change things from spot to reserve instances. You can also drill down into the sub-services, so "Oh, that's costing more than I thought, you know, is it my NAT service or my load balancer service, like which exact spot is taking all that cost?" And then, the budget allows you to build cost centers within your org. So, build out and you know, charge back is hit or miss in government, sometimes it's way up at the top of the command, but you know, we are seeing more and more orgs, and especially on the service provider and fed integrator side, you know, common scenario is government contract awarded to a fed integrator, and they build out a private cloud and need to do charge back. So that's another big aspect. >> Yeah, it's so funny. Remember, you know, just a few years ago it's like "Oh, public cloud, it's super easy and super cheap, and like well, when you actually dig into it, well it's different.", is I guess what they would say. Simple isn't necessarily what I would say, and cost depends on what you're doing with it and how you do it, so we talked a little bit about federal. You were telling me off camera that you were seeing a lot of SLED customers here. Give a little insight as to what are some of the concerns, what are some of the real things that, you know, that segment of public sector are looking for at this show in the ecosystem. >> Yeah, it's one reason we love doing this show, and it's a great spot that brings together, cause state and local is so regionalized, you know, 50 states and then all the different counties, and cities, and a lot of them attend here. I, you know, kind of just gotten into public sector when this show happened last year, and I met a lot of our SLED customers here for the first time, so you know, bring them all to one spot, which is rare in state and local, it's a lot more regional conferences. So, the challenge of staying local is because it's so regionalized, and then you really have four verticals within state and local, you have the state business, which kind of mirrors federal in more large enterprise. Some states are adopting Cloud First strategies, some states are kind of still figuring it out. So, some states are mirroring fed government, and they have this kind of Cloud First, and trying to figure out how to make that work. And then, at the local level, you have the county and cities, and they're very scattered on their approach. We have some significant size counties that are using Nutanix with things like CloudConnect to backup into AWS, and then I would say higher ed is probably the most forward leaning in terms of their cloud usage. A lot of higher ed pushing aggressively in the cloud. Actually, where I used to work, Maryland, University of Maryland, aggressive push there. So, they still have a lot of fragmented IT on-prem though, they have different orgs, business school, engineering school with their own kind of little IT fiefdoms, and then you have central IT trying to standardize and make more public cloud usage. So, they have a lot of the same challenges of a big enterprise, where they need to kind of get that visibility and cost control across, not only, the on-prem, but also as they move into public cloud. >> Yeah Dan, one of the things I've loved when I dig into, you know, whether it's the federal government or even the local government, how technology and IT are helping drive innovation. You know, we often think of, you know, you think about government, you know, just mired in bureaucracy, wonder if you have any, you know, customer stories you can share about, you know, fun and interesting things people are doing, you know, on top of the infrastructure transformational type of activities? >> Yeah, I mean, I think you know kind of the buzzword maybe of this year seems to be a lot around the IOT and machine learning, so it's still a lot in the pilot phases, but Nutanix, we announced Project Sherlock at .NEXT, so kind of our approach to really a PAS IOT at the edge, so PAS machine learning at the edge, and we actually just deployed our first customer on the commercial side a week ago. So, still early days, but I would say the interest at the state and local level is huge, you know, Smart City initiatives, self-driving car initiatives, and just the data is overwhelming. So, they're planning ahead, some of them are pretty far along, but there's obviously starts and stops on where these initiatives are going, but the amount of data, and it's all dispersed, and just how to get their arms around that, how to control that, and then in federal there's a lot of requests for machine learning out at the tactical edge, so we have our, you know, soldiers forward deployed, how do they take their imagery and analyze that, and not have to wait 24 hours for someone to come back from the main data center, and that's real lifesaving, game changing. For them to be able to analyze it right then and there, and also big in disaster relief scenarios, so you know, being able to analyze. I was talking to one customer we had at a CXR round table last week at our local .NEXT event, and they were talking about after the hurricanes in Puerto Rico, just how to analyze like, where's there even power, where's the water good, and overlaying all that on imagery. But, right now, that's like 15 different sources that they were trying to pull together into one system, so a lot of challenges like that, that people are trying to address. >> And I love that, Dan. I think you hit right on it. It's data at the center of it. How can I leverage it? How can I get new value out of it. I've talked to some government agencies that are like, you know "How do I transform how we do parking in a city? I have the data, the have some sensors, oh wait, we can actually make an app." Sometimes it's partnering with the commercial side and business, but other times it's government just driving these. Dan, want to give you the final word, you know, we're just kicking off the event, but you know, give us a final takeaway for Nutanix AWS here at Public Sector Summit, what you want the takeaways to be. >> Yeah, well I mean, we're here both days, I encourage everyone to stop by and talk to Nutanix, and really, Beam was just launched, so the great thing is it's our first SAS offering, which is obviously a mind shift for us, but you can demo it just by signing up. But, it's kind of you know, traditional where we've been in the infrastructure market, where we get customers that are like "Oh, I want to try it out", and you have to ship them a system, or they have to download software. Now, it's just "Oh, go sign up on the SAS offering", so I think that'll be a great new delivery vehicle for Nutanix, and I think as we kind of shape our ecosystem of not only different ways to consume with Xi Cloud Services, Beam being SAS, but also different capital models in terms of way the customers purchase. I think that's another big driver around cloud is how the finance side consumes IT, so I think it's great to see, you know, we're kind of expanding, blending into the AWS ecosystem as well, but tying it all together, so people can manage everything from one spot. >> Alright, well Dan Fallon, pleasure chatting with you this morning helping me kick things up, and absolutely, the diversity of technologies, the how we are going to purchase things changing quite a lot, everything from, you know, modernizing our data center to SAS application. You know, I remember at .NEXT I said "Modernize the platform, then we can modernize the applications on top of it", so working through its customers through changes. Alright we have, just like Dan said, day-and-a-half work of coverage here on TheCube, of course, check TheCube dot net for all the recordings, as well as all the shows we'll be at. I'm Stu Miniman, and thanks so much for watching TheCube. (techno music)

>> Announcer: Live from New York City It's TheCube. Covering CyberConnect 2017. Brought to you by Centrify and the Institute for Critical Infrastructure Technology. >> It got out of control, they were testing it. Okay, welcome back everyone. We are here live in New York City for CyberConnect 2017. This is Cube's coverage is presented by Centrify. It's an industry event, bringing all the leaders of industry and government together around all the great opportunities to solve the crisis of our generation. That's cyber security. We have Cricket Liu. Chief DNS architect and senior fellow at Infoblox. Cricket, great to see you again. Welcome to theCUBE. >> Thank you, nice to be back John. >> So we're live here and really this is the first inaugural event of CyberConnect. Bringing government and industry together. We saw the retired general on stage talking about some of the history, but also the fluid nature. We saw Jim from Aetna, talking about how unconventional tactics and talking about domains and how he was handling email. That's a DNS problem. >> Yeah, yeah. >> You're the DNS guru. DNS has become a role in this. What's going on here around DNS? Why is it important to CyberConnect? >> Well, I'll be talking tomorrow about the first anniversary, well, a little bit later than the first anniversary of the big DDoS attack on Dyn. The DNS hosting provider up in Manchester, New Hampshire. And trying to determine if we've actually learned anything, have we improved our DNS infrastructure in any way in the ensuing year plus? Are we doing anything from the standards, standpoint on protecting DNS infrastructure. Those sorts of things. >> And certainly one of the highlight examples was mobile users are masked by the DNS on, say, email for example. Jim was pointing that out. I got to ask you, because we heard things like sink-holing addresses, hackers create domain names in the first 48 hours to launch attacks. So there's all kinds of tactical things that are being involved with, lets say, domain names for instance. >> Cricket: Yeah, yeah. >> That's part of the critical infrastructure. So, the question is how, in DDoS attacks, denial-of-service attacks, are coming in in the tens of thousands per day? >> Yeah, well that issue that you talked about, in particular the idea that the bad guys register brand new domain names, domain names that initially have no negative reputation associated with them, my friend Paul Vixie and his new company Farsight Security have been working on that. They have what is called a -- >> John: What's the name of the company again? >> Farsight Security. >> Farsight? >> And they have what's called a Passive DNS Database. Which is a database basically of DNS telemetry that is accumulated from big recursive DNS servers around the internet. So they know when a brand new domain name pops up, somewhere on the internet because someone has to resolve it. And they pump all of these brand new domain names into what's called a response policy zone feed. And you can get for example different thresh holds. I want to see the brand new domain names created over the last 30 minutes or seen over the last 30 minutes. And if you block resolution of those brand new domain names, it turns out you block a tremendous amount of really malicious activity. And then after say, 30 minutes if it's a legitimate domain name it falls off the list and you can resolve it. >> So this says your doing DNS signaling as a service for new name registrations because the demand is for software APIs to say "Hey, I want to create some policy around some techniques to sink-hole domain address hacks. Something like that? >> Yeah, basically this goes hand in hand with this new system response policy zone which allows you to implement DNS policy. Something that we've really never before done with DNS servers, which that's actually not quite true. There have been proprietary solutions for it. But response policy zones are an open solution that give you the ability to say "Hey I do want to allow resolution of this domain name, but not this other domain name". And then you can say "Alright, all these brand new domain names, for the first 30 minutes of their existence I don't want-- >> It's like a background check for domain names. >> Yeah, or like a wait list. Okay, you don't get resolved for the first 30 minutes, that gives the sort of traditional, reputational, analyzers, Spamhaus and Serval and people like that a chance to look you over and say "yeah, it's malicious or it's not malicious". >> So serves to be run my Paul Vixie who is the contributor to the DNS protocol-- >> Right, enormous contributor. >> So we should keep an eye on that. Check it out, Paul Vixie. Alright, so DNS's critical infrastructure that we've been talking about, that you and I, love to riff about DNS and the role What's it enabled? Obviously it's ASCII, but I got to ask you, all these Unicode stuff about the emoji and the open source, really it highlight's the Unicode phenomenon. So this is a hacker potential haven. DNS and Unicode distinction. >> It's really interesting from a DNS standpoint, because we went to a lot of effort within the IETF, the Internet Engineering Task Force, some years ago, back when I was more involved in the IETF, some people spent a tremendous amount of effort coming up with a way to use allow people to use Unicode within domain name. So that you could type something into your browser that was in traditional or simplified Chinese or that was in Arabic or was in Hebrew or any number of other scripts. And you could type that in and it would be translated into something that we call puny code, in the DNS community, which is an ASCII equivalent to that. The issue with that though, becomes that there are, we would say glifs, most people I guess would say characters, but there are characters in Unicode that look just like, say Latin alphabet characters. So there's a lowercase 'a' for example, in cyrillic, it's not a lowercase 'a' in the Latin alphabet, it's a cyrillic 'a', but it looks just like an 'a'. So it's possible for people to register names, domain names, that in there Unicode representation, look like for example, PayPal, which of course has two a's in it, and those two a's could be cyrillic a's. >> Not truly the ASCII representation of PayPal which we resolve through the DNS. >> Exactly, so imagine how subtle an attack that would be if you were able to send out a bunch of email, including the links that said www.-- >> Someone's hacked your PayPal account, click here. >> Yeah, exactly. And if you eyeballed it you'd think Well, sure that's www.PayPal.com, but little do you know it's actually not the -- >> So Jim Ruth talked about applying some unconventional methods, because the bad guys don't subscribe to the conventional methods . They don't buy into it. He said that they change up their standards, is what I wrote down, but that was maybe their sort of security footprint. 1.5 times a day, how does that apply to your DNS world, how do you even do that? >> Well, we're beginning to do more and more with analytics DNS. The passive DNS database that I talked about. More and more big security players, including Infoblox are collecting passive DNS data. And you can run interesting analytics on that passive DNS data. And you can, in some cases, automatically detect suspicious or malicious behavior. For example you can say "Hey, look this named IP address mapping is changing really, really rapidly" and that might be an indication of let's say, fast flux. Or you can say "These domain names have really high entropy. We did an engram analysis of the labels of these". The consequence of that we believe that this resolution of these domain names, is actually being used to tunnel data out of an organization or into an organization. So there's some things you can do with these analytical algorithms in order to suss out suspicious and malicious. >> And you're doing that in as close to real time as possible, presumably right? >> Cricket: That's right. >> And so, now everybody's talking about Edge, Edge computing, Edge analytics. How will the Edge effect your ability to keep up? >> Well, the challenge I think with doing analytics on passive DNS is that you have to be able to collect that data from a lot of places. The more places that you have, the more sensors that you have collecting passive DNS data the better. You need to be able to get it out from the Edge. From those local recursive DNS servers that are actually responding to the query's that come from say your smart phone or your laptop or what have you. If you don't have that kind of data, you've only got, say, big ISPs, then you may not detect the compromise of somebody's corporate network, for example. >> I was looking at some stats when I asked the IOT questions, 'cause you're kind of teasing out kind of the edge of the network and with mobile and wearables as the general was pointing out, is that it's going to create more service area, but I just also saw a story, I don't know if it's from Google or wherever, but 80% plus roughly, websites are going to have SSL HTBS that they're resolving through. And there's reports out here that a lot of the anti virus provisions have been failing because of compromised certificates. And to quote someone from Research Park, and we want to get your reaction to this "Our results show", this is from University of Maryland College Park. "Our results show that compromised certificates pose a bigger threat than we previously believed, and is not restricted to advanced threats and digitally signed malware was common in the wild." Well before Stuxnet. >> Yeah, yeah. >> And so breaches have been caused by compromising certificates of actual authority. So this brings up the whole SSL was supposed to be solving this, that's just one problem. Now you've got the certificates, well before Stuxnet. So Stuxnet really was kind of going on before Stuxnet. Now you've got the edge of the network. Who has the DNS control for these devices? Is it kind of like failing? Is it crumbling? How do we get that trust back? >> That's a good question. One of the issues that we've had is that at various points, CAs, Certificate Authorities, have been conned into issuing certificates for websites that they shouldn't have. For example, "Hey, generate a cert for me". >> John: The Chinese do it all the time. >> Exactly. I run www. Bank of America .com. They give it to the wrong guy. He installs it. We have I think, something like 1,500 top level certification authorities. Something crazy like that. Dan Komenski had a number in one of his blog posts and it was absolutely ridiculous. The number of different CA's that we trust that are built into the most common browsers, like Chrome and Firefox and things like that. We're actually trying to address some of those issues with DNS, so there are two new resource records being introduced to DNS. One is TLSA. >> John: TLSA? >> Yeah, TLSA. And the other one is called CAA I think, which always makes me think of a California Automotive Association. (laughter) But TLSA is basically a way of publishing data in your own zone that says My cert looks like this. You can say "This is my cert." You can just completely go around the CA. And you can say "This is my cert" and then your DNS sec sign your zone and you're done. Or you can do something short of that and you can say "My cert should look like this "and it should have this CA. "This is my CA. "Don't trust any other one" >> So it's metadata about the cert or the cert itself. >> Exactly, so that way if somebody manages to go get a cert for your website, but they get that cert from some untrustworthy CA. I don't know who that would be. >> John: Or a comprimised-- >> Right, or a compromised CA. No body would trust it. No body who actually looks up the TSLA record because they'll go "Oh, Okay. I can see that Infoblox's cert that their CA is Symantech. And this is not a Symantech signed cert. So I'm not going to believe it". And at the same time this CAA record is designed to be consumed by the CA's themselves, and it's a way of saying, say Infoblox can say "We are a customer of Symantech or whoever" And when somebody goes to the cert and says "Hey, I want to generate a certificate for www.Infoblox.com, they'll look it up and say "Oh, they're a Symantech customer, I'm not going to do that for you". >> So it creates trust. So how does this impact the edge of the network, because the question really is, the question that's on everyone's mind is, does the internet of things create more trust or does it create more vulnerabilities? Everyone knows it's a surface area, but still there are technical solutions when you're talking about, how does this play out in your mind? How does Infoblox see it? How do you see it? What's Paul Vixie working on, does that tie into it? Because out in the hinterlands and the edge of the network and the wild, is it like a DNS server on the device. It could be a sensor? How are they resolving things? What is the protocol for these? >> At least this gives you a greater assurance if you're using TLS to encrypt communication between a client and a web server or some other resource out there on the internet. It at least gives you a better assurance that you really aren't being spoofed. That you're going to the right place. That your communications are secure. So that's all really good. IOT, I think of as slightly orthogonal to that. IOT is still a real challenge. I mean there is so many IOT devices out there. I look at IOT though, and I'll talk about this tomorrow, and actually I've got a live event on Thursday, where I'll talk about it some more with my friend Matt Larson. >> John: Is that going to be here in New York? >> Actually we're going to be broadcasting out of Washington, D.C. >> John: Were you streaming that? >> It is streamed. In fact it's only streamed. >> John: Put a plug in for the URL. >> If you go to www.Infoblox.com I think it's one of the first things that will slide into your view. >> So you're putting it onto your company site. Infoblox.com. You and Matt Larson. Okay, cool. Thursday event, check it out. >> It is somewhat embarrassingly called Cricket Liu Live. >> You're a celebrity. >> It's also Matt Larson Live. >> Both of you guys know what you're talking about. It's great. >> So there's a discussion among certain boards of directors that says, "Look, we're losing the battle, "we're losing the war. "We got to shift more on response "and at least cover our butts. "And get some of our response mechanisms in place." What do you advise those boards? What's the right balance between sort of defense perimeter, core infrastructure, and response. >> Well, I would certainly advocate as a DNS guy, that people instrument their DNS infrastructure to the extent that they can to be able to detect evidence of compromise. And that's a relatively straight forward thing to do. And most organizations haven't gone through the trouble to plumb their DNS infrastructure into their, for example, their sim infrastructure, so they can get query log information, they can use RPZs to flag when a client looks up the domain name of a known command and control server, which is a clear indication of compromise. Those sorts of things. I think that's really important. It's a pretty easy win. I do think at this point that we have to resign ourselves to the idea that we have devices on our network that are infected. That game is lost. There's no more crunchy outer shell security. It just doesn't really work. So you have to have defensive depth as they say. >> Now servs has been around for such a long time. It's been one of those threats that just keeps coming. It's like waves and waves. So it looks like there's some things happening, that's cool. So I got to ask you, CyberConnect is the first real inaugural event that brings industry and some obviously government and tech geeks together, but it's not black hat or ETF. It's not those geeky forums. It's really a business community coming together. What's your take of this event? What's your observations? What are you seeing here? >> Well, I'm really excited to actually get the opportunity to talk to people who are chiefly security people. I think that's kind of a novelty for me, because most of the time I think I speak to people who are chiefly networking people and in particular that little niche of networking people who are interested in DNS. Although truth be told, maybe they're not really interested in DNS, maybe they just put up with me. >> Well the community is really strong. The DNS community has always been organically grown and reliable. >> But I love the idea of talking about DNS security to a security audience. And hopefully some of the folks we get to talk to here, will come away from it thinking oh, wow, so I didn't even realize that my DNS infrastructure could actually be a security tool for me. Could actually be helpful in any way in detecting compromise. >> And what about this final question, 'cause I know we got a time check here. But, operational impact of some of these DNS changes that are coming down from Paul Vixie, you and Matt Larson doing some things together, What's the impact of the customer and they say "okay, DNS will play a role in how I role out my architecture. New solutions for cyber, IOT is right around the corner. What's the impact to them in your mind operationally. >> There certainly is some operational impact, for example if you want to subscribe to RPZ feeds, you've got to become a customer of somebody who provides a commercial RPZ feed or somebody who provides a free RPZ feed. You have to plumb that into your DNS infrastructure. You have to make sure that it continues transferring. You have to plumb that into your sim, so when you get a hit against an RPZ, you're notified about it, your security folks. All that stuff is routine day to day stuff. Nothing out of the ordinary. >> No radical plumbing changes. >> Right, but I think one of the big challenges in so many of the organizations that I go to visit, the security organization and the networking organization are in different silos and they don't necessarily communicate a lot. So maybe the more difficult operational challenge is just making sure that you have that communication. And that the security guys know the DNS guys, the networking guys, and vice versa. And they cooperate to work on problems. >> This seems to be the big collaboration thing that's happening here. That it's more of a community model coming together, rather than security. Cricket Liu here, DNS, Chief Architect of DNS and senior fellow of Infoblox. The legend in the DNS community. Paul Vixie amongst the peers. Really that community holding down the fort I'll see a lot of exploits that they have to watch out for. Thanks for your commentary here at the CyberConnect 2017 inaugural event. This is theCUBE. We'll be right back with more after this short break. (techno music)