>> Live from Anaheim, California It's the queue covering nutanix dot next twenty nineteen. Brought to you by Nutanix >> Welcome back, everyone to the Cubes Live coverage of Nutanix Next here in Anaheim, California I'm your host, Rebecca Night, along with my co host, John Furrier. We're joined by Pharma's Mahdavi. He is the senior group director Cadence Design Systems. Thank you so much for coming on the Cube. So tell our viewers a little bit about Kate, based in San Jose. Can't tell our viewers a little bit about your company Cadence Design Systems. >> So Cajuns has been a A company in the very essence about thirty years ago. So we make software to enable semiconductor companies to design test than billed chips. So most technique, you know, technology that you bought, you see, and the fries Electronics has some cadence solution. >> So you guys had a lot of legacy and you're talking about the nutanix relationship. >> So our journey with Nutanix started about three years ago. I'd actually explored Nutanix at a previous company. I've been with Cadence three and a half years. Eso liked it, but there was really no opportunity Teo do much At that time, the company was very new at the time. But I cadence, we identified some opportunities Teo to explore nutanix. And it's been a great experience so far Way actually are running a lot of our critical of business applications on nutanix. So we're all in. >> What was the door opener for? What was the door opener for you? You guys there? That cadence. What? Goddammit! >> The overall architecture look good in a presentation level s so it was worth exploring. But, you know, it's a new company. New architecture. Er you have to kind of going to it carefully. So it was a matter of identifying opportunities that were maybe not production, not super business critical to start. But as time goes on, you build confidence and you do more and more. So today we're using Nutanix. As I said for business applications were using your for VD I AA lot of ours End that stop. You know, instances are running on nutanix today. We use that as well because here zero so a lot of art shared services. You know, the n s active directory. Those sorts of services are running on his hands. So, you know, we're looking for more and more opportunities to expand it. >> So I always like to know how this actually helps you and your company. Do people do their jobs better, more quickly, more efficiently, more productively? Can you sort of walk us through what life was like before nutanix and what life is like now in terms of the staffing and the overhead and the >> star? So I would say there's a couple of different, you know, big benefits. One is we're in a cloud, uh, era, right? So a lot of companies are looking for work close to move to the public cloud, and we're no different. We're constantly looking for what? What makes sense and the public cloud. What makes sense on Prem? So from this support and skill sets, fan point is very important to be consistent. I basically have the same support model for both on Prima's well as public public cloud. So that's one big benefit that Nutanix offers because the same skill sets to support. Let's say eight lbs environment is the same as, you know, the nutanix support environment. Thie. Other critical thing is just like any ICTSI organization were challenged with limited resource is you know, doing more with less. So the ease of administration, ease of support, just inherent reliability of the technology allows our staff to, you know, sleep more at nights and, you know, work less often during the weekend. So the overalls support overhead has reduced significantly. So that's the those are the biggest things. I would say. >> Those are two very important things. >> Those are the two biggest things that way went into this, um, this engagement with But, you know, we're pleasantly surprised that performance is exceeded our expectations, you know? You know, I did expect reliability. I didn't quite expect this level of performance improvement, so that's been excellent. So again, we're looking for more and more opportunities to expand it. Just given that experience, he >> said, the staff sleeps well at night. How have they reacted? What if some other anecdotes from the staff freed more free time management playing? What's the most of what was some of the feedback from the from your team? >> Well, I mean, I don't want to give the wrong impression. It's not like they're not >> working. Yeah, I write >> the scenario, but, you know, I would say it's gone from, uh, crazy environments is something a little more humane, S O, I think not only with the staff just across the company. You have those who are who kind of buy in and go into it positively and others who are more reluctant. And that's no different the support staff. So I think just their own confidence level. And, you know, there, >> uh, a >> desire to do more with nutanix as increase as they had more experience with >> it. It's interesting. I did a panel yesterday with some customers from NUTANIX and was a mixed in a big bank, midsized company and and a good, big corporate kind of it. And it's very interesting. The legacy with was where there was more legacy. There was a lot of dependencies, and they were looking at time frames for pushing stuff out, like eight weeks to two months in two hours. So they went for eight weeks. Teo pushing any kind of rule propagation or any kind of new stuff. It weeks the two hours and that was a huge number. Are you Are you guys seeing anything around in terms of performance and group on the time side with Nutanix? What are some of the things that you're getting benefits wise operationally. >> Well, the more we do, the more cookie cutter it becomes. So you know, each migration is easier and faster and so on. And that also acid with confidence, right? The very first critical business application that we moved to Nutanix the level of testing we did was insane. Now it's less Oh, so for multiple reasons that migration experience is much more efficient much, much quicker today than it was early on. >> One of the things we hear to Rebecca was, you know, new channels. The new vendor you mentioned new company. They're ten years old, so still new relative to the bigger guys getting it pushed, getting it through, getting it approved by executive confidence from executive management around. Wait, was this new new company what's the benefits? All kinds of gyrations, of approvals and sometimes politics and, you know, legacy kind of factors in How does that work on your N? How did that go? Getting nutanix through was a struggle. That was The challenge was to take us through that. >> So as you mentioned the fact that it's new technology new company that has its own set of challenges from first, some application owners and executives. You know, why take the risk? Why not do the same thing we've done? You know, always, um so? So that that's one big big challenge. The other was There is a tendency, especially early on when NUTANIX was selling it as an appliance, as opposed to license on Lee. Um, there is a tendency to view it as a hardware solution, and it's exactly not that it's the exact opposite of that is purely a software solution. That's where the value is. So it's very easy to get chopped into that hardware discussion where people will kind of compare with servers and storage versus nutanix s. So you have to kind of change that mindset and show the real value that hyper convergence provides thes of administration, that high performance reliability and so on on DH. Then, as you make that argument and convince more people again, you have to, you know, start small and expand. But that that was some of the main challenges. I would say >> when you're talking about the migration experience and you said when we formed the first business critical application with it was a long time we tested it. We really worked at it. Now we have a bit more faith that it's that it's going to work out. But can you talk about some best practices that emerged in terms of how to migrate and my great well, that maybe other companies could learn from from Cadence Design System? >> Yeah, well, I would say the best practices aren't unique to unit nutanix. Any migration process has, you know, various phases in terms of planning, testing and so on. And I think just having that discipline well documented, consistent process so that you're not starting fresh every time there's a new migration initiative going on. But I think nutanix makes it easier just given the especially the prison management tool. But I would say it's not particularly unique to your tent. NUTANIX Torto organization just need to be well disciplined in immigration process. >> One of the things that you mentioned software, which is great point that cultural shifts, not a hardware box, and it's probably all the best practices around. Evaluating hardware software is becoming more and more central to it. How do you see it evolving because you got cloud right on the horizon. You got public cloud benefits. They are clear if you're greenfield yet legacy Stop. We have containers containing ization happening as a trend lift and shift versus, you know, evolved life cycle management of APS and workloads, or are now under a new kind of view with software that was changing and, you know, as a as a practitioner in the field. Now, do you look at the evolution of how it is going to change? >> So my side of the house is the infrastructure and operations side, and they tend to be historically kind of manual, you know, different network administrator, storage administrator, system administrations, the administrators that is all changing and all becoming more developer skill sets, scripting automation, things without sort. So I think that's the biggest changes going on in today is kind of changing the skill sets and kind of viewing it as a full stack as opposed to just stories. You're just network. So having that holistic view point having ability, too, develop automation that works across the stack. I think that those those are the changes that traditional infrastructure groups need. Thio adapted. >> While I was talking to a customer yesterday And he was a young young guy, was I think, in this late twenties I'm seeing myself. You know, ten years ago he was in high school or college. So you see a new generation coming up where they gravitate towards Dev ops, right? And so they get that so they don't have that dogma. What? We went with this vendor. So they kind of this new thinking, Any observations that you can share on this younger generation coming inside your new talent that's coming in. That's developer or what they like. What? What's the work style? What they gravitate to what some of the tools they like. That's the mindset. >> So I think they can teach us to be honest way have you know, the older folks like myself have a tendency to look at the way things have always been done. Right? So having the fresh viewpoint is great to kind of come into it with a dead body develops mentality, you know, off jump. But I think I which we should kind of welcome that and take advantage of that. Um you know, for cadence in General Wei are pretty mature company in terms of our personnel we don't have that rapid turnover person of, you know, our team members. So we're trying to actually, you know, we welcome that new talent, eh? So that we can kind of get that, uh, Dev officer mentality in house and kind of mature it ourselves. So we're in the beginning of that journey. >> How do you work together? Because, I mean, you're not that old first of all, but But this This is the time where we have multiple generations together working in the workforce, thes digital natives that we were talking about that and the people who get technology so innate Li grew up with it versus the Gen Xers. The boomers are still there. The gen y's that are emerging and graduating. Now, how is it a challenge at at Cadence to to get all these people working collaboratively productively together? >> Well, Katie, this is an extremely technical company. Uh, referred to our customers, you know, they're all double e, you know, Master's and doctorate engineers. So it's a very technical environment. We try not to really focus on the technology, actually, but to look at, you know, the business objectives, you know? What are we trying to achieve what problems that we're trying to solve. That supposed, Tio. Oh, here's a cool technology. How can we use it? You know, the mindset is a little bit different. We're looking at the business side first and then using technology to solve for those problems. So once you have that focus, regardless of your experience, your age, your background, you work together, you know, to to achieve that end goal. >> What you think about the show. We're here at NUTANIX next Anaheim. What's what's your verdict on so far? The content. Positioning your customer. What's next for you guys? Yeah, very loyal customer. Based on what we found. People love the product. What's next, Joe? >> I'm very impressed. I wasn't expecting it to be this large. You know, I went Teo Local smaller version that was in the area last year. That was pretty impressive, too. But this is amazing. I like it because, you know, I t leaders get sales calls all the time, and we kind of get bombarded. So Tennessee so ignore those. This kind of gives us a chance to at our own pace kind of see who the key partners are. Two new tenants look for opportunities and meet some of these other vendors s. So it's been both educational as well as kind of entertaining. >> Excellent. Well, thank you so much. Farmers for coming on the Q b really appreciated >> my pleasure to meet you. Thank you. >> I'm Rebecca Knight for John Furrier. We will have much more of nutanix next here in Anaheim, coming up in just a little bit.

>> Narrator: Live from New York, it's theCUBE. Covering the IBM Machine Learning Launch Event. Brought to you by IBM. Now, here are your hosts, Dave Vellante and Stu Miniman. >> Welcome back to New York City, everybody this is theCUBE, we're here at the IBM Machine Learning Launch Event, Rob Thomas is here, he's the general manager of the IBM analytics group. Rob, good to see you again. >> Dave, great to see you, thanks for being here. >> Yeah it's our pleasure. So two years ago, IBM announced the Z platform, and the big theme was bringing analytics and transactions together. You guys are sort of extending that today, bringing machine learning. So the news just hit three minutes ago. >> Rob: Yep. >> Take us through what you announced. >> This is a big day for us. The announcement is we are going to bring machine learning to private Clouds, and my observation is this, you look at the world today, over 90% of the data in the world cannot be googled. Why is that? It's because it's behind corporate firewalls. And as we've worked with clients over the last few years, sometimes they don't want to move their most sensitive data to the public Cloud yet, and so what we've done is we've taken the machine learning from IBM Watson, we've extracted that, and we're enabling that on private Clouds, and we're telling clients you can get the power of machine learning across any type of data, whether it's data in a warehouse, a database, unstructured content, email, you name it we're bringing machine learning everywhere. To your point, we were thinking about, so where do we start? And we said, well, what is the world's most valuable data? It's the data on the mainframe. It's the transactional data that runs the retailers of the world, the banks of the world, insurance companies, airlines of the world, and so we said we're going to start there because we can show clients how they can use machine learning to unlock value in their most valuable data. >> And which, you say private Cloud, of course, we're talking about the original private Cloud, >> Rob: Yeah. >> Which is the mainframe, right? >> Rob: Exactly. >> And I presume that you'll extend that to other platforms over time is that right? >> Yeah, I mean, we're going to think about every place that data is managed behind a firewall, we want to enable machine learning as an ingredient. And so this is the first step, and we're going to be delivering every quarter starting next quarter, bringing it to other platforms, other repositories, because once clients get a taste of the idea of automating analytics with machine learning, what we call continuous intelligence, it changes the way they do analytics. And, so, demand will be off the charts here. >> So it's essentially Watson ML extracted and placed on Z, is that right? And describe how people are going to be using this and who's going to be using it. >> Sure, so Watson on the Cloud today is IBM's Cloud platform for artificial intelligence, cognitive computing, augmented intelligence. A component of that is machine learning. So we're bringing that as IBM machine learning which will run today on the mainframe, and then in the future, other platforms. Now let's talk about what it does. What it is, it's a single-place unified model management, so you can manage all your models from one place. And we've got really interesting technology that we pulled out of IBM research, called CADS, which stands for the Cognitive Assistance for Data Scientist. And the idea behind CADS is, you don't have to know which algorithm to choose, we're going to choose the algorithm for you. You build your model, we'll decide based on all the algorithms available on open-source what you built for yourself, what IBM's provided, what's the best way to run it, and our focus here is, it's about productivity of data science and data scientists. No company has as many data scientists as they want, and so we've got to make the ones they do have vastly more productive, and so with technology like CADS, we're helping them do their job more efficiently and better. >> Yeah, CADS, we've talked about this in theCUBE before, it's like an algorithm to choose an algorithm, and makes the best fit. >> Rob: Yeah. >> Okay. And you guys addressed some of the collaboration issues at your Watson data platform announcement last October, so talk about the personas who are asking you to give me access to mainframe data, and give me, to tooling that actually resides on this private Cloud. >> It's definitely a data science persona, but we see, I'd say, an emerging market where it's more the business analyst type that is saying I'd really like to get at that data, but I haven't been able to do that easily in the past. So giving them a single pane of glass if you will, with some light data science experience, where they can manage their models, using CADS to actually make it more productive. And then we have something called a feedback loop that's built into it, which is you build a model running on Z, as you get new data in, these are the largest transactional systems in the world so there's data coming in every second. As you get new data in, that model is constantly updating. The model is learning from the data that's coming in, and it's becoming smarter. That's the whole idea behind machine learning in the first place. And that's what we've been able to enable here. Now, you and I have talked through the years, Dave, about IBM's investment in Spark. This is one of the first, I would say, world-class applications of Spark. We announced Spark on the mainframe last year, what we're bringing with IBM machine learning is leveraging Spark as an execution engine on the mainframe, and so I see this as Spark is finally coming into the mainstream, when you talk about Spark accessing the world's greatest transactional data. >> Rob, I wonder if you can help our audience kind of squint through a compare and contrast, public Cloud versus what you're offering today, 'cause one thing, public Cloud adding new services, machine learning seemed like one of those areas that we would add, like IBM had done with a machine learning platform. Streaming, absolutely you hear mobile streaming applications absolutely happened in the public Cloud. Is cost similar in private Cloud? Can I get all the services? How will IBM and your customer base keep up with that pace of innovation that we've seen from IBM and others in the public Cloud on PRIM? >> Yeah, so, look, my view is it's not an either or. Because when you look at this valuable data, clients want to do some of it in public Cloud, they want to keep a lot of it in the system that they built on PRIMA. So our job is, how do we actually bridge that gap? So I see machine learning like we've talked about becoming much more of a hybrid capability over time because the data they want to move to the Cloud, they should do that. The economics are great. The data, doing it on private Cloud, actually the economics are tremendous as well. And so we're delivering an elastic infrastructure on private Cloud as well that can scale the public Cloud. So to me it's not either or, it's about what everybody wants as Cloud features. They want the elasticity, they want a creatable interface, they want the economics of Cloud, and our job is to deliver that in both places. Whether it's on the public Cloud, which we're doing, or on the private Cloud. >> Yeah, one of the thought exercises I've gone through is if you follow the data, and follow the applications, it's going to show you where customers are going to do things. If you look at IOT, if you look at healthcare, there's lots of uses that it's going to be on PRIMA it's going to be on the edge, I got to interview Walmart a couple of years ago at the IBM Ed show, and they leveraged Z globally to use their sales, their enablement, and obviously they're not going to use AWS as their platform. What's the trends, what do you hear form their customers, how much of the data, are there reasons why it needs to stay at the edge? It's not just compliance and governance, but it's just because that's where the data is and I think you were saying there's just so much data on the Z series itself compared to in other environments. >> Yeah, and it's not just the mainframe, right? Let's be honest, there's just massive amounts of data that still sits behind corporate firewalls. And while I believe the end destination is a lot of that will be on public Cloud, what do you do now? Because you can't wait until that future arrives. And so the place, the biggest change I've seen in the market in the last year is clients are building private Clouds. It's not traditional on-premise deployments, it's, they're building an elastic infrastructure behind their firewall, you see it a lot in heavily-regulated industries, so financial services where they're dealing with things like GDPR, any type of retailer who's dealing with things like PCI compliance. Heavy-regulated industries are saying, we want to move there, but we got challenges to solve right now. And so, our mission is, we want to make data simple and accessible, wherever it is, on private Cloud or public Cloud, and help clients on that journey. >> Okay, so carrying through on that, so you're now unlocking access to mainframe data, great, if I have, say, a retail example, and I've got some data science, I'm building some models, I'm accessing the mainframe data, if I have data that's elsewhere in the Cloud, how specifically with regard to this announcement will a practitioner execute on that? >> Yeah, so, one is you could decide one place that you want to land your data and have it be resonant, so you could do that. We have scenarios where clients are using data science experience on the Cloud, but they're actually leaving the data behind the firewalls. So we don't require them to move the data, so our model is one of flexibility in terms of how they want to manage their data assets. Which I think is unique in terms of IBM's approach to that. Others in the market say, if you want to use our tools, you have to move your data to our Cloud, some of them even say as you click through the terms, now we own your data, now we own your insights, that's not our approach. Our view is it's your data, if you want to run the applications in the Cloud, leave the data where it is, that's fine. If you want to move both to the Cloud, that's fine. If you wanted to leave both on private Cloud, that's fine. We have capabilities like Big SQL where we can actually federate data across public and private Clouds, so we're trying to provide choice and flexibility when it comes to this. >> And, Rob, in the context of this announcement, that would be, that example you gave, would be done through APIs that allow me access to that Cloud data is that right? >> Yeah, exactly, yes. >> Dave: Okay. >> So last year we announced something called Data Connect, which is basically, think of it as a bus between private and public Cloud. You can leverage Data Connect to seamlessly and easily move data. It's very high-speed, it uses our Aspera technology under the covers, so you can do that. >> Dave: A recent acquisition. >> Rob, IBM's been very active in open source engagement, in trying to help the industry sort out some of the challenges out there. Where do you see the state of the machine learning frameworks Google of course has TensorFlow, we've seen Amazon pushing at MXNet, is IBM supporting all of them, there certain horses that you have strong feelings for? What are your customers telling you? >> I believe in openness and choice. So with IBM machine learning you can choose your language, you can use Scala, you can use Java, you can use Python, more to come. You can choose your framework. We're starting with Spark ML because that's where we have our competency and that's where we see a lot of client desire. But I'm open to clients using other frameworks over time as well, so we'll start to bring that in. I think the IT industry always wants to kind of put people into a box. This is the model you should use. That's not our approach. Our approach is, you can use the language, you can use the framework that you want, and through things like IBM machine learning, we give you the ability to tap this data that is your most valuable data. >> Yeah, the box today has just become this mosaic and you have to provide access to all the pieces of that mosaic. One of the things that practitioners tell us is they struggle sometimes, and I wonder if you could weigh in on this, to invest either in improving the model or capturing more data and they have limited budget, and they said, okay. And I've had people tell me, no, you're way better off getting more data in, I've had people say, no no, now with machine learning we can advance the models. What are you seeing there, what are you advising customers in that regard? >> So, computes become relatively cheap, which is good. Data acquisitions become relatively cheap. So my view is, go full speed ahead on both of those. The value comes from the right algorithms and the right models. That's where the value is. And so I encourage clients, even think about maybe you separate your teams. And you have one that's focused on data acquisition and how you do that, and another team that's focused on model development, algorithm development. Because otherwise, if you give somebody both jobs, they both get done halfway, typically. And the value is from the right models, the right algorithms, so that's where we stress the focus. >> And models to date have been okay, but there's a lot of room for improvement. Like the two examples I like to use are retargeting, ad retargeting, which, as we all know as consumers is not great. You buy something and then you get targeted for another week. And then fraud detection, which is actually, for the last ten years, quite good, but there's still a lot of false positives. Where do you see IBM machine learning taking that practical use case in terms of improving those models? >> Yeah, so why are there false positives? The issue typically comes down to the quality of data, and the amount of data that you have that's why. Let me give an example. So one of the clients that's going to be talking at our event this afternoon is Argus who's focused on the healthcare space. >> Dave: Yeah, we're going to have him on here as well. >> Excellent, so Argus is basically, they collect data across payers, they're focused on healthcare, payers, providers, pharmacy benefit managers, and their whole mission is how do we cost-effectively serve different scenarios or different diseases, in this case diabetes, and how do we make sure we're getting the right care at the right time? So they've got all that data on the mainframe, they're constantly getting new data in, it could be about blood sugar levels, it could be about glucose, it could be about changes in blood pressure. Their models will get smarter over time because they built them with IBM machine learning so that what's cost-effective today may not be the most effective or cost-effective solution tomorrow. But we're giving them that continuous intelligence as data comes in to do that. That is the value of machine learning. I think sometimes people miss that point, they think it's just about making the data scientists' job easier, that productivity is part of it, but it's really about the voracity of the data and that you're constantly updating your models. >> And the patient outcome there, I read through some of the notes earlier, is if I can essentially opt in to allow the system to adjudicate the medication or the claim, and if I do so, I can get that instantaneously or in near real-time as opposed to have to wait weeks and phone calls and haggling. Is that right, did I get that right? >> That's right, and look, there's two dimensions. It's the cost of treatment, so you want to optimize that, and then it's the effectiveness. And which one's more important? Well, they're both actually critically important. And so what we're doing with Argus is building, helping them build models where they deploy this so that they're optimizing both of those. >> Right, and in the case, again, back to the personas, that would be, and you guys stressed this at your announcement last October, it's the data scientist, it's the data engineer, it's the, I guess even the application developer, right? Involved in that type of collaboration. >> My hope would be over time, when I talked about we view machine learning as an ingredient across everywhere that data is, is you want to embed machine learning into any applications that are built. And at that point you no longer need a data scientist per se, for that case, you can just have the app developer that's incorporating that. Whereas another tough challenge like the one we discussed, that's where you need data scientists. So think about, you need to divide and conquer the machine learning problem, where the data scientist can play, the business analyst can play, the app developers can play, the data engineers can play, and that's what we're enabling. >> And how does streaming fit in? We talked earlier about this sort of batch, interactive, and now you have this continuous sort of work load. How does streaming fit? >> So we use streaming in a few ways. One is very high-speed data ingest, it's a good way to get data into the Cloud. We also can do analytics on the fly. So a lot of our use case around streaming where we actually build analytical models into the streaming engine so that you're doing analytics on the fly. So I view that as, it's a different side of the same coin. It's kind of based on your use case, how fast you're ingesting data if you're, you know, sub-millisecond response times, you constantly have data coming in, you need something like a streaming engine to do that. >> And it's actually consolidating that data pipeline, is what you described which is big in terms of simplifying the complexity, this mosaic of a dupe, for example and that's a big value proposition of Spark. Alright, we'll give you the last word, you've got an audience outside waiting, big announcement today; final thoughts. >> You know, we talked about machine learning for a long time. I'll give you an analogy. So 1896, Charles Brady King is the first person to drive an automobile down the street in Detroit. It was 20 years later before Henry Ford actually turned it from a novelty into mass appeal. So it was like a 20-year incubation period where you could actually automate it, you could make it more cost-effective, you could make it simpler and easy. I feel like we're kind of in the same thing here where, the data era in my mind began around the turn of the century. Companies came onto the internet, started to collect a lot more data. It's taken us a while to get to the point where we could actually make this really easy and to do it at scale. And people have been wanting to do machine learning for years. It starts today. So we're excited about that. >> Yeah, and we saw the same thing with the steam engine, it was decades before it actually was perfected, and now the timeframe in our industry is compressed to years, sometimes months. >> Rob: Exactly. >> Alright, Rob, thanks very much for coming on theCUBE. Good luck with the announcement today. >> Thank you. >> Good to see you again. >> Thank you guys. >> Alright, keep it right there, everybody. We'll be right back with our next guest, we're live from the Waldorf Astoria, the IBM Machine Learning Launch Event. Be right back. [electronic music]