(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat music) >> Welcome to transforming ideas into insights, presented with theCUBE and made possible by Cloudera. My name is Dave Vellante from theCUBE and I'll be your host for today. In the next hundred minutes, you're going to hear how to turn your best ideas into action using data and we're going to share the real-world examples of 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing efficiencies, better forecast retail demand, transform analytics, improve public sector service and so much more. How we use data is rapidly evolving. That is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to, rather we use terms like, digital transformation and digital business. But you think about it. What is a digital business? How is that different from just a business? Well, a digital business is a data business and it differentiates itself by the way it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such, the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings, but increasingly insights are leading to the development of data products and services that can be monetized. Or as you'll hear in our industry examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. Self-service investing, filing insurance claims on our smart phones and so many examples. IOT systems that communicate and act machine to machine. Real-time pricing actions, these are all examples of products and services that drive revenue, cut costs or create other value and they all rely on data. Now, while many business leaders sometimes express frustration that their investments in data, people and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data transformation and leadership. One thing is certain, the next 10 years of data and digital transformation won't be like the last 10. So let's into it. Please join us in the chat. You can ask questions. You can share your comments. Hit us up on Twitter. Right now, it's my pleasure to welcome Mick Holliston and he's the president of Cloudera. Mick, great to see you. >> Great to see you as well, Dave. >> Hey, so I call it the new abnormal, right? The world is kind of out of whack. Offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations, line cooks at restaurants. Everything that we consumers have missed, but here's the one thing, it seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling, their pricing algorithms, commodity prices, we don't know. Is inflation transitory? Is it a long-term threat, trying to forecast GDP? It seems like we have to reset all of our assumptions and Mick, I feel a quality data is going to be a key here. How do you see the current state of the industry in the role data plays to get us into a more predictable and stable future? >> Well, I can sure tell you this, Dave, out of whack is definitely right. I don't know if you know or not, but I happened to be coming to you live today from Atlanta and as a native of Atlanta, I can tell you there's a lot to be known about the airport here. It's often said that whether you're going to heaven or hell, you got to change planes in Atlanta and after 40 minutes waiting on an algorithm to be right for baggage claim last night, I finally managed to get some bag and to be able to show up, dressed appropriately for you today. Here's one thing that I know for sure though, Dave. Clean, consistent and safe data will be essential to getting the world and businesses as we know it back on track again. Without well-managed data, we're certain to get very inconsistent outcomes. Quality data will be the normalizing factor because one thing really hasn't changed about computing since the dawn of time, back when I was taking computer classes at Georgia Tech here in Atlanta and that's what we used to refer to as garbage in, garbage out. In other words, you'll never get quality data-driven insights from a poor dataset. This is especially important today for machine learning and AI. You can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid. As AI is increasingly used in every business app, you must build a solid data foundation. >> Mick, let's talk about hybrid. Every CXO that I talked to, they're trying to get hybrid right. Whether it's hybrid work, hybrid events, which is our business, hybrid cloud. How are you thinking about the hybrid everything, what's your point of view? >> With all those prescriptions of hybrid and everything, there was one item you might not have quite hit on, Dave and that's hybrid data. >> Oh yeah, you're right, Mick, I did miss that. What do you mean by hybrid data? >> Well, Dave, in Cloudera, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now, every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises, in a private cloud, in public cloud or perhaps even in a new open data exchange. Now, this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud. But either way, security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch or read a recent news story. Data breaches are at an all time high and the ethics of AI applications are being called into question everyday. And understanding lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know, Dave, that's the business Cloudera is in. On a serious note, from Cloudera's perspective, adopting a hybrid data strategy is central to every business' digital transformation. It will enable rapid adoption of new technologies and optimize economic models, while ensuring the security and privacy of every bit of data. >> Okay, Mick, I'm glad you brought in that notion of hybrid data because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You got the economics, the physics, the local laws come into play, so what about the rest of hybrid? >> Yeah, that's a great question, Dave and certainly, Cloudera itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind, the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office ethernet may not be happening quite so fast in somebody's rural home in the middle of Nebraska somewhere. So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, involve us kind of slowly coming back to work, beginning this fall. And we're looking forward to being able to shake hands and see one another again for the first time, in many cases, for more than a year and a half. But yes, hybrid work and hybrid data are playing an increasingly important role for every kind of business. >> Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, here's how I think about it. I mean, some industries have transformed. You think about retail, for example, it's pretty clear. Although, every physical retail brand I know has not only beefed up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence. And reverse, we see Amazon building out physical assets, so there's more hybrid going on. But when you look at healthcare, for example, it's just starting with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control of payment systems. In manufacturing, the pandemic highlighted, America's reliance on China as a manufacturing partner and supply chain. And so my point is, it seems at different industries, they're in different stages of transformation, but two things look really clear. One, you got to put data at the core of the business model, that's compulsory. It seems like embedding AI into the applications, the data, the business process, that's going to become increasingly important. So how do you see that? >> Wow, there's a lot packed into that question there, Dave. But yeah, at Cloudera, I happened to be leading our own digital transformation as a technology company and what I would tell you there that's been an interesting process. The shift from being largely a subscription-based model to a consumption-based model requires a completely different level of instrumentation in our products and data collection that takes place in real-time, both for billing for our customers and to be able to check on the health and wellness, if you will, of their Cloudera implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed the rate and pace of getting vaccines to market or we've been assisting with testing process that's taken place. Because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and healthy and safe outcomes for everyone. And Cloudera has been underneath a great deal of that type of work. And the financial services industry you pointed out, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are helping those kinds of organizations get through those difficult challenges. You also happened to mention public sector and in public sector, we're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. And while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, you can't build great ML apps unless you've got a strong data foundation underneath. It's back to that garbage in, garbage out comment that I made previously. And so, in order to do that, you've got to have a great hybrid data management platform at your disposal to ensure that your data is clean and organized and up to date. Just as importantly from that, that's kind of the freedom side of things. On the security side of things, you've got to ensure that you can see who's touched not just the data itself, Dave, but actually the machine learning models. And organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage in addition to data lineage. In other words, who's had access to the machine learning models? When and where and at what time and what decisions were made perhaps, by the humans, perhaps by the machines that may have led to a particular outcome? So, every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models, just as they can track the lineage of data. So, lots going on there across industries. Lots going on as those various industries think about how AI can be applied to their businesses. >> It's a pretty interesting concept you're bringing into the discussion, the hybrid data, sort of, I think new to a lot of people. And this idea of model lineage is a great point because people want to talk about AI ethics, transparency of AI. When you start putting those models into machines to do real-time inferencing at the edge, it starts to get really complicated. I wonder if we could talk, we're still on that theme of industry transformation. I felt like coming into the pre-pandemic, there was just a lot of complacency. Yeah, digital transformation and a lot of buzz words and then we had this forced march to digital, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? >> There definitely is a lot of a POC limbo or I think some of us internally have referred to as POC purgatory, just getting in that phase, not being able to get from point A to point B in digital transformation. And for every industry, transformation, change in general, is difficult and it takes time and money and thoughtfulness. But like with all things, what we've found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts. To say it another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts, as it relates to the underlying technology here and to bring it home a little bit more practically, I guess I would say. At Cloudera, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, right close to home. In place, they can kind of experiment a little bit more safely and economically and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplished, but kind of starting small and then drawing fast from there on customer's journey to the cloud. >> Well Mick, we've covered a lot of ground. Last question, what do you want people to leave this event, this session with and thinking about sort of the next era of data that we're entering? >> Well, it's a great question, but I think it could be summed up in two words. I want them to think about a hybrid data strategy. So, really hybrid data is a concept that we're bringing forward on this show really, for the first time, arguably. And we really do think that it enables customers to experience what we refer to, Dave, as the power of ANT. That is freedom and security and in a world where we're all still trying to decide whether each day when we walk out, each building we walk into, whether we're free to come in and out with a mask, without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe for ourselves and for others. And the same is true of organization's IT strategies. They want the freedom to choose, to run workloads and applications in the best and most economical place possible, but they also want to do that with certainty that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So, hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole. >> Nick, thanks so much, great conversation. I really appreciate the insights that you're bringing to this event, into the industry, really. Thank you for your time. >> You bet, Dave, pleasure being with you.

>> From the cube studios in Palo Alto in Boston bringing you data-driven insights from the cube in ETR. This is breaking analysis with Dave Vellante. >> There are four A players, in the IS slash pass hyperscale cloud services space, AWS, Azure, Alibaba, and alphabet, pretty clever, huh? In our view, these four have the resources, the momentum, and stamina to outperform all others virtually indefinitely. Now combined, we believe these companies will generate more than $115 billion in 2021 IaaS and PaaS revenue. That is a substantial chunk of market opportunity that is growing as a whole in the mid 30% range in 2021. Welcome to this week's Wiki bond cube insights, powered by ETR. In this breaking analysis, we are initiating coverage of Alibaba for our IaaS and PaaS market segments. And we'll update you on the latest hyperscale cloud market data, and survey data from ETR. Big week in hyperscale cloud land, Amazon and alphabet reported earnings and AWS CEO Andy Jassy was promoted to lead Amazon overall. I interviewed John Furrier on the cube this week. John has a close relationship with Jassy and a unique perspective on these developments. And we simulcast the interview on clubhouse, and then hosted a two hour clubhouse room that brought together all kinds of great perspectives on the topic. And then, we took the conversation to Twitter. Now in that discussion, we were just riffing on our updated cloud estimates and our numbers. And here's this tweet that inspired the addition of Alibaba. Now this gentleman is a tech journalist out of New Delhi and he pointed out that we were kind of overlooking Alibaba and I responded that no, we do not just discounting them but we just need to do more homework in the company's cloud business. He also said we're ignoring IBM, but really they're not in this conversation as a hyperscale IaaS competitor to the big four in our view. And we'll just leave it at that for now on IBM, but, back to Alibaba and the big four, we actually did some homework. So thank you for that suggestion. And this chart shows our updated IaaS figures and includes the full year 2020 which was pretty close to our Q4 projections. You know, the big change is we've added Alibaba in the mix. Now these four companies last year, accounted for $86 billion in revenue, and they grew it 41% rate combined relative to 2019. Now, notably as your revenue for the first time is more than half of that of AWS's revenue which of course hit over $45 billion. AWS's revenue, over top 45 billion last year, which is just astounding. Alibaba you'll note, is larger than Google cloud. The Google cloud platform, I should say GCP, at just over eight billion for Alibaba. Now, the reason Baba is such a formidable competitor, is because the vast majority of its revenue comes from China inside that country. And the company do have plans to continue their international expansion, so we see Alibaba as a real force here. Their cloud business showed positive EBITDA for the first time in the history of the company last quarter. So that has people excited. Now, Google, as we've often reported, is far behind AWS and Azure, despite its higher growth rates Google's overall cloud business lost 5.6 billion in 2020 which has some people concerned. We on the other hand are thrilled, because as we've reported in our view, Google needs to get its head out of its ads cloud is it's future. And we're very excited about the company pouring investments into its cloud business. Look with $120 billion essentially in the balance sheet, we can think of a better use of its cash. Now, I want to stress that these figures are our best efforts to create an apples to apples comparison across all four clouds. Many people have asked about, how much of these figures represent, for example, Microsoft office 365 or Google G suite, which by the way now is called workspaces. And the answer is our intention is $0. These are our estimates of worldwide IaaS in PaaS revenue. You know, some of said, we're too low. Some of said, we're too high. Hey, if you have better numbers, Please share them, happy to have a look. Now you maybe asking, what are the drivers of these figures and the growth that we're showing here? Well, all four of these companies, of course, they're benefiting from an accelerated shift to digital as a result to COVID, but each one has other tailwinds. You know, for example, AWS, it's Capitalizing on its a large headstart. It's created tremendous brand value. And as well, despite the fact that, while we estimate that more than 75% of AWS revenue comes from compute and storage, AWS is feature and functional differentiation combined with this large ecosystem is a very much a driving force of it's growth. In the case of Azure, in addition to its captive software application estate, the company on its earnings calls cited strong growth in its consumption based business across all of its industries and customer segments. As we've said, many times, Microsoft makes it really easy for customers to tap into Azure and a true consumption pricing model, with no minimums and cancel any time. Those kinds of terms make it extremely attractive to experiment and get hooked. We certainly saw this with AWS over the years. Now for Google it's growth is being powered by its outstanding technology, and in particular its prowess in AI and analytics. As well we suspect that much of the losses in Google cloud are coming from large go to market investments for Google cloud platform, and they're paying growth dividends. Now, as Tim Crawford said on Twitter, 6 billion, you know that's not too shabby. Also Google cited wins at Wayfair in Etsy, that Google is putting forth in our view to signal that many retailers they might be are you reluctant to do business with Amazon, was of course a big retailer competitor. These are two high profile names, we'd like to see more in future quarters and likely will. Now let's give you another view of this data and paint a picture of, how the pie is being carved out in the market. Actually we'll use bars because my, millennials sounding boards they hate pie charts. And I like to pay attention, to these emerging voices. At any rate amongst these four, AWS has more than half of the market. AWS and Azure are well ahead of the rest. And we think we'll continue to hold serve for quite some time. Now while we're impressed with Alibaba, they're currently constrained to doing business mostly in China. And we think it'll take many years for Baba and GCP to close that gap on the two leaders if they'll ever even get there. Now let's take a look at, what the customers are saying within the ETR survey data. The chart that we're showing here, this is X, Y chart that we show all the time. It's got net score or spending moments on the vertical axis, and market share or the pervasiveness in the datasets in the survey on the horizontal axis. Now on the upper right, you can see the net scores and the number of mentions for each company and the detailed behind this data. And what we've done here is cut the January survey data of 1,262 respondents, you can see that in filtered in there on the left, and we've filtered the data by cloud meaning the respondents are answering about the companies, cloud computing offerings only. So we're filtering out anything of the non-cloud spend. That's a nice little capability of the ETR platform. Azure is really quite amazing to us. It's got a net score of 72.6%, and that's across 572 responses out of the 1262. AWS is the next most pervasive in the data set with 492 shared accounts and a net score of 57.1%. Now, you may be wondering, well, why is Azure bigger in the dataset than AWS? And when we just told you that the opposite is the case in the market in the previous slide. And the answer is, like this is a survey and it's a lot of Microsoft out there, they're everywhere. And I have no doubt that the respondants notion of cloud doesn't directly map into IaaS and PaaS views of the world, but the trends are clear and consistent. Amazon and Azure, they dominate in this market space. Now for context, we've included functions in the form of AWS Lambda as your functions and Google cloud functions. Because, as you can see, there's a lot of spending momentum in these capabilities in these services. You'll also note, that we've added Alibaba to this chart, and it's got a respectable 63.6% net Score, but there are only 11 shared responses in the data. So they'll go into the bank on these numbers, but look, 11 data points, we'll take it. It's better than zero data points. We've also added VMware cloud on AWS in this chart, and you can see that, that capability that service, that has the momentum and you can see those ones that we've highlighted above the 40% red dotted line, that's where the real action in the market is. So all of those offerings have very strong or strong spending velocity in the ETR data set. Now, for context, we've put Oracle and IBM in the chart. And you can see, they both have, you know they've got a decent presence in the data set. They have 132 mentions and 81 responses respectively. So Oracle, they've got a positive net score of 16.7%, and IBM is in a negative 6.2%. Now, remember this is for their cloud offerings, as the respondents in the data set see them. So what does this mean? It says that among the 132 survey respondents answering that they use Oracle cloud, 16.7% more customers are spending more on Oracle's cloud than are spending less. In the case of IBM, it says more customers are spending less than spending more. Both companies are in the red zone, and show far less momentum than the leaders. Look, I've said many times that the good news is, that Oracle and IBM at least have clouds. But they're not direct competitors of the big four in our view, there just not. They have a large software business, and they can migrate their customers, to their respective clouds and market hybrid cloud services. Their definition of cloud is most certainly different than that of AWS, which is fine, but both companies use what I call a kitchen sink method of reporting their cloud business. Oracle includes, cloud and license support, often with revenue recognition at the time of contract, With a term that's renewable and, it also includes on-prem fees, for things like database and middleware, and if, you want to call that cloud, fine. IBM is just as bad, maybe they're worse and includes so much legacy stuff and its cloud number to hide the ball. It's just not even worth trying to unpack for this episode, I have previously and frankly, it's just not a good use of time. Now, as I've said before, both companies they're in the game that can make good money provisioning infrastructure to support their respective software businesses. I just don't consider them hyperscale class clouds which are defined by the big four, and really only those four. And I'm sure I'll get hate mail about that statement, and I'm happy to defend that position, so please reach out. Okay, but one other important thing that we want to discuss is something that came up this week in our Twitter conversation. Here's a tweet from Matt Baker who had strategic planning for Dell. He was responding to someone who commented on our cloud data, basically saying that, with all that cloud revenue who took the hit, which pockets did it come out of, and Matt was saying, look, it's coming out of customer pockets, but can we please end this zero sum game narrative. In other words, it's not a dollar for cloud that doesn't translate into a lost dollar from on-prem for the legacy companies. So let's take a look at that. For first I would agree, with Matt Baker, it's not a one for one swap of spend but there's definitely been an impact. And here's some data from ETR that can, maybe give us some insight here. What this chart shows is a cut of 915 hyperscale cloud accounts. So within those big four, and within those accounts we show the spending velocity or net score cut within further sectors representative of these on-prem players. So servers, storage and networking, so we cut the data on those three segments. And we're looking here at, VMware, Cisco, Dell, HPE, and IBM, for 2020 and into 2021. It's kind of an interesting picture, it shows the net scores for the January of 20 April, July and October 20 surveys and the January 21 surveys. Now all the on-prem players, they were of course impacted by COVID, IBM seems to be that counter trend line. Not that they weren't impacted, but they have this notable mainframe cycle thing going on. And you know, they're in a down cycle now. So it's kind of opposite of the other guys in terms of the survey momentum. And you can see pretty much, all the others are showing upticks headed into 2021, Cisco, you know kind of flattish, but stable and held up a bit. So to Matt Baker's point, despite the 35% or so growth expected for the big four and 2021 the on-prem leaders are showing some signs of positive spending momentum. So let's dig into this a little bit further, 'cause we're not saying cloud hasn't hurt on prem spending. You know, of course it has. Here's that same picture, over a 10 year view. So you're seeing this long, slow, decline occur, and it's no surprise. If you think about the prevailing model for servers, storage, and networking, on prem in particular. Servers have been perpetually under utilized, even with virtualization. You know, with the exception of like backup jobs, there aren't many workloads that can max out server utilization. So we kept buying more servers to give us performance headroom and ran at 20, 30% utilization, you know in a good day. Yes I know some folks can get up over 50%, but generally speaking servers are well under utilized in storage my gosh, it's kind of the same story, maybe even worse. Because for years it was powered by a mechanical system. So more spindles are required to gain performance, lots of copying going on, lots of, you know, pre-flash waste. And in networking it was a story of got to buy more ports. You've got to buy more ports. In the case of these segments, customers will just defense essentially, forced in this endless cycle of planning, procuring, you know, first planning. They got to get the secure the CapEx, and then they procure, and then they over-provision, and then they manage, you know, ongoing. So then along comes AWS, and says, try this on for size and you can see from that chart, the impact of cloud on those bellwether on-prem infrastructure players. Now, just to give you a little bit more insight on this topic, here's a picture of the wheel charts from the ETR data set. For AWS Microsoft, Google, and we brought in VMware to compare them. A wheel chart shows the percent of customers saying they'll either add a platform new that's the lime green. Increased spending by more than 5%, that's the forest green spend flat relative to last year. That's the gray spend less by more than 5% down, that's the pinkish or leave the platform, that's the Bright red. You subtract the red from the green and you get a percentage that represents net score, AWS with a net score of 60% is off the charts good. Microsoft remember, this includes the entire Microsoft business portfolio, not just Azure, so it's still really strong. Google, frankly, we'd like to see higher net scores and VMware's, you know, so there's a gold standard for on-prem. So we include them, so you can see for reference the strong, but notice they got a much, much bigger flat spending, which is what you would expect from some of these more mature players. Now let's compare these scores to the other, on-prem Kings. So this is not surprising to see, but the greens, they go down, the flats that gray area goes up compared to the cloud guys and the red which is virtually non-existent within AWS, goes into the high teens with the exception of Cisco which despite its exposure to virtually all industries including those hard hit by COVID shows pretty low read scores. So that's, that's good. And I got to share one other, look at this wheel chart for pure storage. We're not really not sure what's happening here, but this is impressive. We're seeing a huge rebound, and you can see we've superimposed as candlestick over comparing previous quarters surveys and, look at the huge up check in the January survey for pure that blue line. That's highlighted in that red dot at ellipse, jumps to a 63% net score from below 20% last quarter. You know, we'll see, I've never seen that kind of uptick before for an established company. And, you know, maybe it's pent up demand or some other anomaly in the data. We'll find out when pure reports in 2021, because remember these are forward looking surveys. But the point is, you still see action going on in hybrid and on-prem, and despite the freight train that is cloud, coming at the legacy players. You know, not that pure is legacy, but it's, you know, it's no longer a lanky teenager. And I think the bottom line, coming back to Matt Baker's point, is there are opportunities that the on-prem players can pursue in hybrid and multi-cloud, and we've talked about this a lot where you're building abstraction layer, on top of the hyperscale clouds and letting them build out their data center presence worldwide, spend on capex, they're going to outspend everybody. And these guys, these on-prem, and hybrid and multi-cloud folks they're going to have to add value on top of that. Now if they move fast, you no doubt there'll be acquiring startups to make that happen. They're going to have to put forth the value proposition and execute on that, in a way that adds clear value above and beyond what the hyperscalers are going to do. Now, the challenge, is picking those right spots, moving fast enough and balancing wall street promises with innovation. There's that same old dilemma. Let's face It. Amazon for years could lose tons of money and not get killed in the street. Google, they got so much cash, they can't spend it fast enough and Microsoft after years of going sideways is finally figured out and the some. Alibaba they're new to our analysis, but it's looking like you know, it's the Amazon of China, Plus ANT despite its regulatory challenges with the Chinese government. So all four of these players, are in the driver's seat in our view. And they're leading in not only cloud, but AI. And of course the data keeps flowing into their cloud. So they're really are in a strong position. Bottom line is we're still early into the cloud platform era and it's morphing. It's from a collection of remote cloud services, into this ubiquitous, sensing, thinking, anticipatory system, that's increasingly automated and working towards full automation. It's intelligent and it's hyper decentralizing toward the edge. One thing's for sure, the next 10 years, they're not going to be the same as the past 10. Okay, that's it for now. Remember I publish each week on Wikibond.com and siliconANGLE.com, these episodes they're all available as podcasts just search for breaking analysis podcast. You can always connect on Twitter. I'm @dvellante or email me at david.Vellante@siliconANGLE.com. I love the comments on LinkedIn and of course in clubhouse the new social app. So please follow me, so that you can get notified when we start a room and riff on these topics. And don't forget to check out etr.plus for all the survey action. This is Dave Vellante for the cube insights powered by ETR be well, and we'll see you next time. (upbeat music)

we've made just tremendous progress over the last several years with Microsoft you know started back in 2015 where we you know cross certified hypervisors and that's kind of a basic you know let's work together over the last couple years it's truly blossomed into a really good partnership where you know I think they've and we both gotten over this you know Linux vs. Windows thing and you know I said we've gotten over I think we both recognized you know we need to serve our customers in the best possible way and that clearly means is two of the largest infrastructure software providers working closely together and what's been interesting as we've gone forward we find more and more common ground about how we can better serve our customers whether that's you know what might sound mundane that's a big deal sequel server on realm and setting benchmarks around that or dotnet running on our platforms now all the way to really being able to deliver a hybrid cloud with a seamless experience with openshift from you know on premise - - to Azure and I mean having to H Bank on States twenty five thousand containers running in production moving back and forth - sure and I think it's more building on what I talked to you about a year ago if I remember last May May of 2018 in San Francisco so I was exposing very heavily look the world's going to move towards containers the world is already embraced Linux this is the time to have a new architecture that enables hybrid much along the lines that gem and all of the clients as well as Ginni and Sasha we're talking about on stage yesterday so you put all that together and you say that is what we mentioned last year and we were clear that is where the world is going to go nice step forward a few months from there into October of 2018 and on 29th of October we announced that IBM intends to acquire Red Hat so then you say wow we put actually our money where amount was we were talking about the strategy we were talking about Linux containers openshift the partnership we announced last May was IBM software products together with OpenShift that is we already believed in that but now this allows us coming together it's it's more like a marriage then sort of loose partners passing each other in the middle of the night we are so excited and you know having put in all the time part of this is representing all the work the team has done and the communities have done when you think about all the work that goes into a Linux distribution it is everybody it's the community's it's the partners so we released the Red Hat Enterprise Linux eight beta in November mid-november we've had 40,000 downloads of that beta since November people who have provided feedback and comments suggestions all of that fed into what we've released today as the Red Hat Enterprise Linux eight general availability so it's a big day and part of it is we're just so proud of how we've done it and what we've done and we've really redefined what are not the value of an operating system with Red Hat Enterprise limits eight tech transformation started about ten years ago bean CI over the company about ten years and frankly the first five years were just fixing the basics so getting in place what we'd call world-class systems doing a bunch of stuff on resilience and security and all of that kind of stuff and the other thing and this is the dramatic change you know ten years ago when I joined the company we were 85% outsource to managed service vendors so I had technology people that basically were signing contractors and managing service agreements if we didn't have technology DNA and so you know over those five years and the full ten years actually we've been to not about just in sourcing and rebuilding our technical muscle if you like so now we're we've gone from 85% outsource to 90% in sourced so we run build and manage our own we're at word now a technology company yeah and and five years ago we had a real big shift and you know we were we were closest to what was going on in China and so probably saw this before many many of the other banks saw this around the world of what Alibaba was doing with ant financial and $0.10 and this whole just just complete disruption of how customers interact with the banking industry so we got an early lead on this digital transformation and really for the last five six years would be doubling down on building a pure digital offering and we see ourselves as a technology company providing banking services not as a bank with some technology department in the backend open source is the innovation model going forward period end of story full stop and I think as I said in my keynote yesterday you know leading up to the the biggest acquisition ever for a software company not an open source software coming a software company that happened to be an open source software company I don't think there's any doubt that that open source has one here here today it and it's because of the pace of innovation yeah our goal is to make sure we're supporting those upstream communities so all of all of Red Hat software is open source and we work with a whole community of individuals and companies and the upstream open source software and we want to make sure that we're not just contributing features that we want but that we're a good player or that we're helping to make sure those communities are healthy and so for a number of the projects that were involved in we actually assigned a full-time Community Manager a community lead to help make sure that project is healthy so we have someone on everything from Saif and Gloucester to fedora to kubernetes I'm just making sure the community does well yeah we do a little bit of both and so a lot of it is responding to the community and that's one of the areas that Red Hat is really excelled as taking what's popular what's working upstream and helping moving along make it a stable product or stable solution that developers can use but we also have a certain agenda or certain platforms that we want to present so we start from like various runtimes to actually contain our platforms and so we want to have to kind of drive some of that initiatives on our own to help drive fill that need because we hear it from customers a lot it's like things are doing are great but like there's all these projects that need to come together sort as a product or unified experience and so we spend a lot of our time trying to bring those things together as a way to help developers do those different tasks and also focus across like not just the Java runtimes which we hit a lot of Java so you might have baked security in right I mean we have a secure supply chain and you talk about difficult things for la right every package that we that comes in that is we totally refresh everything from upstream but when they come in we have to inspect all the crypto we have to run them through security scans vulnerability scanners we've got three different vulnerability scanners that we're using we run them through penetration testing so there's a huge amount of work that just comes just to inherit all that from the upstream but in addition to that we've put a lot of work into making sure that well our crypto has to be Fitz certified right which means you've got to meet standards we also have work that's gone in to make sure that you can enable a security policy consistently across the system so that no application that you load on can violate your security policy we've got enough tables in their new firewalling Network bound disk encryption that actually it kind of ties in with a lot of the system management work that we've done so a thing that I think differentiates rl8 is we put a lot of focus on making it easy to use on day one and easy to manage day two well we're not getting there were there what that allows us to do is to take the reference designs that we have and the testing that we've we've previously validated with Intel and Red Hat and be able to snap pieces together so it's just a matter of what's different and unique for the client in the client situation and their growth pattern what's great about trueskill is that in this model is that we can predictably analyze or consumption forward based on the business growth so for example if you're using open shipped and you start with a small cluster for say one or two lines of business as they adopt DevOps methodologies going from either waterfall or agile we can we can predictably analyze the consumption forward that they're going to need so they can plan years in advance as they progress and as such the other snap-ins say uh storage that they're going to need for data and motion or data at rest so it's it's actually smarter and what that ends up doing is obviously saving the money but it saves some time you know typical model is going back to IT and saying we need these servers we need the storage and the software and bolt it all together and the IT guys are you know hair on fire running around already so so they can you know as long as IT approves it they can sort of bypass that that big heavy lift we're trying to do is create role models for women and girls who would like to participate in technology but perhaps are not sure that that's the way that they can go and they don't see people that are like them so they're less tendency to join into this type of communities so with the community award winner we're looking at a professional who's been contributing to open source for a period of time and with our academic winner we're looking to spur more people who are in university to think about it and of course the big idea is you'll all be looking at these women as people that will inspire you to potentially do more things with open source and more things with technology we've been hearing for many many years that we definitely need to have more gender diversity in tech in general in an open source and Red Hat is kind of uniquely situated to focus on the open source community and so with our role is the open source leader we really feel like we need to make that commitment and to be able to foster that right so so Sierra's a supercomputer and what's unique about these systems is that we're solving there's lots of systems that network together maybe are bigger a number of servers than us but we're doing scientific simulation and that kind of computing requires a level of parallelism and it's very tightly coupled so all the servers are running a piece of the problem they all have to sort of operate together if any one of them is running slow it makes the whole thing go slow so it's really this tightly coupled nature of supercomputers that make things really challenging you know we talked about performance if if one server is just running slow for some reason you know everything else is going to be affected by that so we really do care about performance and we really do care about just every little piece of the hardware you know performing as it should so we thought okay let's take all of these best practices that we have and build more or less a methodology around it how to make this actually works like how to do this we really broke it down into like individual sprints do dissin sprint one the distance sprint do to really have the results within three months six months 12 months whatever the places that you want to run on and then we realize talking to customers this by itself isn't still enough so that's why we started to open up this to an entire ecosystem so we brought ecosystem partners along like working closely with red a lot of other companies but also system integrators who can help us we speak up projects because we as a company are software companies we're not a services or consulting company and we do support customers and some of those engagement but if you think of like a really fortune 500 company that's a multi-year project it will keep hundreds of busy people busy so to recap like built-in methodology we built the ecosystem to deliver on that promise at scale and now the last step was we as we were doing this we also built like a reference architecture for it and was just in an internal IDE so how do we like structure this bill that reference architecture and then realize okay I think it's kind of like super helpful for customers so that this way we then decided to open source this reference architecture is fabric as well to like the entire software community so they can also use it so technically these three pieces it's the methodology it's the ecosystem and it's like the reference architecture that you can work with to help you achieve you [Music]

(upbeat music) >> Announcer: Live from Boston, Massachusetts, it's theCUBE, covering your Red Hat Summit 2019. Brought to you by Red Hat. >> And welcome back to Boston, we continue our coverage here in theCUBE of the Red Hat Summit and welcoming now to theCUBE stage for the first time, I believe, David Gledhill. Who is the group chief information officer and head of group technology and operations at DBS Bank. David, good morning to you. >> Morning, hi, it's great to be here. >> All the way from Singapore, and he was early for the segment this morning. So you get extra points for that, congratulations. >> Put that up to jet lag. (men laughing) >> Thanks for being with us. David, anymore we talk about companies in general, everybody says everybody's a tech company now, right? Not the way it used to be. How is that playing out in your world in financial services as far as how deeply ingrained you have to be with the technology? >> Yeah, so very much, we are. Tech transformation started about 10 years ago. Been CIO of the company about 10 years. And frankly, the first five years were just fixing the basics. So getting in place what we'd call world-class systems. Doing a bunch of stuff on resilience and security and all of that kind of stuff. And the other thing, and this is the dramatic change, you know 10 years ago when I joined the company, we were 85% outsourced to managed service vendors. So I had technology people that basically were signing contractors and managing service agreements. We didn't have technology DNA. Over those five years and a full 10 years actually. We've been doing a lot about just insourcing and rebuilding our technical muscle if you like. So now we're, we've gone from 85% outsourced to 90% insourced. So we run, build and manage our own. We're now a technology company. >> And five years ago, we had a real big shift and we were closest to what was going on in China and so probably saw this before many, many of the other banks saw this around the world. Of what Alibaba was doing with Ant Financial and Tencent and this whole, just complete disruption of how customers interact with the banking industry. So we got an early lead on this digital transformation and really for the last five, six years have been doubling down on building a pure digital offering and we see ourselves as a technology company providing banking services, not as a bank with some technology department in the backend. >> Yeah, I'd love if you can, a little bit, to help us dig into that because, I think back it was okay, what does digitization mean 10 years ago, it was like oh, okay, I need to make sure I have a good website and maybe a good mobile app. Which is a fine starting piece, and also the piece you talked about is when it was outsourced, I'm managing pieces but I sign up for what I need today and when the business needs something, those outsources aren't necessarily tied to them, so you're playing telephone with them. Most the companies I've talked to that have brought skills back inside, it's because the needs of the business are constantly asking for more and it can't be well, it's not part of our contract with what we have. We'll get to that in a year or two. >> Yeah, yeah, so that's a very interesting shift. It plays out in a number of different dimensions. First of all, let me go into that question of business and tech and that separation. When we were managed service, it was literally writing contracts and the specs and handing to vendors. It was just a horrible process. And how can you be a modern technology firm like that? So insourcing, for us, was one big thing to owe those people, but when you insource, then you end up with a business unit and a technology unit. And you still have got silos, so how do you break that? Because that really is a problem. If you look at the way technology companies work, they don't work like that. Five years ago when we said, okay, how do you make that flip to being the digital company? We went very deep into how some of the great technology companies operate. We wanted to understand, what is it? How does that DNA work? How does that culture work? How do they organize themselves? How do they build technology? How do they become agile, speed to market? And so we looked at, we studied Google, Amazon, Netflix Apple, LinkedIn, Facebook and we call them the Gandalf companies. And we said, how can we be more like them? Well, a Gandalf is missing a D. And fortunately at DBS, we happen to have a D. (laughing) So our goal became how do we become the D in Gandalf? And that was just like a lightening rod through the organization because all of the sudden it said to our people, forget about biz and tech and things. You need to think about how these technology comp operates and be more like them. Which means there's no separation, there are no silos we are together building great technical products for our customers so we need to re-think the organization to make sure that happens. >> There's magic. (laughing) You've got a saying, making banking joyful. >> Yep. >> All right, which is not exactly the emotion that I associate with having to deal with my bank. It's fine, but joyful? A very unusual adjective there. What's that all about? And again, at the end of the day, how does technology enhance that? How does that compliment that and really boosted that? >> Yeah, so it was quite a radical moment for us. That came up, we were at a leadership meeting and talking about what is our purpose and how do we. Lots of people talk about customer journey, thinking and stuff like that but how do you bring that to life and one of the execs there said, well what about making banking joyful? And the rest of us just looked at him like he was from a different planet. Saying, are you kidding, what do you mean by that? But as we thought about it more, it has a great meaning and a great purpose to it, that we're not there just to do transactions but we're there to enrich lives, create new businesses, to make customers feel great in their financial stability, in the way they deal with us. And it applies on so many levels. So if you're a bank teller, you understand how to make banking joyful by just that, going the extra mile, in terms of service. If you're in infrastructure, and you're dealing with data centers and servers, you understand that making banking joyful, you must be there all the time. You must have sub-second responses, you must feel great in the customers hands, so it's something that you can apply to all aspects of banking and everybody plays a part in making banking joyful for our customers. >> All right, so David, bring us inside a little bit your organization, you said transforming to a technology company. What's that mean, what technology are you using? We're here at the Red Hat Show. Open-source, not the first thing that people think about when they they think about banking. So how does that fit into the culture that you're building? >> Yeah, yeah, okay, so, this Gandalf thing that I talked about, that's great as a logo and a mindset shift, but it doesn't get you very far. And so what we came up with is five key elements that have to change. And we had to work on to become more like a technology company. And one was a shift from projects to running technology like a series of platforms. That enables you to do agile at scale, but for that you need to organize very differently, which is the third thing. And then the fourth thing is that you need to build for modern systems. The legacy way of building technology just wasn't going to get us to where we needed to be as a technology company. And then the last bit is alternate everything. So, if you want speed to market and agility, you have to alternate. That modern technology stat, it was very obvious to us that the legacy, corporate technologies that we used to build systems, were just not going to win it for us. And so that's our move to open-source. Red Hat was a fabulous partner in that and we used Red Hat extensively throughout our entire infrastructure. And so we went through this rapid modernization of moving to open-source, moving to open-source database we used Merare DD quite extensively, but also, picking up pieces of the open-source from the Gandalf companies. We've seen the way they use open-source to scale. Plus also, to provide just amazing services. So for example, Netflix. We run a bunch of banking platforms on Netflix, believe it or not. It's kind of cool, banking on Netflix is a kinda crazy concept, but we brought that do life. What is is that Netflix we loved? We loved their engineering discipline around chaos engineering and the use of chaos to really build resilient platforms. So in our whole test and deployment framework, we have a lot of Netflix chaos elements built into that to make sure that when we actually are testing, we're testing for chaos and random failures which we inject into those platforms. We don't do it in production like Netflix do quite yet. This whole concept of site reliability and chaos and excellence of service is again something we learned from the Gandalf companies. So Gandalf was not just a, oh yeah, let's pester in the heart of the business article. It was really, let's use their engineering disciplines and design principles to build our own systems. Our network, Facebook, which is, you think of it as a network company. We think of network and the infrastructure layer. And our infrastructure and our networking is designed on a bunch of concepts that Facebook have about how they build their network within their data centers. >> Can you help connect the dots, you talk about a phenomenal technologies, chaos engineering, networking like these global companies. How does that lead to the outcome that you talked about? You know, joy to your end users? >> So if you want to make banking joyful, you have to be super-reliable. You have to be on the edge of the innovation curve all the time. Which means you need to be test and learn, which means you'll be very agile. You need to be able to scale very well and the open-source technologies enable us to scale superbly. You need to be able to to perform as well, superbly well. When I joined the company, our measure of how well our applications were performing is are they up or down? And then we advanced that to, well, are they up and maybe 80% of the time they responded within four seconds. Those are terrible measures because they're not joyful. That means 20% of the time we're awful. That doesn't bring joy to a customer. So what brings joy is we've now scrapped all those things and we're starting to look at performance, for example at the 99th percentile, so anything below that is just noise and the signal is what is our worst performing 99%, because if you wanna be joyful every single time that a customer opens your application you wanna be there and respond well, et cetera, et cetera. Same thing goes for customer science. Where do you get customer drop offs and how do you fix problems? So customer science and the engineering disciplines around observing and instrumenting a platform all the time become very , very important. So it goes very, very deep. You know it's a simple concept. But totally changes the way we engineer. >> Your line of work, or your industry obviously is very security oriented, right? >> Yeah. >> I think of healthcare being another with health information what have you. But certainly financial services, so in the open-source community, how do you address this, I would say it's not a clash by any means, but it's a concern, I would think, still that you have to be micro-observant of security practices and yet this is an open-community and exchange of ideas and could be an exchange of vulnerabilities or problems, too. >> So, sure, and we absolutely do. There are certain things that we, certain places that we won't go, or we will go but only for experimentation reasons because of that question. But you know arguably, we think that open-source company can more secure over time than non-open source, because you're also getting a bunch of people fixing it the whole time. And we've seen some of the issues with some of the open-source and the heart bleed and those other bits and pieces. But they were shut down pretty quickly and found pretty quickly. So we move with caution, we're very cognoscente. We move with our eyes open and it's really the zero day vulnerabilities that we are exposed to. But equally, if you're in a proprietary state. The whole thing that came up with the X86 platform in terms of the vulnerabilities there that apply open-source or not. So yeah, security issues exist everywhere. >> Right, all right, so, David, bring us in. You talked about some of the open-sourced technologies. Where does Red Hat fit into this? What's it like, how have they advanced that journey that DBS has been on? >> So for the heavy lifting, for the big applications that we want to run, and the majority of our workload going with open-source is fine, but you want to have open-source that also you think isn't going to break or have big security vulnerabilities to your question. And that's really where a partner like Red Hat comes in because it gives us access to all the wonderful benefits of open-source with a trusted partner that's putting industrial strength quality releases out that we can really rely on and bank on. So, in awhile we used open-source, at the periphery and true open-source that we just plug it off the internet. The really very, very high demanding workloads and very secure workloads we will always work with a partner that can wrap that into an enterprise-quality offering for us. So Red Hat has gone from zero to running way over 50%, 60% of our workload. And we'll continue to put it even more on that because it's a platform we can trust. >> That's great, so, when you look at your journey overall, how far are you along that journey? Anything when you look out, what are some of the things that are exciting you looking forward? >> So while we believe we're ahead of most banks. There are some that are in the mix, Goldmans are pretty far advance, Duetsche, a couple of the others, Capital One, a few. But it's a sort of rare breed. We're about 80%, 90% done on our transformation journey to get stuff to what we'd call cloud ready or optimized. But we think we're just scratching the surface because if you think about plugging ourselves into customers lives, making banking joyful, our external brand promise for that is live more, bank less. And nobody wakes up in the morning and says, oh, I can't wait to go to my local bank branch and go and do some banking. >> (laughing) I heard Steve talking about it yesterday. >> Actually there is one place. My wife loves going there because we do some great free cookies at DBS. >> John: Oh, excellent. >> But other that my wife, the rest of the planet doesn't really do that. >> John: Fair enough. >> If you want to live more, bank less, it's how do we get the toil of banking away from customers yet embed ourselves in their journeys. And for that we believe that this whole play on ecosystems is very important. So being a creator of an ecosystem or participating so that we take the banking toil out, and yet we inject ourselves, be that leisure or travel or insurance or whatever. And you don't see the bank, the bank is invisible. Then you're live more, bank less. To do that, you need great ecosystems. And we think three things help us to plug into ecosystems. Number one is you have to be able to scale very easily. And all the work we've done on the Gandalf stack means that were no longer afraid of scale, just bring it on. The second thing you need a lot of connectivity, and DBS two years ago, we launched the world's largest banking API platform. We went live with 150 different APIs and 60 live partners at the time. That's now grown to over 350 APIs and 100s of corporates and SME partners that wanna partner with us to pug us into their offering. So the more we do that, the more we disappear and let people live more, bank less. >> Well, next time, if you wanna bring some cookies with you, by all means, okay. (David laughs) We're always up for that, David, thanks for the time. >> Sure. >> We appreciate that and good luck on the mission and the journey there at DBS. >> Sure, thanks very much and great to be here, thank you. >> David Gledhill joining us this morning here, as we continue our coverage of the Red Hat Summit. We're in Boston. You're watching theCUBE. (upbeat music)

>> (Announcer) From Boston, Massachusetts, it's the Cube! Covering LiveWorx 18. Brought to you by PTC. >> Welcome back to the Seaport in Boston, everybody. This is day one of the LiveWorx show, PTC's big IoT user conference, but it's much, much more than that. My name's Dave Vellante, Stu Miniman. You're watching the Cube, the leader in LiveTech coverage. It's really our pleasure to have Devin Cleary here, he's the Director of Events at PTC. Dev, thanks so much for coming on The Cube, and thanks for putting together such a great show. >> Oh, thank you so much for having me. This is great. >> You're welcome. So, I say it's a user conference, but it's so much more. I mean, talk about what your intent was and what you've created, you and your team at LiveWorx. >> Absolutely. So for us, we take a step back in corporate events. And we're really trying to bring sort of a unique flair to the corporate events world. In a nutshell, we at PTC have a 25 year legacy of doing really powerful user events, and it was really an inspiration two years ago to kind of shake the mold. And again, no pun intended, be disruptive in the marketplace. So for us, we sort of coined a new term or strategy that we call Industry Inclusiveness. And this is something where we wanted to sort of break down the four walls of the company, and invite industry influencers, individuals who are leading the charge, inclusive of actual competitors, 'cause for us, it's better together. And the whole story and talk track around LiveWorx is collaboration accelerates innovation. So for us, we want to make sure we embrace a lot of different people, walks of life, and diversity, and the intent is to create a one time a week a year, successful program that focuses and profiles nine of the most disruptive technologies on the planet. So this is everything from robotics to AI, to IoT, to AR, blockchain, and so much more. And for us, this is really the essence of what LiveWorx has become, which again for us, we want everyone to know that this event is sort of the world's most respected digital transformation conference. >> So, couple things I want to point out. Well, so over 6,000 people here, the kickoff was in the theater-in-the-round I've only seen that-- We do over a hundred events every year, I've only seen it done twice, and it's worked both times. I think it's a home run when you do the theater-in-the-round. The intro was like, I tweeted out this morning, it was like an Olympic opening ceremony. I mean really, where do you get your inspiration from that? >> So, you know what, for us, I have a really amazing team that works with me and collaboratively. And for us, we really want to sort of challenge the status quo. So, we always look for things actually outside of the tech bubble, if you will. We look at music. We look at fashion. We look at art. We look at a lot of pop culture sort of references and that sort of stems our ideas of how we sort of nurture and create what we call the apex, or LiveWorx or what you saw this morning. And for us, I'm all about what I call delight moments. So these are moments that frankly are sort of above and beyond the core content of what the conference offers and just making people have a great time. Showmanship and entertainment is just as much important as the core again content that we offer at LiveWorx. >> Dev, you've got a big tent here with a lot of different topics. There's a show I go to, we talk about the random collision of unusual suspects, which this reminded me of. Can you talk a little bit about how in these diverse communities, yet we should see some overlap and some bumping together. >> Yeah. Absolutely. So, again with LiveWorx, and sort of again profiling these nine to ten most disruptive technologies out there, we're always trying to recruit people that are very diverse from various backgrounds. You know, one specific goal that we have, just from a geographic persepective is making sure that over half our audience is from international markets outside of the United States. So again, when you're bumping shoulders or walking the halls everywhere around us, you're guaranteed to hear someone that comes from a different walk of life, a different experience, a different educational background and that adds a lot of value to the overall conference. Now, again, we target everyone from administrators to engineers, developers and more because really this show runs the gamut on everything from product design and sort of the ideas of what you want to do, all the way through service, manufacturing, it is the full scope of industry 4.0. So, to your point, there's a lot of intersection and a lot of overlapping because every company, every person, every individual, wants to experience and learn how to embrace what we call disruptive tech. >> You know, again, we do a lot of shows and the vast majority, when someone like you guys brings us to a show, they want to showcase their products and basically pimp up their own stuff. You chose a different approach. First of all, thank you for that. So, this today has been all about thought leadership. Stu and I were saying it reminds us of some of the stuff we do with MIT. Where you have professors, you have thought leaders, talking about not, kind of frankly, some boring products. >> And it's not a sales pitch. >> Right, it's not a sales pitch. But, why that decision and what's your vision for where you want to take this thing? >> Yeah, so again, I would say that a lot of conferences, and this is no offense to my brothers and my sisters in the events world out there, but people are so sick and tired of going to the standard trade show. The days of pipe-and-drape and aisles of just being pitched to and receiving free stress balls, and hiring staff that might not even be employed by the company, but they just frankly look good, those days are completely over. In our audience, the technologists who really matter in this world, who are doing a lot of great work, they want that substance and that core content. So, for us, it's really a vision about that's embraced and sort of evolved into give back and let the content lead your success. And that is going to help amplify the voice and further the mission. We look at LiveWorx as a catalyst well beyond the company that employs me and the people that work for just these companies. We have a vision to make Boston an epicenter, a headquarters, a world-renown attraction for technologists world-wide knowing this city for IoT and for AR. And for us, we embrace the innovation district as that pallet, that backdrop, that environment to allow us to really accomplish that. So, LiveWorx is growing exponentially. We experienced double digit growth this year, which was amazing. Starting where I was only with this company two years ago and less than 25 hundred attendees and we're at 6,100 right now live on the show floor at LiveWorx. So the future is really bright for us, and we're embracing this notion of the convention center is only going to be constricting for so long. It's time that we also implode those four walls and we embrace the outside. And what our plans are going for, which I'm really excited to sort of announce, is we're going to be now becoming more of an industrial innovation week in Boston, and taking our plans mainstream. So, that means taking the content that we focus on, and the partners that we work with, and the industry thought leaders and now you start to actually replicate these events throughout the entire seaport. So, think of it, and again most of you know South by Southwest, I'm a big fan and an avid follower, think of it South by Southwest meets Industrial, and that is the future of this show. >> Love it, and you know, we're thrilled to be part of it. And it's palpable. You actually see now, in the seaport... You know, we were talking off camera, you can't compete with Silicon Valley or on terms with Silicon Valley does. You shouldn't even try. We're bicoastal, we have an office in Palo Alto we know it well. It's a unique vortex. But certainly, IoT, Blockchain, VR, there really is some clear innovation going on here so, if you can focus on that, you can actually really blossom an ecosystem and that's really what you're doing. >> Oh, absolutely. And, again, PTC has been headquartered here for over 25 years, they're a leader in industrial innovation. They're a company that believes in giving back. We have curated and nurtured through partnerships with Harvard Business School, with MIT Innovation Lab, etc. We have cultivated some of the greatest startups of our time right now, who are creating groundbreaking technology in IoT, in AR, that is changing the world. We're even actually doing work right now in our backyard with Boston Children's Hospital, for example. Doing incredible work with our Vuforia product in AR that's helping actually find a cure for Alzheimer's. So, again, the possibilities are endless, and the innovation is limitless. >> Well, you're the hot company right now, obviously growing very rapidly, you're kind of like the Comeback Kid. You're clearly punching above your weight. The Scott Kirsner article in the Globe was unbelieveable. >> (Devin) Thank you I know we're very... Shout out to Scott. >> And so, you got to be thrilled with that. But, what's interesting to me, Dev, is you're not... You could ride that wave, and just pump up PTC but you're doing things that will allow you to sustain this as a community member, paying it forward, you know, it's kind of a cliche, but that's what I see. Thoughts? >> A hundred percent. And, again, the way that we sort of frame LiveWorx is I want you to think of PTC as the presenting sponsor. They are an investor in the vision that this team has to carry forward the community and the movement all around industrial innovation. And again, we feel that Boston being sort of our headquarters in our backyard, it's important that we're giving back and again, furthering that opportunity to further solidify our right as a rightful heir of IoT and AR, as a city, as a community and as the state of Massachusetts. >> Dev, wondering if you could give our audience that didn't come to this event a quick flavor of what's going on, flavoring and I loved you had the Boston food trucks all right outside. They're a little warm. My friends from the west coast are like, "This isn't warm." But for Boston, it hit summer. But, give us a quick tour around what people missed. >> Yeah, so we're all about an immersive experience at LiveWorx. Again, you're going to have sort of a checklist of what you absolutely need to have at an event to sustain someone's expectations. So, the content, the networking, the value. But again, we like to take it a step further and things that I call delight moments. So, for example, this year in Extropolis, and Extropolis, for those of you at home, that is our sort of expectation shattering, ground-breaking, playground for adults in technology. So, every corner, every ounce, every inch of this show floor has something to engage, ignite the 5 senses and tell our story. And one example specifically that I love to highlight this year is I've actually created the vision with a whole slew of individuals from PTC and partners and whatnot. Something we call the X-factory. Manufacturing is one of the biggest industries in business in the world. Mostly every company at an enterprise level has some sort of manufacturing component to it. And what we wanted to do this year is create the factory of the future. Meaning, working with the leaders like McKinsey, and again HeroTech and global brands in Germany who are defining manufacturing and who founded manufacturing in our history, we have partnered with them to say, "What does that factory of the future look like? What are companies going to be doing five, ten, fifteen years from now and what can we expect?" You're getting that first at LiveWorx, which is awesome, and the whole process is "Let's not have a standard kiosk. Let's not do a laptop with a video. Let's actually build out a 20,000 square foot industrial factory with multiple stations from digital engineering to service to again, AR induced digital twins and everything else in between. And let's actually have every single attendee create, design and manufacture a smart connected product. We're working with our partner, Bell and Howell, from a shipping, service and supply chain perspective, and again, we are blowing the roof off this show on that one activation, and there's over a hundred in total throughout this entire show this week. So, that's a little bit of a flavor of LiveWorx. And beyond that, we do things, everything from a puppy daycare hour to sort of do a high tech low touch feel. We do incredible food presentations and we're going to be ending with a big bang tomorrow with our closing party called the Mix-It Six, which is one of my favorite programs the entire week. And that is actually a superhero themed event where we're actually having a guest host and a personal friend, Paul Rudd, who was the Ant Man for Marvel, he'll be hosting our event. And the whole notion around superheroes is that we tell everyone this week "Unleash your inner superhero". Take advantage of the technology that is on display, and realize how it can enable and empower you to now have superhuman powers. So, everything from AR giving you the power to see the invisible, to IoT helping you get the power to predict the future. Everything is possible and everything is creative at LiveWorx. >> Well, it's obviously working. And so, I'm sure the execs are seeing this going, "Great. Good Job. Way to go. We've got some momentum. Let's double down." But, you back up two years ago, how did you sell this to the folks? Cause we see a lot of guys like, "Alright, how many leads we going to get out. How much revenue we going to drive" How'd you get through that knothole? >> So, let's put it in this perspective. There's a lot of intrinsic and intangible ways to measure the success of a show, and the value and the impact brought to a company. One thing I would actually say, I've worked in the tech industry for over six years now, I've been in the events business for over a decade, I've worked for some of the most incredible and impressive, and media-driven startups in the world right now. PTC, though, is a very interesting ecosystem. Their executives actually embrace the notion of what I presented first and foremost, about again, industry inclusiveness as we call that term. And for us, we have a vision at PTC to be disruptive, to be ground-breaking. If we do not embrace that ourselves, as our culture and our business model, how do we hope someone else to believe in the product, and the vision and the mission that we set forth in the marketplace. >> And from that, you got a response of, "Yeah, let's do it." >> So, again, am I going to be a hundred percent honest and transparent? Was everyone embracing that a hundred percent? No. But again, I think the proof is in the pudding and I think again it's a leap of faith in saying, "Listen, take a chance. Be disruptive, and see what the product of our fruits of our labor could be." And again, here you have it three years later, triple the size of the audience, tripling the size of the success, seeing multiple customers, multiple partners multiple industry leaders now attaching themselves to this brand. So for us, LiveWorx is nothing greater than a record breaking success this year, and I'm so excited for the rest of you at home to experience on the live stream, or again check out 2019 June 10-13. >> June 10, right here. Right? >> (Devin) Right here again. >> Dev, first of all thanks so much for having The Cube here and making us a part of this awesome event and look forward to working with you in the future. Congratulations on all your success. >> Thank you so much. >> You're very welcome. By the way, check out thecube.net that's where all the videos here will be. Check out siliconangle.com all the editorial coverage. Wikibond.com is where the research is. We're a wrap here from LiveWorx day one. Dave Vellante, for Stu Miniman. Thanks so much for watching, we'll see you next time.