 From around the globe, it's theCUBE with coverage of KubeCon and CloudNativeCon Europe 2020 virtual. Brought to you by Red Hat, the CloudNative Computing Foundation and ecosystem partners. Welcome back, I'm Stu Miniman. This is theCUBE's coverage of KubeCon, CloudNativeCon Europe 2020, the virtual event. Of course, when we talk about CloudNative, we talk about Kubernetes. There's a lot that's happening to modernize the infrastructure. But a very important thing that we're gonna talk about today is also what's happening up the stack, but sits on top of it in some of the new use cases and applications that are enabled by all of this modern environment. And for that, we're gonna talk about artificial intelligence and machine learning or AI and ML as we tend to talk in the industry. So happy to welcome the program. We have two first time guests joining us from Red Hat. First of all, we have Avanav Joshi and Tushar Katarki. They are both senior managers, part of the OpenShift group. Avanav is in the product marketing and Tushar is in product management. Avanav and Tushar, thank you so much for joining us. Okay, thanks a lot Stu, you're glad to be here. Thanks Stu, and glad to be here at KubeCon. All right, so Avanav, I mentioned in the intro here, modernization of the infrastructure is awesome, but really it's an enabler. We know I'm an infrastructure person. The whole reason we have infrastructure is to be able to drive those applications, interact with my data and the like. And of course, AI and ML are exciting, a lot going on there, but can also be challenging. So Avanav, if I could start with you, bring us inside your customers that you're talking to. What are the challenges, the opportunities? What are they seeing in this space? Maybe what's been holding them back from really unlocking the value that is expected? Yep, that's a very good question to kick off the conversation, right? So what we are seeing is organization, they typically face a lot of challenges when they're trying to build an AI, ML environment, right? And the first one is like a talent shortage. So there is like a limited amount of the AI, ML expertise in the market, and especially the data scientists that are responsible for building out the machine learning and the deep learning models. So yeah, it's hard to find them and to be able to retain them and also other talent like a data engineer or an app dev folks as well. And the lack of talent can actually install the projects, right? And the second key challenge that we see is the lack of the readily usable data. So the businesses collect a lot of data, but must find the right data and make it ready for the data scientists to be able to build out, to be able to test and train the machine learning models. If you don't have the right kind of data, so the predictions that your model is going to do in the real world is only going to be so good. So that becomes a challenge as well to be able to find and be able to like wrangle the right kind of data. And the third key challenge that we see is is the lack of the rapid availability of the compute infrastructure, the data machine learning and the app dev tools for the various personas like data scientists or data engineer, the software developers and so on. If that can also slow down the project, right? Because if all your teams are waiting on the infrastructure and the tooling of their choice to be provisioned on a recurring basis and they don't get it in a timely manner, it can be, yeah, it can install the projects. And then the next one is the lack of collaboration. So you have all these kinds of teams that are involved in the AI project, right? And they have to collaborate with each other because the work one of the team does has a dependency on a different team. Like say, for example, the data scientists are responsible for building the machine learning models. And then what they have to do is they have to work with the app dev teams to make sure the models get, say integrated as part of the app dev processes and ultimately rolled out into the production. So if all these teams are operating in say silos and there is lack of collaboration between the teams, so this can stall the projects as well. And finally, what we see is the data scientists, they typically start the machine learning model, the modeling on their individual, say PCs or laptops, right? And they don't focus on the operational aspects of the solution. So what this means is when the IT teams have to roll all this out into a production kind of a deployment, so they get challenged to take all the work that has been done by the individuals, right? And then be able to make sense out of it, be able to make sure that it can be seamlessly brought up in a production environment in a consistent way, be it on-premises, be it in the cloud or be it say at the edge. So these are some of the key challenges that we see that the organizations are facing as they say try to take the AI projects from say pilot to production. Well, some of those things seem like repetition of what we've had in the past. Obviously silos have been the bane of IT moving forward and of course for many years, we've been talking about that gap between developers and what's happening in the operations side. So Tushar, help us connect the dots, containers, Kubernetes, the whole DevOps movement, how is this setting us up to actually be successful for solutions like AI and ML? Sure, Stu, I mean, in fact, you said it, right? I mean, you know, like in the world of software, you know, in the world of microservices, in the world of app modernization, in the world of DevOps in the past 10, 15 years, we have seen this evolution, revolution happen, right? With containers and Kubernetes, driving more DevOps behavior, very more agile behavior. And you know, so this in fact is what we are trying to say here can ease up the cable to AI ML also. So the value of containers, Kubernetes, DevOps and OpenShift for software development and is directly applicable for AI projects to make them more agile to get them into production to make them more valuable to organizations so that they can realize the full potential of AI. You know, we already touched upon a few personas. So it's useful to think about who the users are, who the personas are. You know, I mean, I've talked about data scientists. These are the people who obviously do the machine learning itself, do the modeling, you know. You know, then there are data engineers who do the plumbing, who provide the essential data. I mean, data is so essential to machine learning and deep learning. And so there are data engineers that are app developers, you know, who in some ways will then use the output of what the data scientists have produced in terms of models, and then incorporate them into services. And of course, you know, none of these things are purely cast in stone. There's a lot of overlap. You could find that data scientists are app developers as well. You'll see some of app developers being data scientists, similar with data engineers. So it's a continuum rather than strict boundaries. But regardless, you know, what all these, you know, personas or groups of people who need or experts need is, you know, self-service to their preferred tools and compute and storage resources to be productive. You know, so, and then let's not forget the IT engineering and operations teams that need to make all this happen in an easy, reliable, available manner and something that is very secure. So containers help here, right? They help you quickly and easily deploy a broad set of machine learning tools, data tools across the cloud, the hybrid cloud from, you know, data center to public cloud to the edge in a very consistent way. You know, teams can therefore, iteratively modify, you know, change a shared container images, machine learning models with versioning and track changes. And this could be applicable to both containers as well as to the data, by the way, and, you know, and be transparent. You know, transparency helps in collaboration but also it could help with regulatory reasons later on in the process. So, you know, and then with containers because of the inherent process isolation, resource control and production from threats, they can also be very secure. Now, Kubernetes takes it to the next level. It, first of all, it forms a cluster of all your compute and data resources. It helps you to run your containerized tools and whatever you develop on them in a consistent way with access to these shared compute and centralized computing storage and networking resources from the data center, the HR, the public cloud. They provide things like resource management, workload scheduling, multi-tenancy controls so that you can be a proper neighbors, if you will, and quota enforcement, right? And so the value of, now, that's Kubernetes. Now, if you want to up-level it further, if you want to enhance what Kubernetes offers, then you go into how do you write applications? How do you actually make those models into services and that's where, and how do you lifecycle them? And that's where the power of Helm and furthermore, Kubernetes operators really comes into the picture while Helm helps in installing some of this for a complete lifecycle experience. You know, a Kubernetes operator is the way to go and they simplify the acceleration and deployment of the lifecycle management from end to end of your entire AI ML tool chain. So all in all, you know, organizations, therefore you see that they need to develop and deploy models rapidly just like applications. That's how they get value out of it quickly. You know, there is a lack of collaboration across teams as I've pointed out earlier, as you noticed that has happened still in the world of software also. So we're talking about, you know, how do you bring those best practices here to AI ML? DevOps approaches for machine learning operations for, you know, many analysts and others have started calling as ML ops, right? So how do you kind of bring DevOps to machine learning? You know, fosters better collaboration between teams, application developers, and IT operations and create this feedback loop so that the time to production and the ability to take more machine learning into production and ML powered applications into production increases significantly. So that's kind of where I wanted to, you know, shine a light on what you were referring to earlier. So. All right, Abhinav, of course, one of the good things about OpenShift is you have quite a lot of customers that have deployed the solution over the years. Bring us inside, you know, some of your customers, you know, what are they doing for AI ML and, you know, help us understand, you know, really what differentiates OpenShift in the marketplace for this solution set. Yeah, absolutely. That's a very good question as well. And we're seeing a lot of traction in terms of all kinds of industries, right? Be it the financial services, like healthcare, automotive, the insurance, oil and gas, manufacturing, and so on, right? Or a wide variety of use cases. And what we are seeing is at the end of the day, like all these deployments are focused on helping improve the customer experience, be able to automate the business processes, and then be able to help them increase the revenues, serve their customers better, and also be able to save costs, right? If you go to openshift.com, forward slash like AI-ML, it's got like a lot of customer stories in there. But today I want to touch on three of the customers we have in terms of the different industries. The first one is like Royal Bank of Canada, right? So they are a top global financial institution based out of Canada, and they have more than 17 million clients globally, right? So they recently announced that they build out an AI-powered private cloud platform that was based on OpenShift as well as the NVIDIA DGX AI compute system. And this whole solution is actually helping them transform the customer banking experience by being able to deliver an AI-powered intelligent apps and also at the same time, being able to improve the operational efficiency of their organization. And now with this kind of a solution, what they're able to do is, they're able to run thousands of simulations and be able to analyze millions of data points in a fraction of time as compared to the solution that they had before. Yeah, so like a lot of great work going on there. Next one is the HCA Healthcare, right? So like HCA is one of the leading healthcare providers in the country, and they're based out of the Nashville in Tennessee, and they have more than 184 hospitals, as well as more than 2,000 sites of care in the US, as well as in the UK. So what they did was, they developed a very innovative machine learning powered data platform on top of OpenShift to help save lives. The first use case was to help with the early detection of sepsis, like it's a life, a threatening condition. And then more recently, they've been able to use OpenShift and the same kind of a stack to be able to roll out the new applications that are powered by machine learning and deep learning, say to help them fight COVID-19, right? And recently they did a webinar as well that had all the details on the challenges they had, like how did they go about it, like the people process and technology, and then what the outcomes are. So, and we are proud to be a partner in the solution to help with such a noble cause. And the third example I want to share here is the BMW Group and our partner DXE Technologies, right? What they've done is, they've actually developed a very high performing a data-driven data platform, development platform based on OpenShift to be able to analyze the massive amount of data from the test fleet, the data and the speed of the, say to help speed up the autonomous driving initiatives. And what they've also done is, they've redesigned the connected drive capability that they have on top of OpenShift. That's actually helping them provide various use cases to help improve the customer experience, right, with the customers. Now the customers are able to leverage a lot of different value add services directly from within the car, their own cars. And then like last year at the Red Hat Summit, so they had a keynote as well. And then this year at Summit, they were one of the innovation award winners. And we have a lot more stories, right? But these are the three that I thought are extremely compelling that I should talk about here on theCUBE. Yeah, I don't know, just a quick follow up for you. One of the things of course we're looking at in 2020 is how has the COVID-19 pandemic, people working from home, how has that impacted projects? I have to think that AI and ML, one of those projects that take a little bit longer to deploy, is it something that you see, are they accelerating it? Are they putting on pause, are new projects kicking off? Anything you can share from customers you're hearing right now, as to the impact that they're seeing this year? Yeah, what we are seeing is that the customers are now even more keen to be able to roll out the digital services that are powered by AI and ML at a fast pace. So we see a lot of customers are now on the accelerated timeline to be able to say complete the AI ML projects. So yeah, it's picking up a lot of momentum and we talk to a lot of analysts as well. And they are reporting the same thing as well. But there is the interest that is actually ramping up on the AI ML projects like across their customer base. So yeah, it's the right time to be looking at the innovative services that can help like improve the customer experience in the new virtual world that we live in now, but COVID-19. All right, Tushar, you mentioned that there's a few projects involved. And of course, we know at this conference that there's a very large ecosystem. Red Hat is a strong contributor to many, many open source projects. Give us a little bit of a view as to in the AI ML space, who's involved, which pieces are important and how Red Hat looks at this entire ecosystem. Thank you, Sue. I mean, so as you know, I mean, technology partnerships and the power of open is really what is driving the technology world these days in any way. So, and particularly in the AI ecosystem. And that is mainly because one of the machine learning is bootstrap in the past 10 years or so. And a lot of that emerging technology to take advantage of the emerging data as well as compute power has been built on the kind of the Linux ecosystem with openness and languages like popular languages like Python, et cetera. And so what you, and of course, TensorFlow, which is based in Java, but the point really here is that the ecosystem plays a big role and open plays a big role. And that's kind of Red Hat's cup of tea, if you will. And, you know, Red Hat really has plays a leadership role in the open ecosystem. So if we take your question and kind of put it into two parts, what is the, what we are doing in the community and then what we are doing in terms of partnerships in terms of commercial partnerships, technology partnerships, we'll take it one step at a time. In terms of the community itself, you know, if you step back to the three years, you know, we worked, you know, with other vendors and users, including Google and NVIDIA and H2O and other seldom, et cetera, both startups and big companies to develop this Kubeflow ecosystem. The Kubeflow is an upstream community that is focused on delivering ML ops as we talked about earlier, end-to-end machine learning on top of Kubernetes, right? So Kubeflow right now is in 1.0. It happened a few months ago now, it's actually at 1.1. You'll see that KubeCon here. And then, so that's the Kubeflow community. In addition to that, we have augmented that with the Open Data Hub community, which is something that extends the capabilities of the Kubeflow community to also add some of the data pipelining stuff and some of the data stuff that I talked about and forms a reference architecture on how to run some of this on top of OpenShift. So the Open Data Hub community also has a great way of including partners from a technology partnership perspective. And then tie that with something that I mentioned earlier, which is the idea of Kubernetes operators. Now, if you take a step back, as I mentioned earlier, Kubernetes operators to help manage the lifecycle of the entire application, containerized application, including not only the configuration on day one, but also day two activities like update and backups restore, et cetera, whatever the application needs for proper functioning that a quote-unquote operator needs for it to make sure. So anyways, the Kubernetes operator ecosystem is also flourishing and we haven't placed that with the Open Operator Hub.io, which is a community marketplace, if you will. You know, I don't want to call it marketplace, a community hub because it's just comprised of community operators. So the Open Data Hub actually can take community operators and can show you how to run that on top of OpenShift and manage the lifecycle. Now, that's the reference architecture. Now, the other aspect of it really is, as I mentioned earlier, is the commercial aspect of it. It is from a customer point of view, how do I get certified, supported software, right? And to that extent, what we have is at the top of the, from a user experience point of view, we have certified operators and certified applications from the AIML ISV community in the Red Hat Marketplace. And from the Red Hat Marketplace, it's where it becomes easy for end users to easily deploy these ISVs, some of the, and manage the complete lifecycle, as I said. So some of the examples of these kinds of ISVs include startups like H2O, although H2O is kind of well known in certain sectors, you know, Perceptilabs, Converge, Selden, Starburst, et cetera. And then on the other side, we do have other big giants also in this, which includes partnerships with NVIDIA, Cloudera, et cetera, that we have announced, including also SAS, I got to mention. So anyway, so these create that rich ecosystem for data scientists to take advantage of. You know, I'd like to summit back in April, April, we along with Cloudera, SAS, Anaconda, showcase a live demo that shows all these things to working together on top of OpenShift with this operator kind of idea that I talked about. So I welcome people to go and take a look, you know, at the OpenShift.com slash AIML, that I've been on earlier, reference should have a link to that, simple Google search might also reveal some of that. But anyways, so, and the other part of it is really our work with the hardware OEMs, right? And so obviously NVIDIA, GPUs is obviously hardware and that acceleration is really important in this world. But we are also working with our OEM partners like HP and Dell to produce this accelerated AI platform that are the key solutions to run your data, you know, to create this AI, open AI platform for the quote-unquote private cloud of the data center. The other thing obviously is IBM. IBM CloudPack for data is based on OpenShift. That has been around for some time and you're seeing very good traction there. You know, if you think about a very turnkey solution, IBM CloudPack is definitely, you know, kind of well ahead in that. And then finally, Red Hat is about driving innovation in the open source community. So as I said earlier, we are doing the open data hub, which is that reference architecture that showcases a combination of upstream open source projects and all these ISV ecosystems coming together. So I welcome you to take a look at that at opendata hub.io. So I think that would be kind of the, you know, some total of how we are not only doing open community building, but also doing certifications and providing to our customers that assurance that, you know, they can run these tools in production with the help of a rich certified ecosystem. And customer choice is key to us, right? So that's the thing that the goal here is to provide our customers with a choice, right? Yeah, they can go with the open source or they can go with the commercial solution as well. So you want to make sure that they get the best in class experience on top of OpenShift and a broader portfolio as well. All right, great, great note to end on Abhinav. Thank you so much. And Tushar, great to see the maturation in this space. Such an important use case. Really appreciate you sharing this with the cube and KubeCon community. Thank you. Thank you, thanks a lot. And have a great rest of the show. Thanks everyone. And stay with us for lots more coverage from KubeCon, CloudNativeCon, Europe 2020, the virtual edition. I'm Stu Miniman and thank you as always for watching the cube.