 Hi, I'm Michael Ducey, Senior Manager of Managed OpenShift Black Belt here at Red Hat. And with me today is Neeraj. Hi, this is Neeraj Ducey, and I'm part of Managed OpenShift Black Belt team. And today we are here to talk about Red Hat OpenShift Data Science. So we've heard a lot about AI, machine learning, data science. What specifically is Red Hat OpenShift Data Science? So Red Hat, so companies these days invest a lot in AI workloads, and they are doing good at it. But there are lots of challenges that comes with it, right? Like giving access to the developers, giving the access in terms of hardware. There are security challenges, and many developers have diverse set of tool sets. So they can use some use different types of tool sets. So from a Red Hat OpenShift Data Science perspective, what we have is we have this Red Hat OpenShift Data Science offering, which sits on top of Managed OpenShift, which is a turnkey application platform. And with that, it can handle these diverse set of workloads that a developer needs in terms of running their AI workloads. So kind of walk me through this. How does it evolve where our data science interact with OpenShift data science, and kind of walk us through that workflow that we see with our customers? So to walk you through that, I would like to take you through an example, right? So Red Hat OpenShift Data Science sits on top of Managed OpenShift. And for example, in our everyday lives, we deal with banking, right? Like either you basically deal with banks in terms of your credit card transactions, your banking, mortgage. So from a data science perspective, when the banks want to mitigate risk, they basically run these machine learning AI algorithms. Sure, be credit card fraud, or accounts getting opened in your name, and various things. That's right. So it all starts with data. So basically, there are lots of transactional data, and there are lots of geographic data that basically data engineers prepare these data for data scientists. So once the data is prepared, basically the data scientist will build these machine learning models, AI models, and then they will work, the data scientist will basically work with the developers. So in this case, like for example, in the fraud detection, they will basically embed those models into some kind of intelligent app. That actually uses the model to make the decisions on the data. That's right. So you're basically, and then finally, like if you don't want your models to basically, you need to monitor your models because you need to make sure that there are right predictions that are being made. And so this allows kind of the data engineers, maybe if the data is dirty, they need to clean up the data so that the model works correctly, or we learn new things, or maybe the new algorithm is developed. That's what we want to actually change that, and then we go through this whole cycle. This whole cycle. So when you think about this whole cycle, basically the data scientists are used to different kinds of tool sets, like Jupyter Notebook, TensorFlow, all those things are available basically into this data science offering, where they can not only use the tools in runtime, but also from a data point of view, they can basically access, there are tools to access data legs, new SQL, SQL kind of services. So it's very similar to the Red Hat product offerings around runtimes where developers have access to Java runtimes and other runtimes that Red Hat provides. We're doing the same thing, but on the data science workload perspective as well. That's right. Yes. So what are some of the other benefits that you can see not only from a developer perspective? So I like this idea of helping the data engineers and the data scientists and the developers stop kind of what I call tool chain thrashing around how do we provide access to these tools in a safe, secure fashion. But from an IT ops perspective, what are some of the benefits from an IT ops perspective around OpenShift data science? So there are three benefits, right? Typically like Red Hat is well known for their real Linux system, and many companies don't think off of it as a AI ML kind of a company. So when we basically, our CTO for the last five years have basically nurtured a project called Open Data Hub. And within this Open Data Hub, there are 20 to 30 different open source tools and services that we now have basically brought that in form of Red Hat data science. So from an IT ops perspective, when you are running Red Hat OpenShift data science on top of managed OpenShift, there basically you provide the infrastructure that is required to run these workloads so that your developer basically focuses on the other data scientist focuses on these. So the developer doesn't need to worry about it getting run in an OpenShift. They just go to Open Data Hub, they select Jupyter Notebook and they can have that deployed easily. Yeah, so the Open Data Hub is a project which we have nurtured and now we are bringing that to market in terms of Red Hat data science. So from that perspective, basically they just need to install an operator and all the tools and runtimes that they require to build their models are there and they can start running it quick. For a lot of the IC ops people that I talked to hardware is a concern because GPU instances for instance in your cloud providers are very expensive. How can OpenShift and OpenShift data science help from that IT ops perspective? So from an IT ops perspective, basically they can create control workloads, control environments or templates where they can basically, when a data scientist spins up a project, they get all the resources that are required and it's a cell service model so they don't have to worry about it. So if they are using managed OpenShift from an infrastructure standpoint, we take care of that from a developer standpoint. Basically, if they require some GPU nodes, those are already provisioned in form of templates. So in a way it is not only secure, but also control so that the developers just can focus on creating their code. On creating the applications and I think also refining their algorithm so that they can actually make more intelligent business decisions. That's right. Overall. Well, thank you so much for coming and talking to us about OpenShift data science. If you want to learn more about OpenShift data science, of course, feel free to visit our website at redhat.com. Thank you. Thank you, Naran. Okay.