 Hey, welcome back, everybody. Jeff Frick here with the Q. We're in Austin, Texas at the Dell EMC HPC and AI Innovation Lab. As you can see behind me, there's racks and racks and racks of gear, where they build all types of special configurations around specific applications, whether it's Oracle or SAP. And more recently, a lot more around artificial intelligence, whether it's machine learning, deep learning. So it's a really cool place to be. We're excited to be here. And our next guest is Bala Chandra Sikaran. He is in the technical staff. This is the engineer, Bala. Welcome. Thank you. So how do you like playing with all these toys all day long? Oh, I love it. I mean, you guys have literally everything in there. A lot more than just Dell EMC gear, but you've got switches and networking gear and everything. And not just the gear, it's also all the software components. It's the deep learning libraries, deep learning models, a whole bunch of things that we can get to play around with. Now that's interesting, because it's harder to see the software, right? Exactly. The software is pumping through all these machines. Right. But you guys do all types of really optimization and configuration, correct? Yes. We try to make it easy for the end customer. And the project that I'm working on, machine learning for Hadoop, we try to make things easy for the data scientists. Right. So we go to all the Hadoop shows, Hadoop World, Hadoop Summit, Strata, a big data NYC, Suga Valley. And the knock on Hadoop is always, it's too hard. There aren't enough engineers. I can't get enough people to do it myself. You know, it's a cool open source project, but it's just not that easy to do. You guys are really helping people solve that problem. And what you're saying is true for the infrastructure, guys. Now imagine a data scientist, right? So Hadoop cluster accessing it, securing it, is going to be really tough for them. And they shouldn't be worried about it, right? They should be focused on data science. So those are some of the things that we try to do for them. So what are some of the tips and tricks as you build these systems that throw people off all the time, you know, that are relatively simple things to fix? And then what are some of the hard stuff where you guys have really applied your expertise to get over those challenges? Let me give you a small example, right? So this is a new project, AI. We hired data scientists. So I walked in the data scientist through the lab. He looked at all the cluster and he pushed me aside and said, hey, you're not going to ask me to work on these things, right? I have no idea how to do these things. So that kind of gives you a sense of what the data scientist should focus on and what they shouldn't focus on. So some of the things that we do and some of the things that are probably difficult for them is all the libraries that are needed to run their project, the conflicts between libraries, the dependencies between them. So one of the things that we do is develop this pre-configured engine that you can readily download into a product and run so that data scientists don't have to worry about what library I should use. They have to worry about the models and accuracy and whatever data science needs to be done rather than focusing on the infrastructure. So you've not only packaged the hardware and the systems, but you've packaged the software distribution and all the kind of surrounding components of that as well. So when you had the data scientists here talking about the Hadoop cluster, if they didn't want to talk about the hardware and the software, what were you helping them with? How did you engage with the customers here at the lab? So the example that I gave is for the data scientists that we newly hired for our team. So we had to set up environments for them, right? So that was the example, but the same thing applies for a customer as well. So again, to help them in solving the problem, we try to package some of the things as part of our product and deliver it to them so it's easy for them to deploy and get started on things. Now the other piece that's included that again is not in this room is the services and the support. So you guys have a full team of professional services. Once you can figure and figure out what the right optimum solution is for them, then you've got a team that can actually go deploy it at their local site. So we have packaged things even for our services. So the services would go to the customer site, they would deploy the solution and download and deploy our packages and be able to demonstrate how easy it is to think of them as tutorials, if you will, right? So here are the tutorials. Here's how you run various models. So here's how easy it is for you to get started. So that's what they would train the customer on. So it's not just the deployment piece of it, just packaging things for them so they can show customers how to get started quickly, how everything works, and kind of give a green check mark, if you will. So what are some of your favorite applications that people are using these things for? Do you get involved in the application stack on the customer side? What are some of the fun use cases that people are using your technology to solve? So for the application, my project is about machine learning on Hadoop. We are packaging clouded as CDSW, that's clouded our data science workbench as part of the product. So that allows data science access to the Hadoop cluster and abstracting the complexities of the cluster. So they can access the cluster, they can access the data, they can have security without worrying about all the intricacies of the cluster. In addition to that, they can create different projects, have different libraries in different projects so they don't have to conflict with each other. And also they can add users to it, they can work collaboratively. So basically tools that help data scientists, software developers do their job and not worry about the infrastructure. They should not be. All right, great. Well, Ball, it's a pretty exciting place to work. I'm sure you're having a ball. Yes, I am, thank you. All right, well, thanks for taking a few minutes with us and really enjoy the conversation. Thank you. All right, he's Ball, I'm Jeff. You're watching theCUBE from Austin, Texas at the Dell EMC High Performance Computing and Artificial Intelligence Labs. Thanks for watching.