 Welcome back everyone, we are here live in Chicago for KubeCon, CNCF, I'm John Furrier, your host, prop stretcher, Savannah Peterson, bringing out all the action in the cloud-native world. We've got a great segment here with IBM and a customer practitioner, because this show's got all the companies here, startups, practitioners, customers, users. It's a great show, Cloud-native is the future. We've got Shaylesh Chinoy, Assistant Dean of Information Technology at Albert Einstein College of Medicine. Welcome to the show. Thank you, Scott. Also the CIO, basically. Yes. Academic title. Great break, beat break, global product executive at IBM. Great to have you back. Quite a run on the queue. It's like your fifth appearance in the past couple months. Yeah. Things are hot, welcome back. Yeah, thanks. It's great to be here. So gosh, we've got the customer situation here. What's interesting is that you got young guns coming in and the marketplace, no new talent, entrepreneurs, developers, big projects, companies and user customers all together sharing. I mean, it's the perfect storm around this cloud-native next gen. It's legit with AI too. It's all going down. This is a hot part of what your story is. Yeah, yeah. And it's really interesting to see the maturity level. As we were in Amsterdam not that long ago and seeing the things that were at top of mind for everyone then and now today and just like, it feels like we're on warp speed, kind of, with the things that are happening. The whole concept of the platform and the platform engineering teams and delivering these services, the whole concept what Kubernetes started with originally was being able to deliver the infrastructure services that people need to be productive. You know, one of the things that's been on theCUBE the past couple months has been with AI. More data's coming in. Again, we've seen this movie before. It's been happening since the big data all 13 years ago. But the budgets aren't going up. More data's coming in, more skills are needed. The budgets aren't rising just as fast. The cybersecurity threats are there as well. So you got to use the code and the new AI tools are an opportunity on how things are stored. What do you do with the data? What are you experiencing with this wave? What's the mindset over there? Well, certainly, I mean, that's a big part that's driving this, right? We have a major inflection point that's going on right now and how it is that we can leverage this technology. But I just want to point out in academic research we have a special challenge which is the ability to manage and share the data that are generated. The intellectual property is generated. We're an institution that's largely funded by the National Institutes of Health and there's an expectation of it. And in fact, we have to comply with rules that say make your data shareable. And that includes the computational code that we have. So portability is important and the promise of containers has existed for a while. But it's now that the platform exists in a way that we can leverage it easily. And I feel like it's a responsibility that we have as technology leaders to make this available and to really, it's an institutional change. So what I envision is that in the research community, particularly academic research communities that every researcher will have a container the moment they walk into the institution and then they'll use that as a way to share what they're doing with the community. Yeah, and I think because we've been talking about this, John and I have been talking about it. We were talking with you about it several times this year that it's that platform engineering aspect of it and how do you simplify your entire stack so that you can do more with the same people. It's pretty much it. And also ultimately for our clients who are the researchers, I can't let the IT department get in the way. Right, and the fact is they need responsiveness. They need to be able to do this. I mean, so it's like working in an artist community, right? I mean, the idea is that inspiration can happen at any time. And so the idea that we'd have to engineer a solution and then give it to them, those days are long gone. The moment that someone gets an NIH grant they're applying for a new one. And you know, the other thing that's been observing too is the speed. The game is so faster now. You got to enable with the platform engineering for devs and users. By the way, the developers are becoming the users too. That's right, right. You have a whole democratization wave happening on discovery and the innovations, not just a department. It could be anyone at use at any time. Talk about the speed and how that challenges you because you got to run faster at the pace of play now is so fast. How does that alter your world? Well for us, what it comes down to is, once again for the platform, so the solution that we chose is kind of turnkey essentially. So we're running OpenShift and IBM Fusion, HCI. So that hyperconversion environment allows us to basically bring in the technology quickly, deploy it quickly, and then be able to maintain it very easily. Otherwise we're stuck, right? We can't work at the pace of innovation if we don't do it this way. And I think that's really something that's changed in the world of OpenShift and containers. I mean, I find it the most exciting time I've seen in a long, long time, maybe in my career, where you've got the perfect storm of innovation and leverage of existing legacy or pre-existing condition resources. Like data, like the data value that people are getting out of the exhaust or pre-data that they can now enable with better software, it changes the game. So if you have good storage practices or good hygiene or any kind of hygiene, AI is going to come in and take unstructured data, structured data, and actually turn it into gold. I mean, that's one of the big things on the enterprise side is that this is saying, wow, I got value in my data, I didn't know I had. It's unlocking a lot more. Right. And you know, I was talking about it, data as a revenue-able asset. You know, how do you extract that value out of the data? And you're absolutely right. It does come back to the speed and the velocity with which you're able to understand and interpret that data, and there's just volumes and volumes that are coming at you. And you know, the good news is there's a lot of new technology that is helping us with this and being able to deal with these problems. Yeah, and I think one of the themes from this morning's keynote was, okay, you know, Kubernetes is here. And I think we've been talking about it, is it coming? How mature has it gotten? How easy has it gotten? I mean, I see it as more like, how does it become enterprise? How does it become, you know, the thing in enterprise? What are you seeing from customers like Albert Einstein and others? Yeah, and it's really interesting in the conversations that Shailesh and I have had around, you know, that innovative moment, you know, that his researchers are having and you don't want to take that away from them. You want to, you know, have the free flow of tools and the technology to be able to help them. And so we're seeing, and a lot of the things that we've talked about around some of the critical data services, you know, ensuring the resilience of the data, you know, what happens if somebody accidentally deletes, you know, volumes of data? How do they get that back very quickly? And so there's a wide range of solutions that his team is actually going to be using to deliver that resilience that they need, things like backup and recovery, which you might think is a very basic thing, but when you start thinking about it in a Kubernetes context, there's some things that you really need to think about. Same thing with doing multi-site disaster and it's what's really interesting to see they're doing right now is using multiple sites to deliver the resilience that they need for their researchers. It's really fundamental to the way that we deliver the service, right? First, high availability essential, of course, right? Absolutely have to have that always on environment. It's not just a need in enterprise, right? It's also in the academic environment. That's the demand that people have. And then, of course, this ability to back up and to provide us with some disaster recovery relief, not because there's going to be a hurricane or an earthquake, but because there's going to be a cyber attack and we have to be able to protect and preserve our intellectual property. I mean, you've got to grud me, it's a tough job. I mean, modernizing IT, that transformation journey has been going on for a while now with the cybersecurity threat, the threat vectors are increasing. Not just the data protection side, you've got two things going on there. You've got data protection challenges, and then you've got the threats, okay? Coming at you. And this is in an academic environment, right? Where the whole tradition of an academic environment is openness, right? So, the emphasis of openness is cybersecurity, right? So we have to somehow keep this environment secure, which takes extra measures, right? And it's regulated as well. It's not, I mean, again, you start to look at the data protection and all the laws and everything like that. It has to be just, you have to be able to have a rock solid platform to be able to go and do that. This is why there's an inflection point now in the technology, right? And so before these become, before fusion, frankly, I would not have considered taking this direction as attractive as it is conceptually. Yeah. It's not sustainable, unless you have this type of a platform and this type of ecosystem. You know, I think, and with not just data protection, we've seen this the cyber security conversations where the compliance and governance is so important. AI actually could be a great win here to do all the reports, the paperwork. Yeah. When is the moment of truth? If you do, I have a breach. When did it start? You got to find the data. So the data hygiene, we think, based on the CUBE research team's view, this is going to be an ongoing, elevated conversation, not just classic data management, data hygiene as an ongoing, always on concept. Correct. Both technology and management chops. I mean, do you see that same thing? Absolutely. I mean, just think about automated playbooks now. Right, all of that, I was just thinking, in the classic cyber security, that's a sore. But now expand that beyond just the way that you maintain your environment, the way that you meet the needs of your community, right? Yeah. That's what it's about. Pete, you have the product executive over there at IBM. One of the things that you must have on your radar, I'd love to get your reaction to if you can. It's not too confidential. Data engineering is coming up to be quite the new discipline, coming out of platform engineering. Pipelines are exploding in value, more are coming online, mentioned automated playbooks, run books, whatever you want to call it. You now have best practices that could be scaled, automatically with AI. So imagine having a playbook run, generate the playbook based on certain conditions. That's on your radar, is that on your radar? Yeah, yeah, and we have this concept that we talk about called data pipelines. And it's like a very modular way to be able to put containers together and to be able to put this open source code together to answer a lot of the questions that they're trying to ask with their data. And then taking the cybersecurity concept even a little bit further, you're absolutely right. Applying AI in that world is absolutely what you want to be doing. Because it's really about time to detection is really critical. How fast can I detect that that ransomware attack is happening? But it's also then time to recover. How fast can I recover? And how fast can I get back to what we at IBM, we call it the minimum viable company. What's the minimum you need to be up and running as quickly as possible? You know, one of the things that we've observed as even a small business compared to the mission you guys have, and obviously at big IBM is, we discovered that we have value in our data by storing it. And we kind of had an instinct. But then when AI came out, we actually realized it. So, you know, we're back to storage again. AI is making everything better. We're back to cybersecurity and IT, information technology, managing. We're going to bring back data processing word back to the old days. We're back into data again, at large scale, high velocity, intelligent, I mean, almost AI, the timing of generative AI was almost perfect. Yeah, absolutely, absolutely. I mean, don't you think this is what unlocks all the potential that exists there, right? I mean, we can really tap into this now. And I think that that's really pretty remarkable. And I would assume as a research institution, you're bringing in new techniques and new types of applications and you need something that's a more modern platform to really take that in. That innovation is really very critical. There's no doubt about it. But I mean, we still have some human element that's involved in this, right? And part of that is just the harmonization of metadata because still within the same discipline, people call the same thing different by different terms. So that ability to harmonize or have AI be able to understand that it's going to be critical for us to unlock that. As we always say in theCUBE, human plus AI is better than AI. So AI is great, but you add the humans to it. Just in scales, intellectual capital, scales ideas, scales data. Well, ultimately, there's no replacement for human curiosity, but you can't, that's not artificial. So that curiosity is what drives a research institution and that's not going to go away. And I love that conversation around how you're enabling creativity. That could come from anywhere. And certainly in medicine, we need a lot more breakthroughs, a lot of problems being solved now that you couldn't get to beat before. Yeah, yeah. It's really cool to see, it's a bit of the collision of organizations and culture and the way we've always done things and then you bring in technology and to get the real benefit of the technology, you kind of have to rethink the organization and the culture and marry the two together. So it's really important to remember that. Pete Shailesh, thanks for coming on theCUBE in the last minute we have left. Talk about the relationship, the partnership between you guys. What's it about? Give a quick plug for what you guys are doing together. Well, I say, you have great ideas. You can bring the resources to the table, but without the right partners and the right partnerships and really world-class service, you really can't do anything. And that's what it comes down to. That's what I think I'm going to appreciate is the relationship with IBM and Red Hat. It's been really fantastic for us. Yeah. Absolutely. Pete, thanks for coming on with guys. Appreciate your time. Thank you so much. Breaking down the barriers to innovation, creativity, innovation. All happened here in theCUBE in Chicago for KubeCon. We'll be back with more coverage. I'm John Furrier, Rob Stretcher and Savannah. Peterson after this short break. Stay with us.