 From San Francisco, it's theCUBE. Covering OSI Soft, Pie World 2018. Brought to you by OSI Soft. Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the OC Soft shows called PI World. It's been going on for over 15 years. We've never been here before. We're excited to be here. Really, he's coming at it from the operations point of view. And they've been worrying about operations and operations efficiency for years. There's people walking around with 15 year pins, which is pretty amazing. I got my first one year pin, so that's good. So we're excited to be here and dive into the details because we've talked a lot about IoT and industrial IoT and kind of coming at it from the IT side. But these guys have been working at it from the OT side for years and years and years, almost 40 years. So our first guest has joined us. He's Remy Deket, the global head applied AI and data center clarity lifecycle. It's a mouthful for my heat transfer technologies. Remy, nice to meet you. Very nice meeting you. Thank you for having me. Give us a little bit more detail on what my heat transfer is all about and then we'll dive into some of the specifics that you're working on. So my heat transfer started about 28 years ago in the simulation of heat and getting rid of all that heat that's being emitted by a lot of data centers, all the servers and the density that's occurring these days. And we've evolved into just developing a software solution, leveraging the pie infrastructure for real time monitoring and extended it beyond for forecasting and doing all sorts of advanced analytics from that data. Right, so heat is the historical enemy of electronics and has been forever. Yes, continuing to be so for sure. And continuing to be so. And the data centers, it's an interesting evolution in the data center space because on one hand, they're consolidating data centers or shutting down data centers. You've got this public cloud phenomenon. On the other hand, it's density, density, density, density, density, which probably is good opportunity for you guys. A great opportunity. Unfortunately, the problems kind of are accentuated by exactly those phenomenon of consolidation and the cloud and the virtualization projects that are going on. So all of that combined makes for a really big cocktail of heat and that heat needs to be dissipated somehow. And of course, the energy efficiency of all the machines are getting better and better. But at some point, it needs to be optimized and that's where the software components to remove the human in the loop, really to optimize that heat distribution and removal. So what are the big themes here at this show is finding inefficiency, this kind of continual quest for better efficiency and using data and big data specifically and sensor data to be able to find the inefficiency and act on the inefficiency. So what are some of the things that you guys look at? You've been at it for a long time, but there's still more opportunities to find inefficiencies. Were you still finding inefficiencies? Oh well, I mean, the main aspect is we have a lot of building automation systems and cooling loop system that have been programmed to try and get to the best situation in any circumstances. And really, when you look at what we're doing now is applying artificial intelligence to augment the abilities of those systems to better control and get to even a better place from an energy efficiency perspective. So that's really the latest kind of evolution to use that big data to learn from that data and then further optimize your cooling environment and your heat distribution. Right, and I'm curious what kind of new learnings came out of kind of the hyperscale players. Obviously, big public cloud players, Amazon and Azure, Google Cloud have giant data centers, not only for their own core businesses, but now they're building them out as public clouds at much bigger scale than the traditional corporate data centers. They're just operating at a whole different level. Oh, a whole new, yeah. Yeah, so what are some of the things that have come out of those experiences that are different than the world kind of pre-public cloud? Well, if you look at the pre-public, private cloud and public cloud, you had maybe on average, five to six kilowatt per rack in a data center was the average power consumed by those racks. Now we're looking, some of our clients have up to 50 kilowatt per rack and now you need water-cooled elements into that rack or other cooling elements that are helping the situation because those kinds of densities are producing a huge amount of heat and that's really a big concern and a big shift from the enterprise-level data center was a little bit less of a consumer of that power. Right, now, do you guys do anything outside of the data center? I know that's your area specialties but we've been doing a lot of autonomous vehicle shows and one of the things that comes up over and over and over is kind of the harsh environment for compute in a car or a truck or a bus or whatever. It's not a beautifully controlled with a lot of great backup power and diesel and air conditioning, very rough environment. So what are some of the applications that you guys can use to help get that compute power in these vehicles? Well, actually the evolution for us more on the software side was to apply our deep learning, artificial intelligence components and agents to other industries. So we're leveraging the forecasting capabilities of these deep learning agents to apply to other areas. So discrete manufacturing was one example, fleet optimization, so to go back to those edge devices. So we do a lot of fleet optimization, fuel optimization on these components and that's completely outside the data center but it's based on the same type of deep learning technologies that we've developed for the data center. And all the forecasts are as more and more the compute in the store moves out to the edge and you've got all the industrial devices running around in the center, it's not new news for the group at this organization. No, clearly. But you're kind of shifting that load of the heat management from the data center out to the edge. To the edge, correct. So it does relieve a little bit of the, let's call it the pressure inside the data center. But at the end of the day, the density of those cloud providers is just being accentuated by the sheer number of devices. So we thought there might be a shift towards the edge from a power, let's say, removal from the core data center and in the end it's actually the opposite. That's happening, the power is really getting denser and denser inside the data center itself. So last question before I let you go. What's your take on the vibe of the show? What's happening here at PyWorld? It's amazing, kind of the international flavors. I'm walking around the halls, I'm seeing badges and hearing all kinds of languages. I mean, this is pretty hardcore industrial internet happening right here. I mean, the operational technologies and the various applications and industries in which Py is used and leveraged worldwide is phenomenal. And it's a very vibrant show and it's actually quite good when it comes down to it. A lot of people, the exchange between the end users together from different industries share their tips and tricks on how they've deployed. Their various stories are just amazing. So a great, great, great PyWorld conference for sure. All right, good. Well, thank you for taking a few minutes and sitting down and sharing the Maya story with us. Thank you for having me. Absolutely. All right, he's Rami, I'm Jeff. We are at OCSoft PyWorld 2018 in downtown San Francisco. We'll be right back. Thanks for watching.