>> Live from San Francisco, it's The Cube, covering Google Cloud Next 2018. Brought to you by, Google Cloud and its ecosystem partners. >> Hello everyone, welcome back this is The Cube, live in San Francisco for Google Cloud's big event. It's called Google Next for 2018, it's their big cloud show. They're showcasing all their hot technology. A lot of breaking news, a lot of new tech, a lot of new announcements, of course we're bringing it here for three days of wall-to-wall coverage live. It's day two, our next guest is Tim Kelton, co-founder of Descartes Labs, doing some amazing work with imagery and data science, AI, TensorFlow, using the Google Cloud platform to analyze nearly 15 petabytes of data. Tim, welcome to The Cube. >> Thanks, great to be here >> Thanks for coming on. So we were just geeking out before we came on camera of the app that you have, really interesting stuff you guys got going on. Again, really cool, before we get into some of the tech, talk to me about Descartes Labs, you're co-founder, where did it come from? How did it start? And what are some of the projects that you guys are working on? >> I think, therefore I am. >> Exactly, exactly. Yeah, so we're a little different story than maybe a normal start-up. I was actually at a national research laboratory, Los Alamos National Laboratory, and there was a team of us that were focused on machine learning and using datasets, like remotely sensing the Earth with satellite and aerial imagery. And we were working on that from around 2008 to 2014 and then we saw just this explosion in things like, use cases for machine learning and applying that to real world use cases. But then, at the same time, there was this explosion in cloud computing and how much data you could store and train and things like that. So we started the company in late 2014 and now here we are today, we have around 80 employees. >> And what's the main thing you guys do from a data standpoint, where does the data come from? Take a minute to explain that. >> Yeah, so we focus on kind of a lot of often geospatial-centric data, but a lot of satellite and aerial imagery. A lot of what we call remote sensing, sensors orbiting the Earth or at low aerial over the Earth. All different modalities, such as different bands of light, different radio frequencies, all of those types of things. And then we fuse them together and have them in our models. And what we've seen is there's not just the magic data set that gives you the pure answer, right? It's fusing of a lot of these data sets together to tell you what's happening and then building models to predict how those changes affect our customers, their businesses, their supply chain, all those types of things. >> Let's talk about, I want to riff on something real quick, I know I want to get to some of the tech in a second. But my kids and I talk about this all the time, I got four kids and they're now, two in high school, two in college and they see Uber. And they see Uber remapping New York City every five minutes with the data that they get from the GPS. And we started riffing on drones and self-driving cars or aerial cars, if we want to fly in the air with automated helicopters or devices, you got to have some sort of coordinate system. We need this geospatial, and so, I know it's fantasy now, but what you guys are kind of getting at could be an indicator of the kind of geospatial work that's coming down later. Right now there's some cool things happening but you'd need kind of a name space or coordinates so you don't bump into something or are these automated drones don't fly near airports, or cell towers, or windmills, wind farms. >> Yeah, and those are the types of problems we solve or we look to solve, change is happening over time. Often it's the temporal cadence that's almost the key indicator in seeing how things are actually changing over time. And people are coming to us and saying, "Can you quantify that?" We've done things like agriculture and looking at crops grown, look at every single farm all over the whole U.S. and then build that into our models and say how much corn is grown at this field? And then test it back over the last 15 years and then say, as we get new imagery coming in, just daily flooding in through our Cloud Native platform, then just rerunning those models and saying, are we producing more today or less today? >> And then how is that data used, for example, take the agriculture example and that's used to say, okay, this region is maybe more productive than this region? Is it because of weather? Is it because of other things that they're doing? >> You can go back through all different types of use cases, everything from maybe if you're insuring that crop, you would might want to know if that's flooded more on the left side of the road or the right side of the road, as a predictive indicator. You might say, this is looking like a drought year. How have we done in drought years of 2007 and-- >> You look at irrigation trends. >> And you were talking off-camera about the ground truth, can you use IOT to actually calibrate the ground truth? >> Yeah and that's the sensor infusion we're seeing, everywhere around us we're seeing just floods and floods of sensors, so we have the sensors above the Earth looking down, but then as you have more and more sensors on the ground, that's the set of ground truth that you can train and calibrate. You could go back and train and train over again. It's a lot harder problem than, is this a cat or a dog? >> Yeah that's why I was riffing on the concept of a name space, the developer concept around, this is actually space. If you want to have flying drones deliver packages to transportation, you're going to need to know, some sort of triangulation, know what to do. But I got to ask you a question, so what are some of the problems that you're asked to look at, now that you have, you have the top-down view geospace, you got some ground truth sensor exploding in with more and more devices at the network, as a instrument anywhere it can have the IP or whatnot. What are some of the problems that you guys get asked to look at, you mentioned the agriculture, what else are you guys solving? >> Any sort of land use or land classification, or facilities and facility monitoring. It could be any sort of physical infrastructure that you're wanting to quantify and predict how those changes over time might impact that business vertical. And they're really varied, they're everything from energy and agriculture, and real estate, and things like that. Just last Friday, I was talking with, we have a two parts to our company. We have from the tech side, we have the engineering side which is normal engineering, but then we also have this applied science, where we have a team of scientists that are trying to build models often for our customers. 'Cause they're not, this is geospatial and machine learning, that's a rare breed of person. >> You don't want to cross pollinate. >> Yeah, and that's just not everywhere. Not all of our customers have that type of individual. But they were telling me, they were looking at the hurricane season coming up this Fall, and they had a building detector and they can detect all the buildings. So in just a couple hours, they ran that over all of the state of Florida and identified every building in the whole state of Florida. So now, as the seasons come in, they have a way to track that. >> They can be proactive and notify someone, hey you're building might need some boards on it or some sort of risk. >> Yeah and the last couple years look at all the weather events. In California we've had droughts and fires, but then you have flooding and things like that. And you're even able to start taking new types of sensors that are coming out, like the European Space Agency has a sensor that we ingest and it does synthetic aperture radar, where it's sending a radar signal down to the Earth and capturing it. So you can do things like water levels in reservoirs and things like that. >> And look at irrigation for farming, where is the droughts going to be? Where is the flooding going to be? So, for the folks watching, go to descarteslabs.com/search they got a search engine there, I wish we could show it on screen here but we don't have the terminal for it on this show. But it's a cool demo, you can search and find, you can pick an area, football field, and irrigation ditch, anything, cell tower, wind farm, and find duplicates and it gives you a map around the country. So the question is, is that, what is going on in the tech? 'Cause you got to use Cloud for this, so how do you make it all happen? >> Yeah, so we have two real big components to our tech space the first is, obviously we have lots and lots of satellite and aerial imagery, that's one of the biggest and messiest data sets and there's all types of calibration workloads that we have to do. So we have this ingest pipeline that processes it, cleans it, calibrates it, removes the clouds, not as in cloud computing infrastructure, but as in clouds over the head and then the shadows they emit down on the Earth. And we have this big ingestion process that cleans it all. And then finally compresses it and then we use things like GCS as an infinitely scalable object store. And what we really like on the GCS side is the performance we get 'cause we're reading and pulling in and out that compressed imagery all day long. So every time you zoom in or zoom out, like we're expanding it and removing that, but then our models, sometimes what we've done is, we'll want to maybe we're making a model in vegetation and we just want to look at the infrared bands. So we'll want to fuse together satellites from many different sources, fuse together ground sources, sensor sources, and just maybe pull in just one of those bands of light, not pull the whole files in. So that's what we've been building on our API. >> So how do you find GCP? What do you like? We've been all the users this week, what are the strengths? What are some of the weaknesses? What's on their to-do list? Documentation comes up a lot, we'd like to see better documentation, okay that's normal but what's your perspective? >> If you write code or develop, you always want something, you know it's always out of feature parody and stuff. From our perspective, the biggest strengths of GCP, one of the most core strengths is the network. The performance we've been able to see from the network is basically on par with what used to have, when we were at national laboratories we'd have access to high performance, super computing, some of the biggest clusters in the world. And in the network, in GCS and how we've been able scale linearly, like our ingest pipelines, we processed a petabyte of data on GCP in 16 hours through our processing pipeline on 30,000 cores. And we'll just scale that network bandwidth right up. >> Do you tap the premium network service or is it just the standard network? >> This is just stock. That was actually three years ago that we got to our bandwidth. >> How many cores? >> That was 30,000. >> Cause Google talked this morning about their standard network and the premium network, I don't know if you saw the keynote, with you get the low latency, if you pay a little bit more, proximate to your users, but you're saying on the standard network, you're getting just incredible... >> That was early 2015, it's just a few people in our company scaling up our ingest pipeline. We look at that, from then that was 40 years of imagery from NASA's Landsat program that we pulled in. And not that far off in the future, that petabyte's going to be a daily occurrence. So we wanted our ingest to scale and one of our big questions early on is actually, could the cloud actually even handle that type of scale? So that was one of the earliest workloads on things like-- >> And you feel good now about right? >> Oh yeah, and that was one of the first workloads on preemptible instances as well. >> What's on the to-do list? What would make your life better? >> So we've been working a lot with Istio that was shown here. So we actually gave a demo, we were in a couple talks yesterday on how we leverage and use Istio on our microservices. Our APIs are all built on that and so is our multi tenant SAS platform. So our ML team, when they're building models, they're all building models off different use cases, different bands of light, different geographic regions, different temporal windows. So we do all of that in Kubernetes and so those are all-- >> And what does Istio give you guys? What's the benefit of Istio? >> For us, we're using it on a few of our APIs and it's things like, really being able to see when you've start splitting out these microservices that network and that node-to-node or container-to-container latency and where things break down. Being about to do circuit retries or being able to try a response three different times before I return back a 500 or rate limit some of your APIs so they don't get crushed or you can scale them appropriately. And then actually being able to make custom metrics and to be able to fuse that back into how GKE scales on the node pools and stuff like that. >> So okay, that's how you're using it. So you were talking about Istio before, there's things that you'd like to see that aren't there today? More maturity or? >> Yeah I think Istio's like a very early starting point on all of this types of tools. >> So you want more? >> Oh yeah, definitely, definitely but I love the direction they're going and I love that it's open and if I ever wanted to I could build it on prem. But we were built basically native in the cloud so all of our infrastructure's in the cloud. We don't even have a physical server. >> What does open do for you, for your business? Is it just a good feeling? Do you feel like you're less locked in? Does it feel like you're giving back to the community? >> We read the Kubernetes source code. We've committed changes. Just recently, there's Google's open source, the OpenCensus library for tracing and things like that. We committed PRs back into that last week. We're looking for change. Something that doesn't quite work how we want, we can actually go.. >> Cause you're upstream >> Add value... >> For your business. >> We get in really hard problems, you kind of need to understand that code sometimes at that level. Build Tools, where Google took their internal tool, Blaze and opened source that bezel and so we're been using that. We're using that on our monorepos to do all of our builds. >> So you guys take it downstream, you work on it, and then all upstream contributions, is that how it works? >> Sometimes. >> Whenever you need to. >> Even Kubernetes, we've looked, if nothing else we've looked at the code multiple times and say, "Oh, this is why that autoscaler is behaving this way." Actually now I can understand how to change my workload a little bit and alter that so that the scaler works a little bit more performantly or we extract that last 10% of performance out to try and save that last 10%. >> This is a fascinating, I would love to come visit you guys and check out the facilities. It's the coolest thing ever. I think it's the future, there's so much tech going on. So many problems that are new and cool. You got the compute to boot behind it. Final question for you, how are you using analytics and machine learning? What's the key things you're using from Google? What are you guys building on your own? If anything, can you share a quick note on the ML and the analytics, how you guys are scaling that up? >> We've been using TensorFlow since very early days that geovisual search that you were saying, where we user TensorFlow models in some of those types of products. So we're big fans of that as well. And we'll keep building out models where it's appropriate. Sometimes we use very simple packages. You're just doing linear regression or things like that. >> So you're just applying that in. >> Yeah, it's the right tool for the right problem and always picking that and applying that. >> And just quick are you guys are for-profit, non-profit? What's the commercial? >> Yeah, we're for-profit, we're a Silicon Valley VC-backed company, even though we're in the mountains. >> Who's in the VCs? Which VCs are in? >> CrosslinK Capital is one our leading VCs, Eric Chin and that team down there and they've been great to work with. So they took a chance in a crazy bunch of scientists from up in the mountains of New Mexico. >> That sounds like a good VC back opportunity. >> Yeah and we had a CEO that was kind of from the Bay Area, Mark Johnson, and so we needed kind of both of those to really be successful. >> I mean I'm a big believer you throw money at great smart people and then merging markets like this. And you got a mission that's super cool, it's obvious that it's a lot to do and there's opportunities as well. >> Tremendous opportunities. Congratulations, Tim. Thanks for coming on The Cube. Tim Kelton, he's the co-founder at Descartes Labs. Here in The Cube, breaking down, bringing the technology, they got applied physicists, all these brains working on the geospatial future for The Cube. We are geospatial here in The Cube, in Google Next in San Francisco, I'm John Furrier, Dave Vellante, stay with us, for more coverage after this short break.