>> Live from San Francisco, it's the Cube, covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. >> Hello and welcome back to The Cube coverage here live in San Francisco for Google Cloud's conference Next 2018, #GoogleNext18. I'm John Furrier with Jeff Frick, my cohost all week. Third day of three days of wall to wall live coverage. Our next guest, Carol Carpenter, Vice President of Product Marketing for Google Cloud. And Ayin Vala, Chief Data Science Foundation for Precision Medicine. Welcome to The Cube, thanks for joining us. >> Thank you for having us. >> So congratulations, VP of Product Marketing. Great job getting all these announcements out, all these different products. Open source, big query machine learning, Istio, One dot, I mean, all this, tons of products, congratulations. >> Thank you, thank you. It was a tremendous amount of work. Great team. >> So you guys are starting to show real progress in customer traction, customer scale. Google's always had great technology. Consumption side of it, you guys have made progress. Diane Green mentioned on stage, on day one, she mentioned health care. She mentioned how you guys are organizing around these verticals. Health care is one of the big areas. Precision Medicine, AI usage, tell us about your story. >> Yes, so we are a very small non-profit. And we are at the intersection of data science and medical science and we work on projects that have non-profits impact and social impact. And we work on driving and developing projects that have social impact and in personalized medicine. >> So I think it's amazing. I always think with medicine, right, you look back five years wherever you are and you look back five years and think, oh my god, that was completely barbaric, right. They used to bleed people out and here, today, we still help cancer patients by basically poisoning them until they almost die and hopefully it kills the cancer first. You guys are looking at medicine in a very different way and the future medicine is so different than what it is today. And talk about, what is Presicion Medicine? Just the descriptor, it's a very different approach to kind of some of the treatments that we still use today in 2018. It's crazy. >> Yes, so Presicion Medicine has the meaning of personalized medicine. Meaning that we hone it into smaller population of people to trying to see what is the driving factors, individually customized to those populations and find out the different variables that are important for that population of people for detection of the disease, you know, cancer, Alzheimer's, those things. >> Okay, talk about the news. Okay, go ahead. >> Oh, oh, I was just going to say. And to be able to do what he's doing requires a lot of computational power to be able to actually get that precise. >> Right. Talk about the relationship and the news you guys have here. Some interesting stuff. Non-profits, they need compute power, they need, just like an eneterprise. You guys are bringing some change. What's the relationship between you guys? How are you working together? >> So one of our key messages here at this event is really around making computing available for everyone. Making data and analytics and machine learning available for everyone. This whole idea of human-centered AI. And what we've realized is, you know, data is the new natural resource. >> Yeah. >> In the world these days. And companies that know how to take advantage and actually mine insights from the data to solve problems like what they're solving at Precision Medicine. That is really where the new breakthroughs are going to come. So we announced a program here at the event, It's called Data Solutions for Change. It's from Google Cloud and it's a program in addition to our other non-profit programs. So we actually have other programs like Google Earth for non-profits. G Suite for non-profits. This one is very much focused on harnessing and helping non-profits extract insights from data. >> And is it a funding program, is it technology transfer Can you talk about, just a little detail on how it actually works. >> It's actually a combination of three things. One is funding, it's credits for up to $5,000 a month for up to six months. As well as customer support. One thing we've all talked about is the technology is amazing. You often also need to be able to apply some business logic around it and data scientists are somewhat of a challenge to hire these days. >> Yeah. >> So we're also proving free customer support, as well as online learning. >> Talk about an impact of the Cloud technology for the non-proit because6 I, you know, I'm seeing so much activity, certainly in Washington D.C. and around the world, where, you know, since the Jobs Act, fundings have changed. You got great things happening. You can have funding on mission-based funding. And also, the legacy of brand's are changing and open source changes So faster time to value. (laughs) >> Right. >> And without all the, you know, expertise it's an issue. How is Cloud helping you be better at what you do? Can you give some examples? >> Yes, so we had two different problems early on, as a small non-profit. First of all, we needed to scale up computationally. We had in-house servers. We needed a HIPAA complaint way to put our data up. So that's one of the reasons we were able to even use Google Cloud in the beginning. And now, we are able to run our models or entire data sets. Before that, we were only using a small population. And in Presicion Medicine, that's very important 'cause you want to get% entire population. That makes your models much more accurate. The second things was, we wanted to collaborate with people with clinical research backgrounds. And we need to provide a platform for them to be able to use, have the data on there, visualize, do computations, anything they want to do. And being on a Cloud really helped us to collaborate much more smoothly and you know, we only need their Gmail access, you know to Gmail to give them access and things. >> Yeah. >> And we could do it very, very quickly. Whereas before, it would take us months to transfer data. >> Yeah, it's a huge savings. Talk about the machine learning, AutoML's hot at the show, obviously, hot trend. You start to see AI ops coming in and disrupt more of the enterprise side but as data scientists, as you look at some of these machine learnings, I mean, you must get pretty excited. What are you thinking? What's your vision and how you going to use, like BigQuery's got ML built in now. This is like not new, it's Google's been using it for awhile. Are you tapping some of that? And what's your team doing with ML? >> Absolutely. We use BigQuery ML. We were able to use a few months in advance. It's great 'cause our data scientists like to work in BigQuery. They used to see, you know, you query the data right there. You can actually do the machine learning on there too. And you don't have to send it to different part of the platform for that. And it gives you sort of a proof of concept right away. For doing deep learning and those things, we use Cloud ML still, but for early on, you want to see if there is potential in a data. And you're able to do that very quickly with BigQuery ML right there. We also use AutoML Vision. We had access to about a thousand patients for MRI images and we wanted to see if we can detect Alzheimer's based on those. And we used AutoML for that. Actually works well. >> Some of the relationships with doctors, they're not always seen as the most tech savvy. So now they are getting more. As you do all this high-end, geeky stuff, you got to push it out to an interface. Google's really user-centric philosophy with user interfaces has always been kind of known for. Is that in Sheets, is that G Suite? How will you extend out the analysis and the interactions. How do you integrate into the edge work flow? You know? (laughs) >> So one thing I really appreciated for Google Cloud was that it was, seems to me it's built from the ground up for everyone to use. And it was the ease of access was very, was very important to us, like I said. We have data scientisits and statisticians and computer scientists onboard. But we needed a method and a platform that everybody can use. And through this program, they actually.. You guys provide what's called Qwiklab, which is, you know, screenshot of how to spin up a virtual machine and things like that. That, you know, a couple of years ago you have to run, you know, few command lines, too many command lines, to get that. Now it's just a push of a button. So that's just... Makes it much easier to work with people with background and domain knowledge and take away that 80% of the work, that's just a data engineering work that they don't want to do. >> That's awesome stuff. Well congratulations. Carol, a question to you is How does someone get involved in the Data Solutions for Change? An application? Online? Referral? I mean, how do these work? >> All of the above. (John laughs) We do have an online application and we welcome all non-profits to apply if they have a clear objective data problem that they want to solve. We would love to be able to help them. >> Does scope matter, big size, is it more mission? What's the mission criteria? Is there a certain bar to reach, so to speak, or-- >> Yeah, I mean we're most focused on... there really is not size, in terms of size of the non-profit or the breadth. It's much more around, do you have a problem that data and analytics can actually address. >> Yeah. >> So really working on problems that matter. And in addition, we actually announced this week that we are partnering with United Nations on a contest. It's called Sustainable.. It's for Visualize 2030 >> Yeah. >> So there are 17 sustainable development goals. >> Right, righr. >> And so, that's aimed at college students and storytelling to actually address one of these 17 areas. >> We'd love to follow up after the show, talk about some of the projects. since you have a lot of things going on. >> Yeah. >> Use of technology for good really is important right now, that people see that. People want to work for mission-driven organizations. >> Absolutely >> This becomes a clear citeria. Thanks for coming on. Appreciate it. Thanks for coming on today. Acute coverage here at Google Could Next 18 I'm John Furrier with Jeff Fricks. Stay with us. More coverage after this short break. (upbeat music)