 Live from San Francisco, it's theCUBE. Covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. Hello and welcome back to theCUBE coverage here live in San Francisco for Google Cloud's conference next 2018 hashtag, Google Next 18. I'm John Furrier with Jeff Frick, my co-host 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 Iain Vala, chief data science foundation for precision medicine. Welcome to theCUBE, 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, BigQuery machine learning, Istio, One Dot, I'm all those 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 healthcare, she mentioned how you guys are organizing around these verticals. Healthcare is one of the big areas. Precision medicine, AI usage, tell us about your story. Yeah, so we are a very small nonprofit and we are at the intersection of data science and medical science and we work on project that have non-profit impact and social impact. And we work on driving and developing projects that have social impact and impersonalized medicine. So Diane, I think it's amazing, I would say with medicine, right? You look back five years wherever you are and you look back five years and you 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 died. 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. Talk about what is precision medicine? Just in 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. Yeah, so precision medicine has the meaning of personalized medicine, meaning that we hone in into smaller population of people to try and 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. Here, talk about the news. Okay, go ahead. Well, 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. Tell me about the relationship of the news you guys have here. Some interesting stuff, nonprofits, they need compute power, they need just like an enterprise. 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 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 nonprofit programs. So we actually have other programs like Google Earth for nonprofits, G Suite for nonprofits. This one is very much focused on harnessing and helping nonprofits extract insights from data. And is it a funding program? Is it a 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. So we're also providing free customer support as well as online learning. So what about the impact of the cloud technology for the nonprofit? Because I'm seeing so much activity certainly in Washington DC and around the world where since the JOBS Act, you can fundings have changed. You've got great things happening. We can have funding on mission-based funding. And also the legacy of brands are changing and open source changes. So faster time to value, right? And without all the expertise is an issue. How is cloud helping you be better at what you do? Can you give some examples? Yeah, so we had two different problems early on as a small nonprofit. First of all, we needed to scale up computationally. We had in-house servers. We needed a HIPAA compliant 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 precision medicine, that's very important because you want to get an entire population that makes your models much more accurate. The second thing was we wanted to collaborate with people with clinical research background. 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 the cloud really helped us to collaborate much more smoothly. And we only need their Gmail access to Gmail to give them access and things. And we can 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, auto MLs, hot at the show, obviously, hot trend. You're starting to see AI ops and disrupt more of the enterprise side. But as a data scientist, as you look at some of these machine learnings, I mean, you must get pretty excited. What are you thinking? What's your vision on how you're going to use, like BigQuery's got ML built in now. This is not new, as Google's been using it for a while. Are you tapping some of that? And what's your team doing with ML? Absolutely. We use BigQuery ML. We were able to use it a few months in advance. It's great because our data scientists like to work in BigQuery. 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 a different part of the platform for that. And it gives you sort of a proof of concept right away. For doing deep learning on those things, we use Cloud ML still. But for early on, you want to see if there is potential in the data. And you're able to do that very quickly with BigQuery ML right there. We also use Auto ML Vision. We had access to about 1,000 patients for MRI images. And we wanted to see if we can detect Alzheimer's based on those. And we use Auto ML for that. Actually, works well. So about the relationship with doctors, not always seen as the most tech savvy. Some 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 do you extend out the analysis and the interactions? How do you integrate into the Edge workflow? So one thing I really appreciated for Google Cloud was that it seems to me it's built from the ground up for everyone to use. And the ease of access was very important to us. Like I said, we have data scientists and statisticians and computer scientists on board. But we needed a method and a platform that everybody can use. And through this program, actually, you guys provide what's called Quick Lab, which is a screenshot of how to spin up a virtual machine and things like that. That a couple of years ago, you have to run a few command lines, 20 command lines to get that. Now it's just a push of a button. So that just makes it much easier to work with people with the 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. Well, congratulations. Carol, a question to you is that, how does someone get involved in the data solutions for change? Application, online, referral, I mean, how do you use work? All of the above. We do have an online application and we welcome all nonprofits 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 more mission. What's the mission criteria? Is there a certain bar to reach, so to speak? We're most focused on, there really is no size in terms of size of the nonprofit or breadth. It's much more around, do you have a problem that data and analytics can actually address? 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. So there are 17 sustainable development goals. And so that's aimed at college students and storytelling to actually address one of these 17 areas. We'd let the follow up after the show, talk about some of the projects. There's a lot of things going on. You know, technology for good really is important right now that people see that and people want to work for mission driven organizations. Absolutely. This becomes a clear criteria. Thanks for coming on, appreciate it. Thanks for coming on today. It's theCUBE coverage here at Google Cloud Next 18. I'm Jeff Frick, stay with us. We'll have more coverage after this short break.