 Ladies and gentlemen, please welcome Diane Greene. Thank you so much and welcome. This is just an incredibly exciting event for all of us in Google Cloud, for all of us at Google, I would say. The guy that was mic'ing me was telling me that there was a queue around the block, that we had a blockbuster. And I'm sorry for the queue, but we're pretty happy to have all of you here to be at capacity at 10,000, all the people live streaming. I really regret the live streamers couldn't see the surround on that amazing animation that we just showed. You know, Cloud, does anybody not agree it's the biggest thing going on in IT right now? Google Cloud, I mean, the adoption of Cloud in general, I believe it must be accelerating. I know at Google Cloud this year, we've seen unbelievable acceleration. What's it? OK. OK. And the quality of our customer conversations are really changing. For a while, it was more, let's use BigQuery. Let's do our data analytics. Let's do a machine learning project. But just two weeks ago, I met with five customers over the course of the week. And three of them said, hey, I wanted to a full lift and shift. I just want to move everything to the cloud. And it was only last September that we actually started calling ourselves Google Cloud, all that we do from the infrastructure to the workplace collaboration, productivity, and even the mobile devices in the enterprise. It's been a remarkable year. I mean, our engineers have done over 500 releases. These are releases to get functionality to our customers. These are new innovations. These are inventions and just ongoing improvement in all that we do. Security, reliability, efficiencies. Cloud is just an incredibly cool place to be working right now. It's basically revolutionizing. It's sort of where a lot of the digital revolution for every industry is going on. In financial services, tremendous efficiencies on how data is processed or how the banks can talk directly to their customers. In health, we're seeing a revolution in diagnosis, in predicting patient outcomes. In retail, a particularly exciting area, taking billions of dollars out as we revolutionize the store of the future. Media, something Google is particularly well situated to do, and energy, manufacturing, optimizing, reducing the energy usage, making the factories the manufacturing more efficient. It's incredibly exciting to be part of this. We have such interesting customers. We have a customer planet labs that is taking pictures of the entire Earth every three hours, and that's going on Google Cloud. I think the cloud is no longer a utility for surplus peak capacity. It's no longer a place just to store things. Startups, it's where they start, but it's not solely for a startup to figure out what demand they're going to have before they spend any money. Workplace productivities really move beyond sending files around via email. And the cloud is really what's giving, increasingly giving customers their competitive advantage. And what's important there? Well, paramount is security. From the board level on down, nobody, you know, everybody really cares about security. And I'll say a little more about security at scale in a little bit. It's about cost performance, affectivity, the flexible costs, no lock-in. And it's about reliability. And I'm just, because of all the interest in reliability, recently, I'm just going to say a few words. You know, you take Google search. It was designed. It runs, as you see when you do a search, it runs at five nines of availability. That's 99.999% of time. And our cloud, that's how we designed our cloud. And we're making it really easy for our customers to design and deploy for that kind of reliability. Distributed, no single point of failure. And I'm really proud. I just learned yesterday that we were recognized as having the highest availability of any cloud over the course of 2016. Thank you. I think 2017 will be promising, too. You know, we're putting a lot of effort across the board, across our entire systems with the unbelievably excellent engineers we have to keep giving you more nines. What else? Well, the world customers are getting increasingly focused on data. You want the world's best data analytics, the world's best machine learning. This is definitely a Google strength. And then finally, workplace culture. You know, really, it's about real time collaboration. It's about finding ways to get the bureaucracy out of a company because everybody's got to move fast now. And we have the technology tools to do that. So over the next three days, you're going to hear from our engineers. They're going to tell you about our products. They're going to give you our roadmaps. You're going to see lots of demos. Our customer-facing people will help you navigate Google Cloud. And you're also, in this keynote, going to hear from our customers. Everybody at Google Cloud is so incredibly proud of the customers that we have here today that we've been working with over the course of the year. We've had customers and a major partner. So now I just want to pause. I'm wearing my red ribbon. I want to acknowledge that it's International Women's Day today. I have to say that response is pretty exciting. You know, this industry, I think I've been incredibly fortunate. But it was sort of an industry where I was lucky and I kind of chose to be kind of oblivious to what was going on. Now we're in an environment where women are just increasingly having a huge impact and adding a lot of value to our industry. And women are celebrated. If they raise their hand, they say, hey, you're missing my value. You're not recognizing what I'm doing. And at Google, we strive at Google Cloud to have an environment where no one needs to raise their hand. But no matter what, it's completely safe to do that. OK. You know, I really look forward to the day when this audience is maybe 50% women. It's more fun to have diversity. So shifting gears, now I want to bring on our next keynote speaker. It's my phenomenal privilege to introduce our incredibly smart, compassionate, and just great leader, Sundar Pichai. Thank you. This is a big crowd. I've done many IEO presentations here, and it feels like a bigger crowd than Google IEO. So thank you all for joining today. It's great to be here. Google was founded on a mission to organize the world's information and make it universally accessible and useful. Over many years, we've had the privilege to create products that have served billions of consumers across the world. To do that, we've had to engage pretty much every branch of computer science. And we have had breakthroughs along the way. It has led us to breakthroughs in cloud computing technology, collaboration software, analytics, things which are now transforming businesses everywhere. So with Google Cloud, now we offer businesses Google quality computing, security, and business tools. And it is an extraordinary big bet for us. Both as a platform, internally for our own innovation, but also as a way of bringing our infrastructure to serve the needs of businesses across the world. Our customers and partners will see many ways to leverage all of Google, be it Google Maps, Android, YouTube, our devices ecosystem. And we'll deliver it over the most efficient, high performing, and secure core computing resources. For over 15 years, we've also been investing in machine learning and AI. And as businesses increasingly shift online and create more information, they can leverage these AI and machine learning tools to understand the information and build better products that delight more customers. Above all, we are excited to see what you all will create with our products and services. When we ship things on the consumer side, we always get surprised in how they use our technology in 1,000 unexpected ways. It makes us better as a company. With Google Cloud, we hope to have the same journey. We hope to grow and learn alongside you. Partners already have pushed us to add more ways to collaborate beyond video conferencing. They've exposed us to more real world scaling needs. For example, the demand that retailers like Home Depot get on Black Friday. They've shown us exciting new use cases for Google products. You will see what eBay has done with Google Home as an example. In many ways, to me, Google Cloud is a natural extension of our mission to make information accessible and useful. We're just doing it for businesses. It means increasing the availability and lowering the cost of data storage and computing resources. It means making it easier for businesses to understand and act on critical information. That's what G Suite does. G Suite enhances information sharing and collaboration. Machine learning finds previously hidden patterns and data, new information, which helps businesses improve their products and better serve their customers. Some really excited by the progress here, and I can't wait to see what you all build next. So without further delay, I'm going to invite Diane back onto the stage. She joined us in late 2015, and the business has grown dramatically for us under our leadership. And given that it's International Women's Day, it's also a pleasure to welcome back on stage a trailblazer for women in technology. So let's have Diane back to share more about how Google Cloud is helping all of you. Thank you. Thank you. Thank you. That was great. Thank you so much, Sundar. We really are one Google. And at Cloud, one of the reasons I joined, we're leveraging almost two decades of innovation and technology really built for the kind of enterprises we have today, or we're starting to have. I want to just kind of underscore or say a few words about our infrastructure. We don't really normally give out many numbers, and I'm not going to necessarily break that policy, but I am going to tell you some interesting nuggets. We have possibly the world's biggest, most powerful, secure network. Tomorrow, Urs Hussle, who really is employee number eight behind our data centers and infrastructure, is going to go into this more and tell you about what we're doing and where we're going. But today, what I'm going to tell you about is just the kind of the scale of our infrastructure. If you took not even our largest data center and stacked all the servers, one after another, it would be higher than more than 5,000 feet above Mount Everest. I was just going to say above Mount Everest, but the person that calculated this said, no, no, add the 5,000 feet. Just one building in our South Carolina campus is bigger than Stamford's Ariaga football stadium. We have a new complex in the Netherlands. It's got over 10,000 miles of cable. That's enough to go from here to Tokyo and back. And then one of the things we're all just very proud of at Google is we've been focused on being carbon neutral from the beginning. Compute takes a lot of power. Someday, we'll be as efficient as our brains. But it does take a lot of power. Google is the largest purchaser of renewable energy. That's wind farms. That's solar. In fact, our purchases are larger than the next three or four purchasers combined. So why did I tell you about that scale? Well, I want to give you some examples of why it matters. Just two customers. You couldn't have failed to notice that Niantic Labs launched Pokemon Go earlier this year. And just six days after they released, they kind of eclipsed their most optimistic three-month predictions. Their queries increased 50-fold. So they were running on Google Cloud. And our site reliability engineers were with them 24 hours a day, seven days a week, making this thing go flawlessly. They were there around the clock. In fact, it's when we realized we needed the same kind of functionality for our customers. Pokemon Go last week at the World Congress, they announced, Niantic announced, that they've had 88 billion Pokemons caught. Painless scaling on Google Cloud. The other example is an enterprise customer, very innovative retailer, Home Depot. They're now running on Google Cloud. And we got them through Black Friday. It was the most painless Black Friday ever. And they'll be here later today to talk about it. Also, with that scale comes the need for phenomenal security. And I'm just going to go into that a little. This shows sort of the layers of security. And you can see there's many gaps in between the networks, the servers, and the software. And so we build our own chips in the network and the computing devices. We have to look at every single endpoint. We actually have over 700 people just full time worrying about security, watching the network securing Google Cloud. And if you add a Chromebook, which this list doesn't store, you get an added layer of security endpoint. I personally only use a Chromebook. By the way, Chromebooks last year outsold Mac OS in the Macs in the United States. And something we're really proud of is what we're doing for education. Over 20 million Chromebooks used by students in schools worldwide and growing. Thank you. So Cloud is just a transformational technology. It's changing how people architect their information, use their data. It's changing how they work with their customers, how they work together. And in effect, our companies are becoming virtualized. We're operating both in the physical world and the virtual. And there's many things you can do in the virtual world more powerfully than you can in the physical, particularly as we all start taking advantage of machine learning. I really believe that the cloud with the best technology is the best cloud. It gives you a competitive edge. And you want to be on a cloud that's going to keep you on the edge of technology is moving so fast. And you want to maintain that competitive advantage. But all this technology needs phenomenal customer focus. We've really doubled down on our customer-facing part of Google. And I'm super proud today to give you those proof points to have our customers here to tell you about that, customers and partners. Just to give you a preview, we've got Disney here. We've got our partner SAP. We've got Colgate Pomalov. We've got Verizon. We've got Home Depot. And we've got HSBC, one of the world's largest banks. And then we'll close out with eBay with a pretty wonderful demo. So I'm going to begin by bringing out one of the most loved brands in the industry. Disney was here last year. And they were just stabbling in the cloud, just starting to do some things. And they actually got more done than they expected. They're ahead of schedule. They're all in, lift and shift. And then they have aspirations for even bolder things we will do together. So I thought it'd be very interesting to start out with the CTO and SVP of Disney, consumer products and interactive media. Mike White, please welcome him to the stage. Yeah, we have chairs. I was worried. I was wondering where they were. It's so great to have you back, Mike. Thanks so much. We just started talking about moving Disney interactive consumer products to the cloud. And now you're taking a cloud-first approach. Can we get an update? Yeah, for sure. First off, we really looked at moving to the cloud as a way to transform the way we develop software. And I can tell you it's been truly transformative for us from software development perspective. Currently, we have about 500 projects in the cloud. That includes everything from customer-facing or guest-facing products and production, we call it, to dev, to QA, to our R&D environments. And also, all of our data suites are going to be running out of BigQuery. We've started with a cloud-first approach for all new applications since the last time we talked. And we've had the luxury of actually being able to re-architect a lot of our legacy systems for the cloud. And as you mentioned, we're ahead of schedule. And that's really a testament to, you said the word, partnership earlier. It truly is a partnership that we've developed with Google that's been really helpful for us. So thank you for that. Oh, you're welcome. I think our teams just really clicked and really just collaborated phenomenally to bring your vision to life. Yeah, for sure. I mean, I think it was really great talking. Developers to developers and making developers come in first for this. But what was really interesting was, within our division, we have different application characteristics. So we have some really top-ranking games. We have Kids JAD app. We have several Disney sites that are all running out of Google Cloud. And that's both internationally and domestically. But what that's really done for us, and I talked a little bit about being transformative from the software development side, was it's really been able to free up our developers and our resources. And the way we look at our developers is they're technologists that are storytellers. For us, we use technology in service of the story. That's what we do at Disney. It's been our legacy since Walt. And so we've really been able to focus on telling great stories. And with that, we've been really taking a machine learning mindset as we're on our road to AI. And so if you think about character development and extension of characters and really getting closer to the characters, that's really what we want to play. And really with this first lens of machine learning, I think in the short term, we're really looking at how that will impact retail, which we're really excited about. It sounds like magic helping magic. Do you mean just for your operations you're doing this, or is it really touching the customer? It is both. So for sure, we're leveraging things like image recognition technology for some back end services. But we also have 8,000 characters within Marvel. And you can imagine all the images that come with that. We're also looking at ways to really strengthen the connection of our consumers through optimization, personalization, and really enhanced experiences. And so those are customer facing and back end as well. We look at that as kind of symbiotic relationship. I just love your mission at Disney to bring this magic to the daily lives of people and your fans around the world. It's so great to support that. Yeah, I mean that's what gets us up in the morning, right? I mean, our companies' stories and characters mean so much to so many people. I mean, for every one of the audience, I can imagine that you have a favorite character that's Disney, or Pixar, or Marvel, or Star Wars. And with that, maybe you have multiple, which is great. And so that's really what we do. We use technology to empower and really create the magic. And we're really excited with the potential of machine learning and AI what that can do for us. So thank you. Thank you, Mike. You know, I can't wait to see where we are next year. Thanks so much. Thanks so much. Great having you here. Thank you. Thank you. You know, it's pretty exciting for us to see this full lift and shift happening from just sort of an initial fairly small engagement, sort of what I was describing earlier, and then next year doing even cooler things with our commitment to really just all out partnering with our customers. Now, actually, I just want to mention that in 1955, the average age of a company was about 75 years. And now in this year, the average age of a company is only about 17 years. But all of our customers and partners that are here today have been around much longer than that in our beating the odds, even eBay, you know, at 22 years. I don't mean even eBay. I mean, even our youngest has been longer. I love to partner. I love to work with a community of technology companies. And we can build on their offerings. They can build on ours. And it's just terrific for the customers, customers first oyes than our partners. We've been working with SAP for over the course of the last year. And the more we've been working together, the more we see that we can do. I was recently in Germany. It was very exciting to be at this, you know, the world's biggest ERP firm, a company that when I started working in the IT industry at CyBase, pre-public, you know, they were already such an impressive company. So today, I am really thrilled to welcome to the stage Google Cloud's latest major strategic partner, Burt Lucard, head of technology and innovation and a member of the executive board at SAP. Thank you, Diane. Oh, thank you. And a warm welcome to the Google Cloud attendees as well from my side. Yeah. I mean, you said it already, representing here SAP as a company which now for almost 45 years is enabling companies to succeed across 25 industries, across 11 lines of business with our cloud, and in historical times as well, our on-premise products. And I mean, you refer to the former times. I still remember joining SAP when mainframe computing was the flagship. We had R2 at that point in time. And since that time, we have led many technology waves and helped our customers to succeed going forward. And as you saw that technology passion, I like what you said. And our focus on customers has rewarded us that we are able to serve now more than 345,000 customers. I have to say enterprise customers, creating products and used by millions of people and decision makers day in and day out. And one of our most important innovation just recently is, of course, a growth engine for us. Last couple of days, this is our flagship product SAP HANA. SAP HANA is our in-memory data and analytics platform. And in the meantime, as well, used by more than 14,000 customers. And these customers implement scenarios that leverage HANA's advanced analytical processing. They use spatial graphs, streaming data, text analysis, as well as predictive capabilities. And to turn machine learning and insight into real intelligence and then follow into action. Of course, as well, use that platform for our traditional transactional business. It's just such an impressive story. Hasa, Platner, and you, and how you've been so agile and keep reinventing SAP. And HANA, I first was aware of Hasa because of my sale boat racing has very good taste in boats. But he's also been great for design thinking. You've led in the mainframe in the client server and now making a core part of your business, the cloud. And I mean, Hasa is basically one of our founders and still the technology advisor. And we have now, as SAP, the goal these days to become the cloud company powered by HANA. But now we are ready to take things a step further. And today we are delighted to announce here in San Francisco the general availability of SAP HANA on Google Cloud Platform. Of course, if anybody questions, with full support from our SAP's existing support contracts, no doubt about that. And we will offer enterprises the words leading best network, the fastest service, and the best enterprise data platform for mission-critical applications analytics in one stop. And for the developers and the engineers in the audience, I'm also pleased to announce that the developer edition for SAP HANA, we call it HANA Express, is also being offered in Google Cloud Launcher, which is Google's marketplace for enterprise-grade partner applications. And with just a laptop, it's going to be easy to build SAP software applications, capable of instantly working with the largest possible suite of products, accessing data on-prem and in the cloud world. And obviously, this is great news for our corporate software developers, and I mean our joint corporate software developers, looking to build new products on existing data and applications. And our cloud partnership story with you, with Google, does not stop here. This is just the starting point. We are working on running the SAP Cloud Platform, and specifically our cloud foundry-based platform as a service framework, with a host of business and integration service, as well, on the Google Cloud Platform. And we recognize Google definitely as a front-runner, as a front-runner in technologies and specifically in topics like containerization, in scalable data processing, which are featured prominently here at the Google Next conference. And our SAP Cloud Platform and big data teams will work very closely with your teams on making technologies such as Kubernetes, such as Spanner work for scaling enterprise workloads. And I see a tremendous opportunity moving forward from that point for our customers to build these scalable enterprise applications using these endless combinations of services, of APIs exposed on the Google Cloud Platform, but as well on the SAP Cloud Platform. And we will have that offered together for you, for our customers. And lastly, our joint memorandum of understanding on machine learning co-development is just the starting point. It's just the starting point, and you can expect to hear more from the two of us or from our companies. And our next big milestone is the SAP Sapphire event, which is SAP's Google Next in May in Orlando. You know, this is just a tremendously important partnership to us here at Google. And our commitment really is to SAP and your thousands of customers as another, yet another, but a really big deal element of our partnership with SAP and between SAP and Google Cloud. We're collaborating to develop leading industry governance risk and compliance. We want a solution for the public cloud. And what that means is it's going to enable SAP to be a data custodian of the customer's data that's stored in GCP. It's increasingly important for enterprises, customers moving from on-prem to the cloud to retain their insight, their control, while still benefiting from the agility, the scale, the presence of a public cloud. And we see this joint solution as the basis for a new way to approach compliance and governance in public cloud environments. I completely agree, Diane. And being with Google Cloud ensures, from my perspective, deployment to the greatest number of markets and enterprise envisions. And our strategic intent is to work on extending GCP's robust set of existing capabilities to jointly develop solutions that bring global visibility, access control, which our customers ask for, to our customers for improved risk and compliance management. And to serve as a data custodian, we feel honored. And this is the first step we analyze GCP's audit logs, assure appropriate visibility, and control. But again, not for us. Again, for our customers. And ultimately, work towards approving access control requests. And even, you mentioned security before, encryption key management on-service provider access to the customer's data. I really think this partnership is going to improve compliance and risk and really ease the hassle that thousands of companies, millions of customers are facing. We really see opportunities also for more of the machine learning on SAP's data and applications. The enterprise is just more value and insight from the data they already have, building these new applications. And that's why we're announcing these plans to work jointly on our new machine learning. We're also really interested in tighter integrations with G Suite. Is this your part? No. OK. With G Suite, it's collaborative tools. And anyhow, we actually G Suite, I'll just say, is a platform also with open APIs. And so we are integrating G Suite with the SAP applications. I think that's going to be super exciting for people. Really increase customer satisfaction. And just to underscore that, we have a joint customer, just wonderful person. And from a company that's actually 211 years old, I'm really pleased to welcome Mike Crowe, the Chief Information Officer from Colgate, Paul Molliv, if he can join us on stage. By the way, a long-lasting friend of mine. Hi, Mike. Thank you. Great to have you here, Mike. Please, why don't you just tell us a little bit about your Colgate, Paul Molliv, and your partnerships with both of us? Oh, sure. So first of all, thanks for having me. I'm happy to be here. Colgate is a leading global consumer products company, selling our products in 223 countries around the world. Our 38,000 Colgate people focus on four core categories, oral care, personal care, home care, and pet nutrition. Likewise, we have a very focused IT strategy, which at its core relies on a small number of strategic partnerships. We chose SAP 23 years ago and Google Cloud just last year for very much the same reasons. You're both highly innovative, and you both have a willingness to partner with us. And we see tremendous value from implementing your technology. We have, of course, been innovating with SAP for years on business processes. And we're already starting to see the benefit of Google's collaboration suite of applications. So Mike, I mean, you were one of the first potential customers I met when I joined Google. And now you've implemented G Suite. Can you tell us a little bit about it? Sure. So we started our project in May of last year. And we were very encouraged by the positive email responses we received from our employees as we announced that we were going Google. Still, we knew that it would be a major change management effort to go to a new collaboration platform. So Diane, we worked with your team, as well as our implementation partner, SADA Systems, to lay out a phased approach for implementation. And in just three months, we were able to launch for our global IT organization all around the world. We learned from that. And then we leveraged that excitement that our employees had shown to recruit a few thousand volunteers to be early adopters. And within that set, we had about 900 Google guides who were to be the champions on the ground as we implemented and to be the trainers as we rolled out. And all of that set us up very nicely for our final implementation, which was in one weekend in November, we took more than 20,000 users live. So all told, more than 28,000 users in six months. Yeah, Mike, I mean, it's just phenomenal to successfully move 23,000 people over the weekend. And I can't tell you how much I enjoyed the little video you sent me to educate your company where you're sitting there talking to Mr. C, a cartoon character. What have you seen since you launched? Well, in just a little over three months, we see that people are working differently. Sure, everybody is using mail and calendar. But the usage of the other tools is quite impressive. 90% of our users are active on Google Drive. And in February alone, we had more than 57,000 hours of video hangouts, allowing our user base to connect more easily both while in the office as well as on mobile devices while outside the office. And this is critically important for us as our Colgate teams around the world work more and more together from different locations. And then finally, I'm seeing a faster uptake in the productivity suites in docs and sheets and slides as well, faster than I had expected. So Mike, I still remember when you asked me last summer, how is your relationship to Google? And I was thinking, OK, we have conversation. Now he is asking me. He knows me now for more than two decades. But now we are here together. I think it's pretty amazing. But I have to ask you, now we have made the first step. What excites you going forward? What should the two of us deliver to you going forward? Well, first, I'm very excited about the Google SAP partnership, about the announcements that have been made here today, and about the possibilities that the combined strengths of these two great companies bring out. I think it makes a perfect sense. It's a perfect fit to combine SAP's business enterprise business application expertise with Google's expertise on infrastructure as a service running in the Google cloud platform. Burt, you and I have been talking about incorporating machine learning into a broad array of the SAP business processes for some time, so excited about that as well. And then one more example, as you know, we're users of SAP's digital boardroom, a co-innovation project between us and SAP, which allows us to review the state of the business at any time using the latest data. And we're already working on incorporating Google Slides into that application as well. So in summary, I think the possibilities are going to be numerous for us. This has been really terrific for us. We've really enjoyed working with both of you. It's great for Google Cloud. It's great for you guys, and we're really looking forward to see what we're all going to do next. Thank you so much, both of you. Thanks for having us here today. All right. Thanks a lot. Thanks, Mike. I was trying to. So as Mike was saying, G Suite is this fantastic collaboration suite. It's really transforming the culture and the productivity at Colgate. One thing also that I'll just add about G Suite that we hear in every customer that deploys is the young people so rejoice when G Suite gets rolled out. And one of our bigger customers, PWC, actually did a poll of their employee base. And the number one valued thing from particularly their young employees was their collaboration and productivity tool. It's so important to them. OK. So for our next guest, I'm really excited to bring out Premier Technology Company, Verizon. They've got 114 million retail customers. We've been working closely with Verizon for a year now. Not simply. And we've been working with them on workplace productivity. It's been a long project. It's been a challenging project. And I really wanted to talk about that here. It's gone really well. Great outcome. And here to talk about it coming on stage, let me welcome Alan De Silva, who's the vice president for Digital Workplace at Verizon. Hi, Alan. Thanks so much for coming. Great to see you. Good morning, everyone. At Verizon, we believe deeply in our brand statement, which says better matters. And we believe better matters not just for our customers, but our employees as well. We are keenly aware of their need to move faster, collaborate more effectively, have flexibility in how they work, when and where. And it's really a commitment we've made to a much more modern and progressive work environment. And we're delighted that G Suite's going to be a big part of that transformation. So are we. We're delighted too, Alan. Starting in just a few weeks, I understand you're going to start moving 150,000 Verizon workers over to the G Suite's platform. But we both know it didn't happen that quickly that you started down this path more than a year ago. And I'd just like to know, what were you looking for? Yeah. So a little over a year ago, we formed a group that was really focused on raising the bar in productivity through collaboration and employee engagement. And we've done a lot in that short period of time. We fostered collaboration by rolling out activity-based workplaces. We rolled out unified communications globally. And we made improvements to our mobile options. But we realized that we were really missing some key features, things like real-time collaboration, and other modern features that you see in these productivity suites. So we did a comprehensive trial of G Suite. And our employees really loved it. They loved the simplicity and intuitiveness of the product, the ease of use. The real-time collaboration really blew them away. You know, it worked well on desktops. They were really surprised by how well it worked on mobile devices. And the simplicity of the solution actually helped us secure the platform in a much more straightforward way. And then I'd say the last thing is that we realized that actually a number of our employees at Verizon, specifically at companies like AOL and Telogis, were already on G Suite. And so for us, that's how we came about, make the decision. I'd say one more thing. We also realized that we had to escape to where the puck's going, because in terms of talent, we knew that as the years past, the talent that comes into the company is going to expect a product like this. And so we had that in mind as we made our decision. Thank you. You know, Ellen, a lot of the time changes. Change can be pretty hard no matter how good the technology is. And did you have resistance in going to the cloud? And what did you do about that if you did have that resistance? Oh, sure. We had a fair amount of resistance. But it really is a matter of addressing the fundamental perceptions of what it takes to go to the cloud. And so what we did is we cracked open the door. We asked everyone to sort of keep an open mind. And then over several months, we got really deep on a number of topics like security, operations, user experience, regulation, and compliance matters. And we also looked closely at ROI, of course. And in going through the trial, in talking to you, in talking to your partners, in talking to your customers, we realized that really it was the right decision for us. The Google team was really fantastic to work with. Eric Geithner, if you're out there, kudos to you. But they answered all of our questions. They got us really comfortable with the decision that we needed to make. Thanks for that shout out to Eric. Alan, do you have any advice for anyone in the audience that's looking at making a similar transformation that you've gone through at Verizon? Absolutely. So first of all, you've got to bring along all the stakeholders that are going to be key in the decision making that you're going to make. So bring along your CISO and legal teams, for starters for sure, because they need to kind of go on this journey with everyone. They've got to understand what it's going to take and get comfortable with it. The other thing we found is that we learned a lot from customers of Google and G Suite and from partners. So I'd say definitely take advantage of that. Remember that this is partly a technology transformation. But it's really about change and driving change in the organization. So you've really got to give thought to, how are you going to drive change management around this? And I would say if your mission is to go transform the company, then you really have to have meaningful change. Otherwise, where's the transformation? Right. So what has the reception been like since you announced this move to G Suite? Oh, the reception's been pretty interesting. So we put out an article on our internet site in the middle of January. And within a couple of days, it got like the most reads for the whole month. We had hundreds of comments and questions that were posted. And people saw it as a really meaningful step in the right direction. These were folks who had experienced G Suite at other jobs or had used it in their personal lives. And they were really comfortable and excited about the idea that we would go forward in this direction. And then there were a number of employees who naturally were concerned about how it was going to change their job. They were used to doing things a certain way. And they posted a lot of questions online. And we went through and he answered each and every one of them. And that really helped the broader community understand where we were going. And that was really important. We also identified all the early enthusiasts, the ones we call them Google Guide, just like Mike said earlier. And we're going to rope them into the early adoption phases of this so that they can get on board and get ready to support our colleagues when we bring them overall. So it was a great validation. It was a great early validation that we made the right choice. And we're really looking forward to starting the migrations in a few weeks. You know, it's a big step, Alan. And I really admire the commitment you've brought to bear on this. We've been working pretty closely. I know I'll be hearing more from you. And I really look forward to the updates and supporting you. Great. Thanks, Diane Fadden. Thank you. Thanks so much. Bye-bye. Yeah. Yeah, G Suite is built for the cloud, integrated, works seamlessly. It's really proving its value. Going back to Google Cloud, once someone decides to move to Google Cloud, we really have an incredible commitment to deliver top quality customer support. We don't want any of our customers going down. We use site reliability engineers to keep our seven apps that have over a billion active users available. And we realized, as I said before, with the Niantic Pokemon Go launch, that our customers need the same. We established a new organization called Customer Reliability Engineering, where those people work with our customers to make sure that your services are architected and monitored and keep full availability. Now, Google can't scale to do enough customer reliability engineers for all our customers. And so what we've done is we've reached out to partners. And I'm really excited today to announce two of our early partners. We have Pivotal Labs, Premier Consulting Development Company, Leading and Agile Development, our first certified CRE partner. They offer CRE services to our customer. And then another customer, really a pioneer in the cloud. They understand the cloud inside out. That's Rackspace. And they're our first managed service provider. And we look forward to doing more and more with them. Now, somebody that's already taken advantage of our CRE Home Depot, I'm really excited to welcome to the stage Paul Gaffney, Senior Vice President at Home Depot. Hi, Paul. Thanks for your help. Yeah. OK. So you've got an impressive history. Quite an innovative retailer doing extremely well. How do you think about technology? Technology is a really important thing to us, particularly in this landscape, as consumers' expectations are increasingly being shaped by the software that they use. We've got over 2,000 stores in North America, 400,000 real, live human beings who take care of our customers. And that's our heritage, that excellent experience that you get when you're talking to someone wearing an orange apron. And increasingly, consumers are expecting that we do exactly that same thing in software. And so we think about software as an extension of our overall culture. It's sort of your virtual company running, isn't it? It is. And what have been the proof points in using the cloud for you? The important thing about cloud for us, as we started to get better as a software engineering organization, we paid attention to a lot of different things. One of those things was building cloud-native apps. There were a variety of reasons why we focused on building cloud-native apps. But we also said, well, we should probably run them on the cloud if we're going to build them cloud-native. And cloud plays an important role for us economically. Cloud economics are a substantial improvement to our on-premises economics. But also thinking differently about resiliency and availability, moving from, I think, what's a classical big company IT orientation, which is make the infrastructure robust to a more modern view that says, just assume that everything's going to fail. And that was a big catalyst for us to engineer our software differently and partner with you guys on learning what does it mean to live in a world where you make your stuff resilient to failure. You know, I've come to love all these days when you have these spike loads. And you guys recently, of course, every year you have Black Friday. I was just wondering, how did it go for you guys this year? I'm glad that you love these spike loads. And as shareholders, I think my fingernails know. Yeah, so do we. Not really. As everyone in here is a consumer, I think you guys all know that there are times during the year when all of us in retail attempt to conspire to get you to do more shopping than is normal. But those of you who are in the room who know that that ends up somewhere, that has to be processed. One of the fantastic things about an elastic environment is you don't have to attempt to provision in advance and pay for in advance all of that spike. And if you architect your stuff correctly, you can scale that out instead of having to scale it up. And we were really nervous, not because we weren't prepared. The team that worked on this does fantastic prep. And you guys were a great partner. I mean, outstanding prep. But everyone's kind of nervous because, thankfully, our 400,000 folks do such a nice job each year that more folks do business with each year. So this last Black Friday was an all-time high for us. And we were running meaningful portions of our e-commerce infrastructure on Google Cloud for the first time. And it was our smoothest performance ever. Just tremendous to see. And it's great to see you using Kubernetes and containers. How about your work with Big Data? I think, as you know, a very interesting offshoot of our partnership on cloud was thinking about, OK, we're going to be moving some main line, line of business workload from on-premises to the cloud. Should we be doing the same thing with data? And as the audience might imagine, we've got a lot of data. And we replenish those 2,000 stores continuously. And we have started to move quite a bit of our data analytics environment from proprietary on-premises infrastructure onto BigQuery and other parts. I think I'm excited by the technology. But frankly, I'm more excited by the investments you guys are making in talent. And I'm looking forward to the work we're going to do on exploring some really interesting problems where we think that the machines can help us understand some things, perhaps, that the humans find difficult to understand. Yeah. I mean, we're really looking forward to this ongoing work and I can tell you that Fei-Fei Li and her cloud machine learning group are really excited about what they're going to be able to do with Home Depot. Thanks so much, Paul, for being here. Great pleasure, Diane. Thank you. OK. Bye-bye. Thanks. So now we're going to turn and we're going to talk about security, privacy, data analytics at scale with a big bank. And here, to lead that discussion and tell you about it, I'm delighted to welcome Tarik Shao Ket, Google Cloud's president for all customer-facing operations. Hey, Tarik. Morning, everybody. So you've heard a lot today from our customers and you've heard a lot today from Diane and from Sundar about what our mission is here at Google Cloud. But to put it in the terms of what we're looking to do for customers, we very much view our mission at Google Cloud and on our customer team as generating real step change in business value for our customers. That business value happens in a number of different ways and increasingly in mission-critical applications. It happens, first of all, at the cost savings level of how do you actually move to a more flexible environment? How do you move to a more agile environment? How do you move to a lower cost environment? And so that is absolutely one of the major conversations that we have with our customers. As you heard Paul talk about and you heard Mike from Disney talk about, we also are increasingly seeing the cloud being used as a platform to drive real, tangible revenue growth for companies, for them to both create new applications, but also critically importantly, to understand their customers better, to get into their data better, and to really understand how to become closer to their customers, how to become more efficient in their operations, and how to engage their employees better. So you have the cost savings piece and you have the revenue generation piece. But the third part that really has been a thrilling evolution of the market is how many companies are really coming to Google Cloud now and saying, we need a partnership and we need a partnership to help transform our business, to really help us think about what is the business model of the future and how do we take our existing operations and how do we move that in a both gradual way and in a step change way into the future. What one of the most thrilling partnerships that we have in this regard is a right with HSBC. HSBC challenges us in each and every one of these areas and the cost savings on the revenue growth and on the transformation levels every day. Now you may know them as one of the largest financial institutions in the world. They have over 150-year history, 4,000 offices and 70 countries and over $2 trillion of assets on their balance sheet. Now to tell you more about this, I'm truly delighted to welcome to the Google Cloud stage Daryl West, who is the global chief information officer at HSBC. And he's going to share his thinking about the role of cloud and cloud scale data analytics and machine learning for the financial services world. We are living in a time of rapid digital change, where the only limits are the scale of our ideas and degree of our dedication. The technology ideas of today will be the intuitive habits of tomorrow. Never before has innovation promised so much, touching so many in so many different ways. Our frontier is financial technology. It's changing the way we work, make money, and the way we trust each other. To succeed, we need to adopt the mindset of the Silicon Hub to the world and create an incubator of free thinkers, thought leaders, innovators born of collaborative spirit, all fueled by a belief that technology will enhance our customers' financial lives. To imagine a future beyond what we already know, creating a team with the power to transform the world's leading financial enterprise. We are technology. Thank you, Tarek. And good morning, everybody. It gives me immense pleasure to be able to be here with you today, particularly on International Women's Day. I've been at HSBC now for a couple of years. And I have the honor and privilege of leading an awesome IT team that's at the center of an amazing transformation happening in our organization. I'm going to talk about our commitment to cloud, and I'm going to talk about how we're collaborating and partnering with Google to make HSBC a simpler, better, and faster organization. An organization focused on amazing customer experiences through new digital channels and a company committed to using the latest technology and data analytics and machine learning to transform the way we run our business. Now, I'm sure many of you in the audience are travelers, and I'm sure many of you have stood in airport walkways and seen the HSBC advertisements, and they're quite distinctive. And you've probably asked the question, who are these people? Well, who are we? We're the world's largest international bank. As Tarek said, we've been around for 150 years. We're present in 70 countries. We have 37 million customers, customers ranging from individuals to small businesses, mid-sized corporates, large global companies, as well as governments as well. So we have a huge business, a huge global business, with the number one bank in the world for trade finance and cross-border financing, and we're a significant player in the global foreign exchange markets. So given our central position in global finance, as you can imagine, we are a systemically important financial institution, heavily regulated. Now, in the banking business, clearly trust and confidence is a central part of our business. We have to make sure that our customers feel confident and trust in us to be the custodians of their assets. So information security, reliability, and resilience in how we deliver our services are fundamental to our business. Now, apart from having the $2.4 trillion of assets on the balance sheet, we also have at the core of the company a massive asset in our data. And what's been happening in the last two or three years is a massive growth in the size of our data assets. So as you can see here, 56 petabytes of data in 2014, that's actually doubled as of now sitting here in 2017, over 100 petabytes of data. And what's happening is our customers are adopting digital channels more aggressively. We're collecting much more data about how our customers are interacting with us. And obviously embedded in this data is massive insight. And what we need to do as a bank is work with partners to enable us to understand what's happening with this data, draw out the insight so we can run a better business and create some amazing customer experiences. Now, our journey in data is very similar to other companies. This is no different to many enterprise companies, the history here. We have lots of good old fashioned databases, all of our core systems are running on product systems that have been around for 20, 30, even 40 years. The systems are robust and they're scalable and they do a great job, but they don't have the database structures that actually allow us to really do the data analytics and machine learning that we need to do. So the history of enterprises like wine is that we typically did extracts into traditional data. We're housing platforms, which worked well for many years, but of recent times they've become expensive to run and difficult to use. So we took the plunge about three years ago to really embed ourselves into the evolving Hadoop ecosystem. And for an organization like mine that has this traditional history of what we call legacy platforms, it's been a tough road for us. All of this has been done on-prem. We've had to provision large infrastructure, physical infrastructure, build out data centers and hire talented new people that have understanding of these new technologies. So we thought about this very carefully in the last year and we said to ourselves, well, really, we're a bank. At the core of our business, we're a bank, but we also have a significant technology company embedded within the organization. And the question that I was asking the management team is, do we really want to compete with the cloud providers and people like Google? Are we really going to try and do what they do as well as they do it? And I think our conclusion was that it was better for our business if we adopt a cloud-first strategy. So we started working with Google about six months ago now, so it's still early in our relationship. But we've been working with them on some of the most important and critical business problems that we have to solve in our business. Give you an idea of what these types of problems are. So the initial use cases here are typically characterized by business problems that have very large data sets and require very intense computing capability in short bursts. Now, the first one on this list is all about anti-money laundering. So it's one of our obligations of being a bank. We work with the governments and the crime agencies to identify nefarious activity, money laundering, criminal activity. Now, we have a set of applications that monitor a huge time series of data. So a massive data set, billions of transactions for all of our customers. We're running analytics over this huge data set with great compute capability to identify patterns in the data and to bring out what looks like nefarious activity within our customer base. And those patterns that we identify then escalated into the agency. So we work with them to track down the bad guys. So this is an application that is clearly requires massive data sets, great computing capability, but also a machine learning capability to be able to identify the patterns in this huge data set. The other applications that are mentioned here in terms of finance and risk, we have billions of transactions, those transactions need to be aggregated so we can manage our finances and our risk at a global level. Evaluation services in our trading business, as I said, we're a major player in global financial markets. We need to be able to run complex multicolor simulations on a regular basis to be able to better understand our trading positions and our risks. So this requires a significant compute capability. So we were faced with the question of, do we build out a new data center and put thousands of servers and thousands of cores out there to do this activity? Or should we actually work with somebody who does it as a core business? And our conclusion was, rather than build that out as ourselves, we should be working with world-class leaders in this space and hence our work with Google. Now all of this doesn't happen by accident and there are some critical enablers that have to all be in place to make this work for organizations like us. The first couple of boxes here talk about risk and compliance and information security. You know, we are a heavily regulated business. We have to provide very resilient, very safe and very performant services to our customers and also very agile because the requirements of our customers are changing tremendously quickly. We also have to respect the data protection and data residency rules that we have in our 70 countries. There are lots of rules about what data can sit where and what data can be shared. So we have to respect all of that and we're working closely with our partner at Google on this construct. Information security, as you heard already this morning, it's a massive issue for everyone, particularly for a bank. We have to keep our data safe from the cyber criminals and we're working very well with Google on solutions in the security space. Probably the biggest impact though or the biggest enabler for this journey is the other two boxes in the middle here. You have to drive a cultural shift in your business to make this really work. You have to adopt an agile methodology. You have to adopt a DevOps mindset and you need to be able to recruit and retain talented people that understand how to use these new technologies and work in this way. So just a point of note, we are recruiting. Okay. And we're a great company just like Google. So the other issue here on the second box is about integration. I mentioned the legacy platforms we have. All of our core data is sitting on these systems of record. We have to make sure that we can integrate all of this technology complexity to work with the Google platform, the Google Cloud Platform. So this integration layer is very, very important. You've got to get that culture in place. You have to get the people that understand how to do it and you have to build the integration layers to be able to make it all work in that rapid cycle. And lastly, of course, data preparation. You know, we have, like most big enterprises, a data sprawl, as I said, and a massive growth of data. But also, you need to invest your time and effort in your data architecture and making sure you understand where your crown jewels are, where the copies are, and get a really good understanding of the preparation of getting the data ready to be able to interface effectively into the Cloud Partner. And obviously, the agreements of legal and commercial are obviously very important as well. So in summary, these enablers are fundamental to us achieving our goals at HSBC. And we're working in a very collaborative and partnership mode with Google to get these enablers in place to run a better business. Thank you, Daryl. It's a fascinating journey that you guys are on there at HSBC. Just a couple of questions for you. You mentioned that you did a pretty thorough investigation of the technologies, particularly as you were looking at us around data flow and BigQuery. What aspects convinced you to move forward with GCP? Yeah, we've spent a good amount of time working with all the big cloud providers and evaluating their products. I think if you look at the scarce talent we have on data analysts and data scientists, they need tools that allow them to do their work in a very productive way. So we've found, as we've worked with your team and your subject matter experts working with our data scientists, that your suite of products have been very performant, very easy to use. There's always a challenge of getting data scientists to shift from their favorite platform to a new one. And I think the way that your products are actually being configured and presented to our scientists, they've really enjoyed that experience. So it's early in our relationship. We're still in pilot mode, and we hope to get these use cases to production in the coming weeks and months. But I think our experience so far has been very positive. Terrific. What is just looking forward the next three to five years, how do you see the cloud really playing out and all these technologies impacting your business? Yeah, look, it's very difficult to be able to predict the future even three years ahead. It's a long way away, right? So the world's going to change. I think what's definitely going to be consistent is that our data is going to continue to grow. Massive data sets need to be managed. We know also we're going to be pushed to go faster. I mean, our customer base is dragging us into a new world and consuming services on new digital channels, which require us to be agile and fast to market. We know we're going to have the need to use data analytics and particularly machine learning to run a bit of business. So it's going to get bigger. It's going to get more complex. And we're going to need to partner with people like Google and others to be able to use the best technology in the world to solve these problems for our business. Great. Well, finally, I think everyone in the audience and I'm sure could use a little advice. You guys are leaning very forward into the cloud and cloud technology. What advice would you give everyone here for how to really start their journey? Yeah. Well, I think the first thing you need to do is you have to make the jump in thinking to make yourself a cloud-first company. It requires getting everybody on board in the technology team, but also the business. You get to get the business to think about things in a very different way. And I mentioned the criticality of new ways of working with agile methods, DevOps methods are really important. So that first thing is make that jump and be bold and have courage to go and do innovative things. Second thing is pick a partner that has the same type of culture as you. I think you need to. If you're going to be innovative, you're going to be bold and take on big challenges, you need a partner that's going to think the same way and work in the same ways of working. And just I think basically the conclusion I've come to is have a good look out there. Meet the people in the market because they're all very different. And certainly from my perspective, the chemistry between the people and the teams is actually the number one thing because what we're doing is complex, what we're doing is difficult in many cases. And the chemistry and the culture of the organizations is very important. And that's right. That's a multi-year journey. That's a multi-year journey. So you have to like how you're hanging out with for the years ahead. Exactly. Well, again, on behalf of all of Google Cloud, we're really thrilled that you're here today. So thank you very much. Thank you. And I'd like to welcome Diane Greene back onto the stage. Thank you. Thank you, Tarek and Daryl. That was fantastic. And now we're going to have a look at how an iconic e-commerce company, eBay, is thinking about the cloud. I think we have a video first, and I want to welcome on stage RJ Pittman, who is head of products, CTO. We transfer about 30 gigs a second and search through 1 billion active listings in our quest to find your perfect. Through cutting-edge technology and the performance and scale of cloud, we strive to meet our customers where they are and transform the future of commerce to help find your version of perfect. The future of commerce from eBay. RJ, thank you for coming. Great to have you here. Great to see you. So RJ, you've had a phenomenal amount of experience working in online environments. Why don't you start talking about how your view of the cloud has changed? Well, the biggest thing is it's moving, and it's moving fast. In the last four years, it's, from my perspective, gone from a very powerful and compelling proposition for outsourcing your IT infrastructure and shifting some things so that you can focus on your core business to now we see it as a strategic growth engine and a strategic growth engine that accelerates innovation. And this is a very different position that the cloud has taken, and it's playing a very important role for us in that manner at eBay. And what do you think that means for eBay? Well, you know, so in the video, as you can see, I mean, we've been around for a long time. The business is continuing to grow. We have over 1 billion live listings at any given moment in 200 countries around the world. 167 million shoppers. And so you can imagine that if we start moving the capabilities of the cloud to eBay, we're not just changing our infrastructure and changing our business. We're changing the experience for all of those shoppers and all of our buyers and sellers around the world. And that's the really important connective tissue here. This is not just a technology exercise for us. It is absolutely a customer-centric effort. Yeah, absolutely. And with that kind of phenomenal growth, I can see that you really are paying attention to technology to help you through it. Yeah, for us, the e-commerce space is competitive and it's moving fast. And the demands of customers and shoppers today is changing so quickly. And as new generations of buyers come into the world, first millennials, now Gen Zers, they have very different shopping habits, very different expectations. And they're much higher expectations. And so we eBay at our scale, if we're intent on setting ourselves up to meet the demands and exceed the demands of this next generation of online shoppers, we have to move quickly. And the cloud has become a very strategic partner for us in that way. So RJ, before we get into our cool demo, can you just tell me a little bit about what you're doing on Google Cloud? Sure. So this was an undertaking for us that we've been thinking about for a few years, but we really got serious about it a year ago. And we knew that there was a lot of untapped potential inside the eBay marketplace. And we wanted to get at it. And as a marketplace, let's also remember that first and foremost, we're a tech company. And we utilize technology to differentiate, to compete, to innovate, and to create these amazing customer experiences for all of our customers around the world. And so for us, we went out on a mission and said, if we could bring the eBay marketplace to a modern stack environment, if we could bring our customers to a place where we can be innovating almost every day and bringing great new features and capabilities to them, that would give us a tremendous advantage and a ton of momentum. So literally in the space of about five months, we took a great team of engineers and set off to do exactly that. And in five months, we went from nothing to the entire one billion live listings of eBay running in GCP. It was not a test. It was not a prototype. It quickly moved to a production grade environment for us that we ended up launching and bringing into market six months ahead of our plan. And mind you, that in total start to finish was about seven months. So it can be done. Do not fear the cloud. It can be done. And look at our business. This is super important. You have to be careful when you're serving live customers in a live marketplace like that. And now the way we've approached it, we were smart about this, and we didn't just flip the switch and move the entire $85 billion marketplace business at the throw of a switch. We're actually running a parallel swim lane. And that gives us lots of latitude to play with. And they're both live systems. And this is very difficult to do in most environments because we're transacting, as you saw, thousands of orders a second. And we now have two hot, hot systems running, our core system on-prem. And then we have the new Google platform, both serving customers. And we've had to build a system that ensured that we didn't create two separate marketplaces, meaning no two buyers could buy the same one item that's for sale because that would create obviously a lot of problems for us. And it was actually one of the most important decision factors for us in really leaning in on the Google platform is the services and the capability that Google offers that nobody else did that allowed us to build a true replication, replicated system that runs in real time side by side to help companies of our size and scale and complexity make that transition much more seamlessly to the cloud. And I mind you, the other cool thing about it is it didn't just allow us to go to a cloud instance here in North America, but using the global footprint of GCP. We took the show on the road, and we were able to test our theory and look at lighting up the Google Cloud in multiple countries around the world so that we could be serving where our customers are and providing a great high-speed performance as if that data center was sitting in their backyard. We've not been able to offer that kind of experience and that kind of performance in the 22 years that we've been around. So this is really breakthrough for us, and it's our customers that are the ones that really stand to gain. That's just a great story about what you've done. And I think also in another area, you're kind of leveraging AI and for your catalog, and what about that? So this is a really cool part of the story where you have a billion live listings and we have 22 years of historical transaction data. We have more economic data on the supply and demand information on virtually any product that's been made and sold in the last 20 years. We know what it sold for, what the demand was, what countries are most interested in what products and when, and you can imagine if we could then start to encapsulate and capture all of that knowledge and bring it forward for our shoppers, you could build arguably one of the smartest shopping experiences and smartest catalogs on the planet. And the challenge there, too, has been getting to a place where we had truly the computing capacity and performance to get our arms around literally billions and billions of records that go back in time, but also the new ones that are created every day that there's transactions on eBay. And going to GCP, we have the scale and the performance that allowed us to lay down some really great AI technology, going well beyond machine learning, going very seriously into deep learning, natural language understanding, and computer vision to create something that we've called eBay Shopbot. And this thing is pretty great. And it is the largest marketplace in social where we took our billion item catalog and merged it with a billion people in the social graph and brought them a highly personalized shopping assistant that gets to know you, that understands natural language, and can start to make really, really great recommendations across our entire inventory of a billion items to help you find the best item for you at the right time, at the right place, at the right price. And so when technology can start doing the heavy lifting and provide you with almost a concierge like personalized service, I think that's when we've really, really made great strides. And that's something that we brought together with the innovation at eBay and the great work going on in Google. I have to say that sounds like a perfect match for Google Home, and we actually have a Google Home sitting over there. Could you show us all what you're talking about? In the spirit of going to where our customers are, we've taken it to Google Home because we want to get inside the home. Let's do it. Now, you know, we're doing this live. There's like 10,000 people on the network. We really hope this works. Hey, Google, let me talk to eBay. Sure, here's eBay. Hi, I'm eBay. I'm the world's price guide. You can ask me what something is worth. What's my camera worth? Is it new or used? New. What is the brand of your camera? Canon. Tell me the model of your new Canon camera. 5D. What else do you want to tell me about your Canon camera? Nothing. Your camera is worth $2,548. Not bad. So this is maybe an unexpected use case, but one that's really interesting and close to the hearts of eBay because we support not only our shoppers, but also our tens of millions of sellers all around the world. And what this allows us to do is start to bring more sellers into the marketplace. You may have lots of really cool things sitting around your house that you didn't know had really great market value. Now you'll be able to ask Google and ask eBay through the Google Home partnership to get you started and bring you a little bit closer to eBay. And there's so much more to come with this technology. And let me tell you this. It was five months for us to go from where we were to in the Google Cloud, and it took us five days to get into Google Home. And so with that kind of power, there's great, great potential. RJ, thank you so much. Thank you. That was fantastic. Really fun being here. Thanks, everyone. I just love the fact that here's a chatbot running on Google Cloud talking to Google Home. We have one Google partnering with eBay. And now you're all in for a treat. We're going to bring Fei-Fei Li on stage. I first met Fei-Fei when she came to Stanford back in 2012. She's a neighbor of mine. And for those of you that don't know, Fei-Fei was really instrumental in the explosion of machine learning. She built ImageNet, which is the largest database of labeled images. And what that facilitated was all the academic researchers, industry researchers, now had something to benchmark against. And we started seeing this explosive improvement and adoption of machine learning. So let me just welcome to the stage Fei-Fei Li. She is in charge of Google's Cloud Machine Learning. She's head of the AI lab at Stanford and here now to lead us in our AI efforts at Google. Please welcome. Good morning, everyone. My name is Fei-Fei Li. I am the chief scientist of Google Cloud AI and Machine Learning. So in Google's code word, I'm still a noogler. And it's quite an honor and privilege to be on this stage to share with you some of my thoughts about AI, Machine Learning, and Google Cloud. So the world is changing incredibly fast. Some say that we're living in a fourth industrial revolution. And much of it is propelled by the phenomenal force of computing. As an AI technologist for nearly 20 years working on machine learning, computer vision, I've witnessed my field growing from a lofty but academic pursuit to the biggest driver of this change. But change happens at many scales. And it takes imagination to see them all. Let's take a familiar example, the self-driving car. It's easy to understand the appeal. With the help of sensors and algorithms, a self-driving car reduces accident risks and gives us more time to work, socialize, and just relax while commuting. This is great for a single driver. But what happens when thousands of people have self-driving cars? Suddenly, through the coordination of vehicles, traffic congestion is reduced, and parking is dramatically simplified. But what about millions of people having them? Entire cities will be reshaped to reflect the fundamental shift in the use of its infrastructure. So the difference between each scale is participation. As the technology reaches more people, its impact becomes more profound. This is why the next step for AI must be democratization, lowering the barriers to entry, and making it available to the largest possible community of developers, users, and enterprises. Speaking of democratization and reaching many people, Google's cloud platform already delivers our customers' applications to over a billion users every day. That's a lot of participation. Now, if you can only imagine, combining the massive reach of this platform with the power of AI, making it available to everyone, then we stand to witness a greater improvement in quality of life than any other time in history, from finance to education, from manufacturing to health care, from retail to agriculture, you name it. This is why delivering AI and machine learning through Google Cloud excites me. It means finally sharing the technology and insights I've been involved in for years as an AI researcher at Stanford. That's also where, by the way, I began a collaboration and partnership in AI with Dr. Jia Li, who was one of my first PhD student many years ago. I'm very excited that she has joined Google with me as the head of R&D in AI and machine learning at cloud. And speaking of International Women's Day today, this is another bad-ass woman in STEM, CS, and AI. There's no shortage of examples of AI solving real-world problems, such as the demo we just saw talking to the eBay shop through Google Home. As impressive as these achievements are, they're just the beginning of the transformation of the entire enterprise. More and more problems are being addressed by AI. And the tools we use to build AI solutions are becoming more and more sophisticated in their functions, but also easier to use. This will change our world dramatically, and it's happening at a faster pace than most people think. Let's take a look at a few examples. AI has been influencing retail as long as it existed. For instance, machine learning algorithms are already helping Google's adsense and shopping to deliver relevant information to our customers. But so much more is just waiting to be done, such as supply chain optimization for routes and inventory, or predicting changes in demand over time. Drones navigation and self-driving vehicles for delivery of items customers ordered. Intelligent analysis for loss prevention and safety. Or understanding customer movements and perception in stores to optimize shelf space displays a visibility. Another example for media and culture are already being influenced by AI. Do you have a teenager at home? Wonder why? What's the technology that's mesmerizing them with the cat ear and rainbow filters of the Snapchat app? That's a clever computer vision technology. Machine learning already delivers Google Photo Automatic Image Tagging and YouTube's recommendation list. But media experiences will soon make much more use of it. AR and VR will rely on computer vision for motion tracking, environment detection, and games. More and more news content will be automatically generated, allowing journalists to focus on bigger and deeper stories. And AI will play a growing role in helping us to create and stylize our own content, such as videos, music, and artwork. In the financial service world, we are already seeing machine learning to help fairly and intelligently to predict credit card risk for new applicants, or even anticipate delinquencies among existing customers. And many similar advances are in the work as we speak. Insurance claim will be assessed by machine learning agents. Banking will become even more virtual as conversation bots take over call centers or even in-person bankers will manage in finances. And as HSBC said earlier, our own perception will be augmented with intelligent agents to flag criminal activities such as money laundering or fraud. Last, but not the least example here, healthcare is among the most profound applications of AI for truly improving people's lives. We've already seen some incredible AI achievements in recent years. A few months ago, my colleagues at Google Brain has shown that using a deep learning algorithm, a computer can detect signs of diabetic retinopathy, a disease that could potentially blind more than 400 million people. Now, imagine that kind of insight in spending the entire healthcare industry. So many forms of visual diagnosis may soon be automated to help doctors and reduce overhead and errors and extending treatment to the underserved population. Machines can also help to handle clerical tasks such as scribing doctor visits, managing chronic diseases, and leading to more reliable and faster services. And this will accumulate into full-scale smart hospitals and homes with intelligent sensors to track hospital activity, keep patients safe, ensure adherence to hygiene practices, and augment surgical protocols. I hope you're excited just as I am about the opportunities that AI and machine learning can deliver. But this remains a field of high barriers. It requires rare expertise and resources few companies can afford on their own. That's why cloud is the ideal platform for AI. That's also why we're making huge investment in cloud AI ML that will emerge over the next year in a form of powerful, easy-to-use tools that will give every cloud customer an unwrap into this field. In other words, Google Cloud is democratizing AI. This entails four broad steps, democratizing computing, democratizing algorithms, democratizing data, and democratizing talent and expertise. And let's talk about what each of these means. First and foremost, AI requires enormous computing. Today, a deep learning algorithm can easily build tens of millions of parameters and billions of connections. Training and using such models require computational resource. Of course, that's exactly what the cloud was designed to deliver. Last year, we introduced the beta version of Cloud ML Engine. Today, I'm here to announce its general availability. Cloud ML Engine is a platform that can harness all the power, computing power, and deliver it to you transparently. Simply put, you develop the machine learning models however you like using familiar tools like the TensorFlow library in your own environment. ML Engine allows you to focus on the creativity of your solution and leave the infrastructure to us. Then when it's time to train those models, upload them to the cloud where ML Engine can do much faster and much at a much larger scale. Finally, deploy the result anywhere from your own premise to a mobile device where it can put its training to use to solve real-world problems. But even with the compute power in the world, AI remains among the most complex field in computer science. And that's still a serious obstacle for many enterprise and customers. For developers not quite ready to build their own models, the easiest way to put AI to use today is through one of the APIs Google has provided to deliver fully trained machine learning models to tackle common problems. These APIs are like a switch that immediately activate an intelligent component in any application, allowing it to understand speech and photos or translate text or parse natural language. But there's a lot more to Google's depth and breadth of AI technology. At Google, we have numerous research teams housing an enormous amount of ongoing AI research, spanning many areas of AI and machine learning. Our researchers are some of the most prolific authors of scientific papers at top AI journals and conferences, and our teams are frequent winners of best papers and AI competitions. And the results of this work are quickly turned into products and services that we want to deliver to our customers. So I'm especially eager to announce some of the latest products of that effort. The Vision API has been under steady development and features some significant new capabilities. First is an expansion of the API's metadata to recognize millions of entities from Google's knowledge graph for images that are available on the web. We're now using the same metadata that powers the entire Google image search. Second, an enhanced optical character recognition capable of retrieving text from images of text-heavy documents such as legal contracts and other complex paperwork. But the world of pixels goes beyond just pictures. In fact, videos are among the most prevalent form of internet data. YouTube alone sees hundreds of hours of videos uploaded every minute. Understanding the rich content of videos has been a tremendous technology challenge for many years. In fact, many of us computer vision researchers we've often considered videos the dark matter of the digital universe. Today, I'm very excited to announce an entirely new API powered by Machine Intelligence, the Video Intelligence API. I'd like to introduce my colleague, Sarah Robinson, to demonstrate this API in more details. Sarah. Thank you, Fei-Fei. Thank you. So the best way to experience the Video APIs through a live demo and let's start by taking this video of a Super Bowl commercial for Google Home. And I'm going to play the first few seconds. We can see that it starts with a mountain landscape. Then we see a house, a city street, then it goes to a dog and a garage. So lots of scene changes happening in this video. And if we were to manually categorize what was happening throughout it, we need to watch the entire thing and write down what was happening in every scene. Luckily, the Video API takes care of this for us, which is a single REST API request. So it tells us two things. One at a high level. It tells us what is this video about. And then it also tells us, at a more granular level, what labels it finds in the video in each scene. So if we scroll down here, we can see it identifies a dog and it can tell us exactly where in the video that dog appears. It also identifies, at the end of the video, there's a birthday cake. And if we scroll down a bit more, we can see that not only does it know it's a dog, it knows what type of dog it is, that it's a dashing. And if we scroll down through the rest of the labels, we can see that it also successfully identifies that mountain pass scene from the beginning. So this is what the API can do with one video, but you likely have more than one video that you wanna analyze. So let's take a look at how a company might use the Video Intelligence API. A media publisher could have hundreds of petabytes of video data sitting in storage buckets. And one common thing they might wanna do is create a highlight reel, focus on a specific type of content, or search their large library for a specific entity. So let's see how we would use the Video Intelligence API to search a large library of videos, given all this metadata that we get back from it. So we've got a lot of videos here. And let's say this media publisher has hours of sports video, but they only wanna find the content relevant to baseball. So let's go ahead and search our library here for baseball videos. And we can see that not only does it show us which videos have baseball, it tells us exactly when in those videos baseball appears. My favorite example is this one. We have this video which only has a tiny bit about baseball but it's able to identify that clip for us. Whereas if we were to manually do this, we'd have to watch the whole video looking for that specific scene. So if we click on this scene, we can see that this is from last year's year in search video when the Cubs won the World Series. So let's do one more search. I live on the East Coast where it's pretty cold right now. I've heard there's been a ton of rain in SF this past year. I think we can all agree it'd be pretty nice to be on a beach right now. And while machine learning can't take us there, it can do the next best thing and find all the beach clips in our video library for us. So let's search for our beach videos and then we can click to all of our beach clips in the videos below. So as you saw through this demo, the Video Intelligence API makes it easy to quickly and easily understand a large library of video content, something that was almost impossible just a few months ago. Tasks that used to take hours now take seconds with the Video Intelligence API and I'm excited to make it available to all of you today. Thank you. Thank you, Sarah. So I'm so excited to see this as a computer vision researcher. I've seen my field working on videos, understanding for decades. And now finally we're beginning to shine light onto the dark matter of the digital universe and provide values to our customers who can use it to harness the enormous amount of information embedded in these videos. Now, let's go on. Data is the next piece of the democratization puzzle. Just as we learned through a lifetime of exposure to the world to gain our human intelligence, AI requires a huge amount of data to develop its own insight. But these data sets are among the steepest barriers to overcome. I have a lot of personal experience of this having led the development of the ImageNet data set which provides over 15 million label images to the machine vision community. Many of you are now familiar with the history after ImageNet. In 2012, it became one of the most important enablers of the deep learning revolution. But to this day, it's still one of the most used training data sets and benchmarks of deep learning algorithms. While the results of ImageNet have been incredible, the long difficult journey of putting it together was a tremendous testament of how great the challenge still remains. What we need is a more scalable and effective way to democratize data to more data scientists, machine learning developers, domain experts and eventually to our business. That's why I'm thrilled to make the next announcement, Google Cloud's acquisition of Kaggle. Years led by co-founders Anthony Goldblum and Ben Hamner, the Kaggle team has been building an unprecedented community of over 850,000 data scientists hosting contests and making new data sets publicly available. By merging with Google Cloud Platform, we're giving this community direct access to the most advanced machine learning environment as well as providing a direct path to market their models. Working together with Kaggle, we're empowering the largest concentration of machine learning talent in the world. In fact, Kaggle is already partnering with Google Cloud and research to host the largest video understanding competition called the YouTube 8 million video understanding challenge. So speaking of talent and expertise, we've also been extremely committed to help our partners and customers to develop more machine learning and AI expertise at the levels they need. At Google, we've always made significant investment in research. Every year, Google gives large grants to over 250 academic research projects worldwide, supports dozens of PhD students and hosts thousands of interns. Moreover, the Google Brain Residency Program recognizes that expertise in AI will be an increasingly important resource in years ahead and is taking steps to find, educate and empower the future leaders of this field. At Google Cloud, in parallel with all these efforts, we're equally committed to using our expertise to deliver real results right now to our customers. The Advanced Solution Lab allows customers with more ambitious goals to partner directly with Google to solve complex AI problems. Let's take the insurance company USAA as a recent example. Many of their engineers were well-versed in data science and some even had a background in machine learning, but they needed help to build a true foundation of expertise. To do this, a team of USAA developers came to Google's Advanced Solution Lab where they learned directly from our own machine learning engineers and experts. That team now is hard at work putting their new skills to use with additional teams being trained in the same way. So I think the most meaningful technologies are the ones that transform a precious resource into something that can benefit everyone. The printing press helped literacy expand beyond the privileged, made books so affordable that they could fill the shelves in the homes and libraries all over the world. The electric grid deliver power to the entire communities turning heat and light from luxuries to staples of everyday life. The mass production of the Industrial Revolution meant that artisanal objects that were once prohibitively expensive could now enrich the lives of hundreds and millions of people. And of course, the internet has made everything from newspaper to university courses so easy to share that they can reach a worldwide audience overnight, often for free. What these examples have in common is the transformation from exclusivity to ubiquity. I believe AI can deliver this transformation at a scale we've never seen and imagined before to help spread the luxuries of the privileged few to the rest of us at a global scale. This is why I'm inviting everyone in this audience to be part of this, where we at Google Cloud are making the tools available but it's up to you to put them to use. And speaking of AI, it took a few years to convince our next speaker of the power, the sweeping power of AI. So if you're still on this journey, it's really not a shame because you're in very good company. It's my honor and privilege to invite Mr. Eric Schmidt. Hi, Eric. You're welcome. Thank you. The... Fei-Fei said, leave the infrastructure to us. And I don't think anyone could have said it better. Last year when I was here, I said, we will meet you where you are. What I've thought about this year is just get to the cloud now. Just go there now. There's no time to waste anymore. There's lots of reasons. And I think we've talked about them this morning. But imagine a model that goes something like this. You're sitting there and you are building something on a container on your laptop. You get it working and then you just release it to the cloud and it scales infinitely. That's how easy this is now. That's how easy it is for you to do this as an individual programmer. Now, does this matter? Well, it mattered in the last year. Let's think about Pokemon Go. So here's a fantastic product which had 50 times more demand in its first two hours than ever planned in their most optimistic forecasts. And I'm pleased to say that our system expanded, that the container architecture and the use of Kubernetes actually could handle the load. And I'm quite convinced that there was no other way to handle such a global phenomenon. And you sit there and you go, well, I'm not trying to be Pokemon Go. Well, if you could, you'd be pretty happy. Think about the sort of global success. So you might as well plan for global success and infinite demand. And even if you don't hit it, your architecture will be right and your costs will be lower. Give you another example. Start, well, if it sounds like open source, right? Using Kubernetes, Apache Beam, Spinnaker, using GRPC, Redis, HBase, you're gonna have a blast. It's gonna be a great week, right? Give you another example, consistent with this. We just had the strongest IPO in a long time in tech in Snap and Snapchat. As part of that, when you read the narrative, there are two things that strike you. First was their incredibly fast software development in their app, both on Android and iPhone and in their community, with huge success, new models, new models of interaction, which you all know and many of you use. But the other confusion that's interesting in their process was nobody could figure out how they could do this with so little capital. And the answer is because they used our infrastructure, right, in a deal that's a hugely successful deal for both corporations. So you sit there and you go, well, why would they do that? I mean, after all, had they spent $2 billion in data centers, they'd have $2 billion for the data centers. Yeah, but then they'd be putting $3 billion and $4 billion and $5 billion and $6 billion in, whereas this is the way they can ride the scale and the investment that we have done. So are you not planning to be like Snapchat? Well, I think everyone here who represents the corporation would be happy to be as successful as they are. Makes sense, right? But the interesting insight is in the last year, we've done two things since this conference that I'm particularly proud of. One, Fei-Fei talked about, which is the sort of making machine learning real at a commercial and enterprise grade scale. You saw the demos, you understand the message, I'll talk about that in a minute. But the other thing that we did is we took the Google network, which is vastly better than anything else you've ever seen, all that dark fiber, all that special fiber, all the interconnect, and we made it directly accessible to the programs that you're using. So using Snap as an example, not only can they use our platform infrastructure, but they get access to their global customers through our network by virtue of the way this works. That's a very big deal. So you say, well, I'm only trying to get regional customers. Dream big. Think about having customers in pretty much every country. You get the idea. So what I want you to do is I want you to sort of understand that we can do both great cloud that is great technology in the cloud, but we can also now work with you as a customer and a partner to build a business that actually scales globally in which you make a lot of money, which is ultimately what everybody cares about. So I want you to imagine that, and the good news is that Fei-Fei talked about it, the others have talked about it as well, we have this incredible leadership platform for the things that I think matter for the future. But the truth is that most of our customers are still struggling with the enormous challenge of their existing infrastructure. Most customers say, that's a great speech, but I'm still trying to deal with my non-X86 apps, my AS400 mainframe, all the kind of stuff that they have and many of you work in such companies or run them. So how do you do that? So let's imagine a sales call where I am your salesperson visiting you and we say, what are we going to do? And what I'll tell you, the first thing to do is take your X86 binaries that you have in some data center inside your company and move them to the Google VM. And you go, huh, well it turns out it's cheaper. And if it's cheaper, that frees up some money to do the next step. So the first thing is the easiest one, literally take the existing PCs and the existing PC software that is X86 software specifically and put it under the Google VM. What do you do with the existing systems? You leave them running. Makes perfect sense. That's phase one. What do you do next? What you do is you take that system, now you've got it working half in the cloud, half not. And you say, I'm now carefully going to take the data out of all these systems and put them in a modern scalable managed database, either SQL or non-SQL. How do I do that? Well, we work with you to do that. And then of course over time, you build and move that data to modern end to end services. I'll bet the rest of my professional career that the future of your business is big data and machine learning applied to the business opportunities, customer challenges, and things before you. It's true whether it's the video API and intelligence service we just announced, or more traditional classification, or the new deep learning approaches that are being pioneered at Google and elsewhere. So what happens is, at that point, you say, OK, I've managed to get the data over here, and now I don't know what to do. Well, Google has a lot of resources that we can throw at this. We have customer engineers to help you. We've got an office of the CTO to help you with the technology. We've got solutions architects who actually do a whole business solution for you. We have an advanced solutions lab that actually showcases some of these things. We have professional services that you can work with in the ways that you would imagine. We have people who are called strategic customer engineers who help you build your cloud. And we finally have customer reliability engineering to keep the thing running. So not only, it used to be, we would say, well, we have this incredible technology derived from the way Google operates, but we didn't have a full service product offering. We didn't have all the service. We didn't have all the people who could help you. And you're sitting there going, well, do I really believe you? Well, we have the references now. They are among you, and you've heard some of them today. And what's interesting is that not only do you have to do that, but you're going to need additional help, especially with the legacy systems. So we have system integrator partners, examples that I wrote down, Pythian, Augusto, Gruvnaut, Sata, global systems integrators like Accenture and PwC. So OK, that sounds like a pretty good pitch. The last time anybody looked at this, it takes years to make this transition. So that was another problem that we faced a year ago, and we figured out how to solve that. We're working now to get these conversions to occur in one month, two months, three months, four months. Now, why? Because time is everything. And while you're doing this conversion, to some degree, some of your customer experience software, some of your value added, is sort of being held captive. So another example. We have Lush, which is a cosmetic retailer, moved their point of sale in a month. Ocado rebuilt into the cloud in six hours. We used all of Everdo with three petabytes of data in three months. That's how fast in the model that I'm describing, you can get there. So I think that there was a speed limit. That speed limit was defined by our ability to work with the data that you had and the install base that you had, and moving it into this new model. The new model works, trust me. And if you're doing a new build, you're clearly going to do that anyway, because you're intelligent. The problem was you're stuck in between. You can't get to the new one. You're still stuck on the old one. So I don't know. What is an example? If you have a data as an object, we now have a software mechanism that will take the data, and which we call, by the way, a simple transfer service, that will basically just move it into our cloud structure and just do it all over it. That's how the data gets there so quickly. You built on containers. Again, we can support every model of containers. You're built on Hadoop. We love Hadoop in the sense that it's very similar to the architectures that it was based on, and it's easy to host it inside of our model or use something even more extensive like BigQuery. You like virtual machines, as I said, just move them straight over. So we've made huge strides in terms of engineering investment and sales and customer service investment to scale that. So how do you do this? Well, first place, if you leave the infrastructure to us, we put $30 billion, and I know because I approved it. So it's real, right, into this platform. Please do not attempt to duplicate it. You have better uses of your money. I'd much rather have you take the money that you have and the talent that you have and build on top of this platform to provide that rapid iteration and that rapid extension in the model that I'm describing. What's interesting is that we're continuing to take this stuff and make it stronger. We just launched Spanner. And if you don't know what Spanner is, Spanner is, in my view, a work of art in a computer science sense. It's a way of doing SQL, so it's a proper database, but it's both globally essentially replicated and also globally coherent at a scale of data that has never been seen before. And by the way, we use it, it's how Google works. And by the way, we released it a week ago for the cloud. We have a container builder that does the same thing. So if you think about it, your model is something like this. BigQuery provides petabyte scale analysis on demand. Bigtable provides millions of QPS, Google Cloud Storage for the actual millions of objects. Again, the prices are very, very low. And all of this platform that I'm describing, this get to the cloud now model. And most people, by the way, are still in the moving their x86s into VMs, getting some of the data out of the VMs, putting them into proper databases. So you're still in the sort of getting their phase. Once you have the data, you have an opportunity for real transformation. I think that big data is so powerful that nation states will fight over how much data matters, that he who has the data that can do the analytics in the algorithms that Fei-Fei talked about at the scale that we're talking about will provide huge nation state benefits in terms of global companies and benefits for their citizens and so forth and so on. But this is all basically because once you have to have the data, you have to do one more thing. You have to change from writing programs to instead building programs that learn outcomes. And what machine learning really is, and AI really is, is it's a change of way you program. And over and over again, and we've talked about this to some length already as a company, we've seen because of vision, we have the possibility of drastically reducing the number of deaths in automobile accidents and truck accidents, a big deal, a huge deal. And by the way, accidents and cars are going up right now in America because the drivers appear to be distracted by the very technology that is going into the cars. So we've got to solve that problem, we've got to do it now. That is happening. In healthcare, in pathology, radiology, all of the sort of patient centered issues were not that different from each other and the machine learning and collaborative learning that's now possible really will produce amazing healthcare outcomes. And there's a possibility that the kind of intelligence that is going to be buildable on these platforms will really surpass the kind of insights that the average person in a business is going to have. We're seeing over and over again, people are using big data to do customer analysis, pattern matching analysis and customer targeting that really does produce extra insight. And that extra insight is worth billions and billions of dollars in their marketing programs and knowing it. So let me finish by saying that we're here for real. You're here for real. This is an incredibly serious mission, something I've wanted to do since I joined the company 17 years ago. It's something that I know that the people you've heard with, wherever they started care a great deal about it. The company has both the money, the means and the commitment to pull off a new platform of computation globally for everybody who needs it. And something which allows you to be satisfied that our principles, which are basically about openness and openness access really will allow you both the freedom of choice, you won't be locked in, but also the freedom to innovate. When I look at our partners and I look at the speed with which they can now use their scarce resources, which are always programmers to scale their business solutions, their enterprise solutions, their customer solutions, their video solutions and their marketing solutions, it just warms my heart. It's something we've looked at, we've looked forward to for decades. With that, thank you very much and I'm gonna bring you back to Diane. Thank you. Thank you. Eric is always so wonderful to hear our ex brilliant insights and perspectives. He was one of the original enterprise people way back when. So I wanna just recap what you heard from our customers, our partners. You heard from Disney, they're doing a full lift and shift to Google Cloud. They're using, they're going after machine learning. I like to say bring the magic to the magic. Our SAP partnership so broad, you heard about certifying HANA, you heard about integrating with G Suite, the machine learning partnership and also this very interesting partnership we're doing for a data custodian to help our customers solve the difficult issues around compliance, regulatory security. And then you heard from our joint customer, Colgate, a 211 year old company. You have to keep reinventing yourself to make it that long and they're transforming themselves today with G Suite. And then Verizon moving 150,000 employees to G Suite. Again, going after a cultural transformation and dealing with some really difficult issues around moving to the cloud in their environment. Then we had Home Depot, very progressive forward looking retailer, looking to technology to get the advantage, looking to machine learning to get the advantage, already taking advantage of Google scale to get them through things like Black Friday. Then we had a very interesting look into one of the world's largest banks, HSBC, about how they're going to use Google Cloud and the technology and the machine learning to really streamline their operations and do things people didn't really realize was possible. And then we closed out with eBay. They definitely see Google Cloud as a technology that gives them huge competitive advantage. Very interesting how easily they were able to run their catalog on our cloud and work in a hybrid environment. And then of course we saw that wonderful demo where the chatbot running on Google Cloud was talking to Google Home, giving answers about the price of a camera in their catalog, just terrific stuff. I wanna also, where are all the product announcements? All the product announcements, we have dozens of them. They're tomorrow, when you'll hear from our leaders, you'll hear from Urs Hussle, you'll hear from Brian Stevens, you'll hear from Prabhakar Raghavan around G Suite, you'll hear from Chet Kapoor, the CEO of Apigee, telling you all kinds of new products that we're bringing out and where we're going and quite a bit of vision. Then Friday, it's about developers through and through, it's about open source startups and Sam Ramajee will lead that up. Before I wrap, I wanna really thank all our partners, our precious partners, all 150 of them on the floor. I really look forward to working, running into you on the showcase floor. And personally, I also wanna express my deep appreciation to all the thousands of people in Google Cloud. I mean, you're amazing people, it's why I'm at Google and the hard work you've done over the last year and how smart and excellent your work is, much appreciation. I wanna remind you, we're gonna take a break, but at 12.30, we're gonna have two real internet giants. I was a judge in the first Queen Elizabeth Prize for Engineering, I think it's now in its third year. Vint Cerf and Mark Antreas and we're two of the winners for inventing the internet. Those two haven't skipped a beat, they're actively leading and defining our world in technology and as an ongoing basis, they'll be here for a discussion with Quentin Hardy. So, with that, I'm really wrapping it up. It's been a long keynote. Thank you for staying, we really appreciate it. And hey, everybody, enjoy the show. Thank you very much. Thank you.