>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 >>sponsored by >>Intel and AWS. Yeah, Welcome back to the cubes. Ongoing coverage of aws reinvent virtual the Cuba has gone virtual to. We're gonna talk about machine intelligence, cloud and transformation in healthcare. An industry that is rapidly evolving and reinventing itself to provide better quality care faster and more accurate diagnoses. And this has to be done at lower cost. And with me to talk about This is Dr Taha. Awesome. Who? Who is the director of machine learning at Amazon Web services? Doctor, good to see you again. Thanks for coming on. >>Thank you so much. Good to see Dave. >>Yeah, last time we talked, I think it was a couple of years ago. We remember we were talking about Amazon. Comprehend medical. And, of course, you've been so called so called raising the bar, so to speak, Over the past 24 months, you made some announcements today, including Amazon Health Lake, which we're gonna talk about. Tell us about it. >>Well, we're really excited about eso our customers. Amazon Half Lake, a new hip eligible service for health care providers health insurance companies and pharmaceutical companies to securely store, transform Aquarian, analyze health data in the cloud at petabytes scale, a Amazon health lake uses machine learning models trained to automatically understand context and extract meaningful data from medical data from raw, disparate information such as medications, procedures, Um, and diagnosis. Um Therefore, revolutionizing a process that was traditionally manual Arab prone and highly costly requires a lot of expertise on teams within these organizations. What healthcare Catholic does is it tags and indexes every piece of information on then structure in an open standard. The fire standard, or that's the fast healthcare interoperability resource, is in order to provide a complete view 360 degree view of each patient in a consistent way so you'll be able to curry and share that data securely. It also integrates with other machine learning services and a lot of services that AWS offers, such as Amazon Quicksight or Amazon sage maker. In order to visualize and understand the relationships in the data identify trends, Andi also make predictions. The other great benefit is since the Amazon health lake automatically structures all the health care organizations data into open standard. The fire industry format. The information now can be easily and securely shared between systems. Health systems onda with third party applications. So eso providers, health care providers will will enjoy the ability to collaborate more effectively with each other but also allowing patients and federal access to their medical information. >>I think now, so one of things that people are gonna ask is Okay, wait a minute. Hip eligible Is that like cable ready or HD ready? And but people need to understand that it's a shared responsibility. But you can't come out of the box and be HIPPA compliant there a number of things and processes, uh, that that your customer has to do. Maybe you could explain that a little >>bit. Absolutely. I mean, in practice a little bit. This is a very, very important thing, and and it's something that we really fully baked into the service and how we built Also the service, especially dealing with health care information. First off, AWS, as you know, is vigilant about customers, privacy and security. It is job zero for us. Your data and Health Lake is secure, compliant, and auditable data version is enabled to protect um, the data against any accident collision, for example, and per fire sophistication. If you are to delete one piece of data, it will be version it will be on Lee. Hidden from analysis is a result not believed from the service. So your dad is always encrypted on by using your own customer. Manage key in a keys in a single tenant. Architectures is another added benefit to provide the additional level of protection when the data is access and search for example, every time inquiry a value, for example, someone's glucose level if the data is encrypted and decrypted and and and and so on and so forth. So, additionally, this system in a single tenant architectures so that that way the data, uh, the key. The same key is not shared across multiple customers. So you're saying full ownership and control of your data along with the ability to encrypt, protect move, deleted in alignment with organization, security and policies. Now a little bit about the hip eligibility. It's a term that AWS uses eso for customers storing protected health information or P h. I A. DBS by its business associate agreement on also Business Associate amendment require customers to encrypt data addressed in transit when they're using area services. There are over 100 services today. They're hip eligible, including the Amazon. Health like this is very important, especially for, uh enabling discovered entities and their business associates subject to HIPAA regulations, and is be able to kind of and this shared model between what a the best protection and services and how it can process and store and managed ph I. But there's additional level of compliance is required on the on the customer side, um, about you know, anywhere from physical security thio how each application can be built, which is no different than how you manage it. For example, today in your own that data center, what not? But this is why many cats, growing number of health care providers, um, players as well as I, because professionals are using AWS utility based cloud services today to process, store and transmit pH. I. >>So tell us more about who was gonna benefit from this new capability, what types of organizations and would be some of the outcomes for for for patients, >>absolutely every healthcare provider today, or a payer like a health insurance company or a life. Science companies such as Pharma Company is just trying to solve the problem of organizing instruction their data. Because if you do, you make better sense of this information from better patient support decisions. Design better clinical trials, operate more efficiently, understand population health trends on be able them to share that that security. It's really all starts with making sense of that of that data. And those are the ultimate customers that we're trying to empower with the Amazon Amazon Health Lake. Um, >>well, And of course, there's downstream benefits for the patient. Absolutely. That's ultimately what we're trying to get to. I mean, absolutely. I mean, I set up front. I mean, it's it's everybody you know, feels the pain of high health care costs. A lot of times you're trying to get to see a doctor, and it it takes a long time now, especially with with covitz so and much of this, oftentimes it's even hard to get access to your own data s. So I think you're really trying to attack that problem. Aren't >>you absolutely give you a couple of examples like I mean, today, the most widely used clinical models, uh, in practice to predict. Let's say someone's disease risk lack personalization. Um, it's you and I can be lumped in the same in the same bucket, for example, based on a few attributes that common, UM, data elements or data points, which is problematic also because the resulting models produce are imprecise. However, if you look at an individual's medical records, for example, you know a diabetic type two diabetic patients there, if you look at the entire history and from all this information coming to them, whether it's doctor knows medication dosages, which line of treatment the second line treatment, uh, continuous monitoring of glucose and that sort of thing is over hundreds. You know, there are hundreds of thousands of data points in their entire medical history, but none of this is used today. At the point of care on. You want all this information to be organized, aggregated, structured in a way that you will be able to build even better models easily queried this information, aan den observed the health of the individual in comparison with the rest of the population because at that point you'll be able to make those personalized decisions and then also for patient engagement with the health lake ability to now emit data back on dshea air securely the a p i s that conform to the fire standard. So third party applications can be built also, um, Thio provide the access whether that's for analytics or digital health application, for example, a patient accident, that information all that is very, very, very important. Because ultimately you wanna, um, get at better care of these these populations better. In Roma, clinical trials reduce duplicative tests and waste and health care systems. All that comes when you have your entire information available in a way that structured and normalize on be able to Korean and analyze andan the seamless integration between the health lake and the arrest of the services like Amazon sage maker. You can really start to understand relationships and meaning of the information, build better, better decision support models and predictive models at the individual on a population level. >>Yeah, right. You talked about all this data that's not not really used on. It's because it's not accessible. I presume it's not in in one place that somebody can analyze its not standardized. It's not normalized. Uh, is that >>right, that is the biggest. That is the biggest challenge for every healthcare provider, pair or life science organization today. If you look at this data, it's difficult to work with. Medical health. Data is really different that I siloed spread out across multiple systems, and it's sort of not incompatible formats. If you look at the last decade, I mean, one of the greatest things is we witnessed a great transformation healthcare towards digitization of the record. But your data is scattered across many of these systems anywhere from found your family history, the clinical observation, diagnosis and treatment. When you see the vast majority of that data is contained in unstructured medical records like Dr Notes P. D efs of insurance, um, of laboratory reports or insurance claims and forms with the With With Covad, we've seen in quite a bit of uptake of digital sort of, um uh, delivery of care such as telemedicine and recorded audios and videos, X rays and images, uh, the large propagation of digital health, APS and and digital assistances and on and wearables and as well as these sort of monitors like glucose, monitor or not, data come in all shapes and form and form and start across all these things. It's a huge heavy lift for any health care organization to be able to aggregate normalized stored securely on. Then also be able to kind of analyze this information and structure in a way that zizi to scale. Um uh, with regards, Thio, the kind of problems that you're going after. >>Well, Dr Cox, who We have to leave it there. Thank you so much. I have been saying for years in the Cube. When is it? That machine's gonna be able to make it make better diagnoses than doctors. Maybe that's the wrong question. Maybe it's machines helping doctors make faster and more accurate diagnoses and lowering our costs. Thanks so much for coming. >>Thank you very much. Appreciate it. Thank you. >>Thank you for watching everybody keep it right there. This is Dave Volonte. We'll be back with more coverage of aws reinvent 2020. You virtual right after this short break

>> fly from San Francisco. It's the Cube covering Google Club next nineteen Tio by Google Cloud and its ecosystem partners. >> Hey, welcome back. Everyone live here in San Francisco, California is the cubes coverage of Google Cloud Next twenty nineteen star Third day of three days of wall to wall coverage. John for a maiko stupid demon devil on things out around the floor. Getting stories, getting scoops. Of course, we're here with Sadeer has Bay. Who's the director of product management? Google Cloud. So great to see you again. Go on Back on last year, I'LL see Big Query was a big product that we love. We thought the fifty many times about database with geek out on the databases. But it's not just about the databases. We talked about this yesterday, all morning on our kickoff. There is going to be database explosion everywhere. Okay, it's not. There's no one database anymore. It's a lot of databases, so that means data in whatever database format document relational, Unstructured. What you want to call it is gonna be coming into analytical tools. Yes, this's really important. It's also complex. Yeah, these be made easier. You guys have made their seers announcements Let's get to the hard news. What's the big news from your group around Big Queria Mail Auto ml Some of the news share >> the news. Perfect, I think not. Just databases are growing, but also applications. There's an explosion off different applications. Every organization is using hundreds of them, right from sales force to work today. So many of them, and so having a centralized place where you can bring all the data together, analyze it and make decisions. It's critical. So in that realm to break the data silos, we have announced a few important things that they went. One is clouded effusion, making it easy for customers to bring in data from different sources on Prum Ices in Cloud so that you can go out and as you bring the data and transform and visually just go out and move the data into Big query for for analysis, the whole idea is the board and have Dragon drop called free environment for customers to easily bring daytime. So we have, like, you know, a lot of customers, just bringing in all the data from their compromise. The system's oracle, my sequel whatever and then moving that into into big Query as they analyze. So that's one big thing. Super excited about it. A lot of attraction, lot of good feedback from our customers that they went. The second thing is Big Query, which is our Cloud Skill Data warehouse. We have customers from few terabytes to hundreds of terabytes with it. Way also have an inline experience for customers, like a data analyst who want to analyze data, Let's say from sales force work, they are from some other tools like that if you want to do that. Three. I have made hundred less connectors to all these different sense applications available to our partners. Like five Grand Super Metrics in Macquarie five four Barrel Box out of the box for two five clicks, >> you'LL be able to cloud but not above, but I guess that's afraid. But it's important. Connectors. Integration points are critical table stakes. Now you guys are making that a table stakes, not an ad on service the paid. You >> just basically go in and do five clicks. You can get the data, and you can use one of the partners connectors for making all the decisions. And also that's there. and we also announced Migration Service to migrate from candidate that shift those things. So just making it easy to get data into recipe so that you can unlock the value of the data is the first thing >> this has become the big story here. From the Cube standpoint on DH student, I've been talking about day all week. Data migration has been a pain in the butt, and it's critical linchpin that some say it could be the tell sign of how well Google Cloud will do in the Enterprise because it's not an easy solution. It's not just, oh, just move stuff over And the prizes have unique requirements. There's all kinds of governance, all kinds of weird deal things going on. So how are you guys making it easy? I guess that's the question. How you gonna make migrating in good for the enterprise? >> I think the one thing I'll tell you just before I had a customer tell me one pain. You have the best highways, but you're on grams to the highway. Is that a challenge? Can you pick that on? I'm like here are afraid. Analogy. Yeah, it's great. And so last year or so we have been focused on making the migration really easy for customers. We know a lot of customers want to move to cloud. And as they moved to cloud, we want to make sure that it's easy drag, drop, click and go for migration. So we're making that >> holding the on ramps basically get to get the data in the big challenge. What's the big learnings? What's the big accomplishment? >> I think the biggest thing has Bean in past. People have to write a lot ofthe court to go ahead and do these kind of activities. Now it is becoming Click and go, make it really cold free environment for customers. Make it highly reliable. And so that's one area. But that's just the first part of the process, right? What customers want is not just to get data into cloud into the query. They want to go out and get a lot of value out off it. And within that context, what we have done is way made some announcements and, uh, in the in that area. One big thing is the B I engine, because he'd be a engine. It's basically an acceleration on top of the query you get, like subsequently, agency response times for interactive dash boarding, interactive now reporting. So that's their butt in with that. What we're also announced is connected sheets, so connected sheets is basically going to give you spreadsheet experience on top ofthe big credit data sets. You can analyze two hundred ten billion rose off data and macquarie directly with drag drop weakened upriver tables again. Do visualizations customers love spreadsheets in general? >> Yeah, City area. I'm glad you brought it out. We run a lot of our business on sheep's way of so many of the pieces there and write if those the highways, we're using our data. You know what's the first step out of the starts? What are some of the big use cases that you see with that? >> So I think Andy, she is a good example of so air. Isha has a lot of their users operational users. You needed to have access to data on DH, so they basically first challenge was they really have ah subsequently agency so that they can actually do interact with access to the data and also be an engine is helping with that. They used their story on top. Off half now Big Quit it, Gordon. Make it accessible. Be engine will vote with all the other partner tooling too. But on the other side, they also needed to have spread sheet like really complex analysis of the business that they can improve operation. Last year we announced they have saved almost five to ten percent on operational costs, and in the airline, that's pretty massive. So basically they were able to go out and use our connective sheets experience. They have bean early Alfa customer to go out and use it to go in and analyse the business, optimize it and also so that's what customers are able to do with connected sheets. Take massive amounts of data off the business and analyze it and make better. How >> do we use that? So, for a cost, pretend way want to be a customer? We have so many tweets and data points from our media. I think fifty million people are in our kind of Twitter network that we've thought indexed over the years I tried to download on the C S V. It's horrible. So we use sheets, but also this They've had limitations on the han that client. So do we just go to Big Query? How would we work >> that you can use data fusion with you? Clicks move later into Big Query wants you now have it in big query in sheets. You will have an option from data connectors Macquarie. And once you go there, if you're in extended al far, you should get infection. Alfa. And then when you click on that, it will allow you to pick any table in bickering. And once you link the sheets to be query table, it's literally the spreadsheet is a >> run in >> front and got through the whole big query. So when you're doing a favour tables when you're saying Hey, aggregate, by this and all, it actually is internally calling big credit to do those activities. So you remove the barrier off doing something in the in the presentation layer and move that to the engine that actually can do the lot skill. >> Is this shipping? Now you mention it. Extended beta. What's the product? >> It's an extended out far for connected sheets. Okay, so it's like we're working with few customers early on board and >> make sure guys doing lighthouse accounts classic classic Early. >> If customers are already G sweet customer, we would love to get get >> more criteria on the connected sheets of Alfa sending bait after Now What's what's the criteria? >> I think nothing. If customers are ready to go ahead and give us feedback, that's what we care of. Okay, so you want to start with, like, twenty twenty five customers and then expanded over this year and expand it, >> maybe making available to people watching. Let us let us know what the hell what do they go? >> Throw it to me and then I can go with that. Folks, >> sit here. One of the other announcements saw this week I'm curious. How it connects into your pieces is a lot of the open source databases and Google offering those service maybe even expand as because we know, as John said in the open there, the proliferation of databases is only gonna increase. >> I think open source way announced lot of partnerships on the databases. Customers need different types of operational databases on. This is a great, great opportunity for us to partner with some of our partners and providing that, and it's not just data basis. We also announced announced Partnership with Confident. I've been working with the confident team for last one place here, working on the relationship, making sure our customers haven't. I believe customers should always have choice. And we have our native service with Cloud pops up. A lot of customers liked after they're familiar with CAFTA. So with our relationship with Khan fluent and what we announced now, customers will get native experience with CAFTA on Jessie P. I'm looking forward to that, making sure our customers are happy and especially in the streaming analytic space where you can get real time streams of data you want to be, Oh, directly analytics on top of it. That is a really high value add for us, So that's great. And so so that's the That's what I'm looking forward to his customers being able to go out and use all of these open source databases as well as messaging systems to go ahead and and do newer scenarios for with us. >> Okay, so you got big Big query. ML was announced in G. A big query also has auto support Auto ml tables. What does that mean? What's going what's going on today? >> So we announced aquarium L at Kew Blast next invader. So we're going Ta be that because PML is basically a sequel interface to creating machine learning models at scale. So if you have all your data and query, you can write two lines ofthe sequel and go ahead and create a model tow with, Let's say, clustering. We announced plastering. Now we announced Matrix factory ization. One great example I will give you is booking dot com booking dot com, one of the largest travel portals in the in the world. They have a challenge where all the hotel rooms have different kinds off criteria which says they have a TV. I have a ll the different things available and their problem was data quality. There was a lot of challenges with the quality of data they were getting. They were able to use clustering algorithm in sequel in Macquarie so that they could say, Hey, what are the anomalies in this data? Sets and identify their hotel rooms. That would say I'm a satellite TV, but no TV available. So those claims direct Lansing stuff. They were easily able to do with a data analyst sequel experience so that's that. >> That's a great example of automation. Yeah, humans would have to come in, clean the data that manually and or write scripts, >> so that's there. But on the other side, we also have, Ah, amazing technology in Auto Emma. So we had our primal table are normal vision off thermal available for customers to use on different technologies. But we realized a lot of problems in enterprise. Customers are structured data problems, So I have attained equerry. I want to be able to go in and use the same technology like neural networks. It will create models on top of that data. So with auto Emel tables, what we're enabling is customers can literally go in auto Emel Table Portal say, Here is a big query table. I want to be able to go out and create a model on. Here is the column that I want to predict from. Based on that data, and just three click a button will create an automated the best model possible. You'LL get really high accuracy with it, and then you will be able to go out and do predictions through an FBI or U can do bulk predictions out and started back into Aquarian also. So that's the whole thing when making machine learning accessible to everyone in the organization. That's our goal on with that, with a better product to exactly it should be in built into the product. >> So we know you've got a lot of great tech. But you also talk to a lot of customers. Wonder if you might have any good, you know, one example toe to really highlight. Thie updates that you >> think booking dot com is a good example. Our scent. Twentieth Century Fox last year shared their experience off how they could do segmentation of customers and target customers based on their past movies, that they're watched and now they could go out and protect. We have customers like News UK. They're doing subscription prediction like which customers are more likely to subscribe to their newspapers. Which ones are trying may turn out s o those He examples off how machine learning is helping customers like basically to go out and target better customers and make better decisions. >> So, do you talk about the ecosystem? Because one of things we were riffing on yesterday and I was giving a monologue, Dave, about we had a little argument, but I was saying that the old way was a lot of people are seeing an opportunity to make more margin as a system integrated or global less I, for instance. So if you're in the ecosystem dealing with Google, there's a margin opportunity because you guys lower the cost and increase the capability on the analytic side. Mention streaming analytics. So there's a business model moneymaking opportunity for partners that have to be kind of figured out. >> I was the >> equation there. Can you share that? Because there's actually an opportunity, because if you don't spend a lot of time analyzing the content from the data, talk aboutthe >> money means that there's a huge opportunity that, like global system integrators, to come in and help our customers. I think the big challenges more than the margin, there is lot of value in data that customers can get out off. There's a lot of interesting insights, not a good decision making they can do, and a lot of customers do need help in ramping up and making sure they can get value out of that. And it's a great opportunity for our global Asai partners and I've been meeting a lot of them at the show to come in and help organizations accelerate the whole process off, getting insights from from their data, making better decisions, do no more machine learning, leverage all of that. And I think there is a huge opportunity for them to come in. Help accelerate. What's the >> play about what some other low hanging fruit opportunities I'LL see that on ramping or the data ingestion is one >> one loving fruit? Yes, I think no hanging is just moving migration. Earlier, he said. Break the data silos. Get the data into DCP. There's a huge opportunity for customers to be like, you know, get a lot of value. By that migration is a huge opportunity. A lot of customers want to move to cloud, then they don't want to invest more and more and infrastructure on them so that they can begin level Is the benefits off loud? And I think helping customers my great migrations is going to be a huge Obviously, we actually announced the migration program also like a weak back also way. We will give training credits to our customers. We will fund some of the initial input, initial investment and migration activities without a side partners and all, so that that should help there. So I think that's one area. And the second area, I would say, is once the data is in the platform getting value out ofit with aquarium in auto ml, how do you help us? It must be done. I think that would be a huge opportunity. >> So you feel good too, dear. But, you know, build an ecosystem. Yeah. You feel good about that? >> Yeah, way feel very strongly about our technology partners, which are like folks like looker like tableau like, uh, talent confluence, tri factor for data prep All of those that partner ecosystem is there great and also the side partner ecosystem but for delivery so that we can provide great service to our customers >> will be given good logos on that slide. I got to say, Try facts and all the other ones were pretty good etcetera. Okay, so what's the top story for you in the show here, besides your crew out on the date aside for your area was a top story. And then generally, in your opinion, what's the most important story here in Google Cloud next. >> I think two things in general. The biggest news, I think, is open source partnership that we have announced. I'm looking forward to that. It's a great thing. It's a good thing both for the organizations as well as us on DH. Then generally, you'LL see lot off examples of enterprise customers betting on us from HSBC ends at bank that was there with mean in the session. They talked about how they're getting value out ofthe outof our data platform in general, it's amazing to see a lot more enterprises adopting and coming here telling their stories, sharing it with force. >> Okay, thanks so much for joining us. Look, you appreciate it. Good to see you again. Congratulations. Perfect fusion ingesting on ramps into the into the superhighway of Big Query Big engine. They're they're large scale data. Whereas I'm Jeffers dipping them in. We'LL stay with you for more coverage after this short break