>> Announcer: Live from San Jose, California, it's theCUBE, covering Innovating to Fuel the Next Decade of Big Data, brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at Western Digital at their global headquarters in San Jose, California, it's the Almaden campus. This campus has a long history of innovation, and we're excited to be here, and probably have the smartest person in the building, if not the county, area code and zip code. I love to embarrass here, Janet George, she is the Fellow and Chief Data Scientist for Western Digital. We saw you at Women in Data Science, you were just at Grace Hopper, you're everywhere and get to get a chance to sit down again. >> Thank you Jeff, I appreciate it very much. >> So as a data scientist, today's announcement about MAMR, how does that make you feel, why is this exciting, how is this going to make you be more successful in your job and more importantly, the areas in which you study? >> So today's announcement is actually a breakthrough announcement, both in the field of machine learning and AI, because we've been on this data journey, and we have been very selectively storing data on our storage devices, and the selection is actually coming from the preconstructed queries that we do with business data, and now we no longer have to preconstruct these queries. We can store the data at scale in raw form. We don't even have to worry about the format or the schema of the data. We can look at the schema dynamically as the data grows within the storage and within the applications. >> Right, cause there's been two things, right. Before data was bad 'cause it was expensive to store >> Yes. >> Now suddenly we want to store it 'cause we know data is good, but even then, it still can be expensive, but you know, we've got this concept of data lakes and data swamps and data all kind of oceans, pick your favorite metaphor, but we want the data 'cause we're not really sure what we're going to do with it, and I think what's interesting that you said earlier today, is it was schema on write, then we evolved to schema on read, which was all the rage at Hadoop Summit a couple years ago, but you're talking about the whole next generation, which is an evolving dynamic schema >> Exactly. >> Based whatever happens to drive that query at the time. >> Exactly, exactly. So as we go through this journey, we are now getting independent of schema, we are decoupled from schema, and what we are finding out is we can capture data at its raw form, and we can do the learning at the raw form without human interference, in terms of transformation of the data and assigning a schema to that data. We got to understand the fidelity of the data, but we can train at scale from that data. So with massive amounts of training, the models already know to train itself from raw data. So now we are only talking about incremental learning, as the train model goes out into the field in production, and actually performs, now we are talking about how does the model learn, and this is where fast data plays a very big role. >> So that's interesting, 'cause you talked about that also earlier in your part of the presentation, kind of the fast data versus big data, which kind of maps the flash versus hard drive, and the two are not, it's not either or, but it's really both, because within the storage of the big data, you build the base foundations of the models, and then you can adapt, learn and grow, change with the fast data, with the streaming data on the front end, >> Exactly >> It's a whole new world. >> Exactly, so the fast data actually helps us after the training phase, right, and these are evolving architectures. This is part of your journey. As you come through the big data journey you experience this. But for fast data, what we are seeing is, these architectures like Lambda and Kappa are evolving, and especially the Lambda architecture is very interesting, because it allows for batch processing of historical data, and then it allows for what we call a high latency layer or a speed layer, where this data can then be promoted up the stack for serving purposes. And then Kappa architecture's where the data is being streamed near real time, bounded and unbounded streams of data. So this is again very important when we build machine learning and AI applications, because evolution is happening on the fly, learning is happening on the fly. Also, if you think about the learning, we are mimicking more and more on how humans learn. We don't really learn with very large chunks of data all at once, right? That's important for initially model training and model learning, but on a regular basis, we are learning with small chunks of data that are streamed to us near real time. >> Right, learning on the Delta. >> Learning on the Delta. >> So what is the bound versus the unbound? Unpack that a little bit. What does that mean? >> So what is bounded is basically saying, hey we are going to get certain amounts of data, so you're sizing the data for example. Unbounded is infinite streams of data coming to you. And so if your architecture can absorb infinite streams of data, like for example, the sensors constantly transmitting data to you, right? At that point you're not worried about whether you can store that data, you're simply worried about the fidelity of that data. But bounded would be saying, I'm going to send the data in chunks. You could also do bounded where you basically say, I'm going to pre-process the data a little bit just to see if the data's healthy, or if there is signal in the data. You don't want to find that out later as you're training, right? You're trying to figure that out up front. >> But it's funny, everything is ultimately bounded, it just depends on how you define the unit of time, right, 'cause you take it down to infinite zero, everything is frozen. But I love the example of the autonomous cars. We were at the event with, just talking about navigation just for autonomous cars. Goldman Sachs says it's going to be a seven billion dollar industry, and the great example that you used of the two systems working well together, 'cause is it the car centers or is it the map? >> Janet: That's right. >> And he says, well you know, you want to use the map, and the data from the map as much as you can to set the stage for the car driving down the road to give it some level of intelligence, but if today we happen to be paving lane number two on 101, and there's cones, now it's the real time data that's going to train the system. But the two have to work together, and the two are not autonomous and really can't work independent of each other. >> Yes. >> Pretty interesting. >> It makes perfect sense, right. And why it makes perfect sense is because first the autonomous cars have to learn to drive. Then the autonomous cars have to become an experienced driver. And the experience cannot be learned. It comes on the road. So one of the things I was watching was how insurance companies were doing testing on these cars, and they had a human, a human driving a car, and then an autonomous car. And the autonomous car, with the sensors, were predicting the behavior, every permutation and combination of how a bicycle would react to that car. It was almost predicting what the human on the bicycle would do, like jump in front of the car, and it got it right 80% of the cases. But a human driving a car, we're not sure how the bicycle is going to perform. We don't have peripheral vision, and we can't predict how the bicycle is going to perform, so we get it wrong. Now, we can't transmit that knowledge. If I'm a driver and I just encountered a bicycle, I can't transmit that knowledge to you. But a driverless car can learn, it can predict the behavior of the bicycle, and then it can transfer that information to a fleet of cars. So it's very powerful in where the learning can scale. >> Such a big part of the autonomous vehicle story that most people don't understand, that not only is the car driving down the road, but it's constantly measuring and modeling everything that's happening around it, including bikes, including pedestrians, including everything else, and whether it gets in a crash or not, it's still gathering that data and building the model and advancing the models, and I think that's, you know, people just don't talk about that enough. I want follow up on another topic. So we were both at Grace Hopper last week, which is a phenomenal experience, if you haven't been, go. Ill just leave it at that. But Dr. Fei-Fei Li gave one of the keynotes, and she made a really deep statement at the end of her keynote, and we were both talking about it before we turned the cameras on, which is, there's no question that AI is going to change the world, and it's changing the world today. The real question is, who are the people that are going to build the algorithms that train the AI? So you sit in your position here, with the power, both in the data and the tools and the compute that are available today, and this brand new world of AI and ML. How do you think about that? How does that make you feel about the opportunity to define the systems that drive the cars, et cetera. >> I think not just the diversity in data, but the diversity in the representation of that data are equally powerful. We need both. Because we cannot tackle diverse data, diverse experiences with only a single representation. We need multiple representation to be able to tackle that data. And this is how we will overcome bias of every sort. So it's not the question of who is going to build the AI models, it is a question of who is going to build the models, but not the question of will the AI models be built, because the AI models are already being built, but some of the models have biases into it from any kind of lack of representation. Like who's building the model, right? So I think it's very important. I think we have a powerful moment in history to change that, to make real impact. >> Because the trick is we all have bias. You can't do anything about it. We grew up in the world in which we grew up, we saw what we saw, we went to our schools, we had our family relationships et cetera. So everyone is locked into who they are. That's not the problem. The problem is the acceptance of bring in some other, (chuckles) and the combination will provide better outcomes, it's a proven scientific fact. >> I very much agree with that. I also think that having the freedom, having the choice to hear another person's conditioning, another person's experiences is very powerful, because that enriches our own experiences. Even if we are constrained, even if we are like that storage that has been structured and processed, we know that there's this other storage, and we can figure out how to get the freedom between the two point of views, right? And we have the freedom to choose. So that's very, very powerful, just having that freedom. >> So as we get ready to turn the calendar on 2017, which is hard to imagine it's true, it is. You look to 2018, what are some of your personal and professional priorities, what are you looking forward to, what are you working on, what's top of mind for Janet George? >> So right now I'm thinking about genetic algorithms, genetic machine learning algorithms. This has been around for a while, but I'll tell you where the power of genetic algorithms is, especially when you're creating powerful new technology memory cell. So when you start out trying to create a new technology memory cell, you have materials, material deformations, you have process, you have hundred permutation combination, and the genetic algorithms, we can quickly assign a cause function, and we can kill all the survival of the fittest, all that won't fit we can kill, arriving to the fastest, quickest new technology node, and then from there, we can scale that in mass production. So we can use these survival of the fittest mechanisms that evolution has used for a long period of time. So this is biology inspired. And using a cause function we can figure out how to get the best of every process, every technology, all the coupling effects, all the master effects of introducing a program voltage on a particular cell, reducing the program voltage on a particular cell, resetting and setting, and the neighboring effects, we can pull all that together, so 600, 700 permutation combination that we've been struggling on and not trying to figure out how to quickly narrow down to that perfect cell, which is the new technology node that we can then scale out into tens of millions of vehicles, right? >> Right, you're going to have to >> Getting to that spot. >> You're going to have to get me on the whiteboard on that one, Janet. That is amazing. Smart lady. >> Thank you. >> Thanks for taking a few minutes out of your time. Always great to catch up, and it was terrific to see you at Grace Hopper as well. >> Thank you, I really appreciate it, I appreciate it very much. >> All right, Janet George, I'm Jeff Frick. You are watching theCUBE. We're at Western Digital headquarters at Innovating to Fuel the Next Generation of Big Data. Thanks for watching.

>>Yeah. Hey, everyone, Welcome to the cubes. Continuous coverage of AWS Re invent 2021. I'm Lisa Martin with John Furrier were running one of the industry's largest and most important hybrid tech events with AWS and massive ecosystem of partners. Right now there are two live cube sets to remote sets over 100 guests on the programme and we're pleased to welcome back one of our alum I to talk about the next generation and cloud innovation. Georgia Lisa is joins John to me, the director of product management for EC two edge at A. W S George. Welcome to the programme. >>Glad to be here in person. Thanks Great to be here in person. Awesome to be here in person. Finally, >>one of the things that is very clear is the US flywheel of innovation and there was no slowdown with what's happened in the last 22 months. Amazing announcements, new leadership. We talked a little bit about five g yesterday, but let's talk more about that. Everyone is excited about five g consumers businesses. What's going on? >>So, yeah, I wanted to talk to you today about the new service that we launched called AWS Private. Five g. Essentially, it's a service that allows any AWS customer to build their own private five g network and what we try to do with the services make it that simple and cost effective for anyone without any telco experience or expertise, really, to build their own private five g network. So you just have to go to your AWS console. Um, describe the parameters for network simple stuff like, Where do you want it to be located? The throughput, the number of devices and AWS will build a plan for your network and seep you everything that you need. Just plug it together. Uh, turn it on and the network automatically configures itself. All you got to do is popular sim cards that we send you into your mobile devices and you have a private five g network working in your your premise is >>one of the things that we know and love about AWS is its customer obsession. It's focused on the customer's that whole flywheel of all the innovation that comes out as Adam was saying yesterday to the customers, we deliver this, but but you wanted more. We said we deliver this, but you wanted more. Talk to me a little bit about some of the customer catalysts for private five G. >>Actually, one of the good examples is where we are right now. More and more AWS customers need to connect an increased number of devices, and these devices become more data hungry. You know they need to push data around. They also become more and more wireless, right? Uh, so when you are trying to connect devices in the manufacturing floor, bit sensors, you know, connect the tracks, forklifts or in a convention centre. You look at how many devices there are around us. When you're trying to connect these devices with a wired network, you quickly run into physical problems like it's. It's hard to lay cable anywhere, and customers try to use for many of these use cases. But as a number of devices grows into the thousands and you know you need to put more and more data around, you quickly reach the limitations of what the WiFi technology and also WiFi is not really great at covering really open, large space. So that's where these customers, you know, think of college campuses, convention centres, manufacturing floors, all of these customers. Really? What they need to be able to do is to level the power of the mobile networks. However, doing that by yourself is pretty hard. So that's what we aim to to enable here we are waiting to enable these customers to build very easily and cost effectively their own. Uh, >>Okay, George. So I have to ask. I'm truly curious. I love this announcement. Um, because it brings together kind of the edge story. But also, I'm a band with love. I love more broad. Give me more broadband. Faster, cheaper and more broadband. How does it work? So take me through the use case of what do I need to deploy? Do I need to have a back haul connection? What does that look like? Is there a certain band with requirements? How big is the footprint? What's the radius? Just walk me through. How do I roll this out? >>Yeah, sure. Some of that stuff actually depends on your requirements, right. How How big? How much of a space do you want to cover? Basically, what we see, if you were in preview right now, so we're sipping you. The simplest configuration, which is basically these things called small cells there, you know, radio units and antennas. And all you have to do is connect them to your local. The network has Internet access. These things connect and automatically had, you know, connect home to the cloud and basically integrate and build up your whole network. All all you need is that Internet connection, and I don't know what to do. Now, how big is the network? You can You can make it pretty big. You can cover hundreds of thousands of square feet with with cellular networks with mobile networks. Um, you know, the bigger you they especially want to cover the more of these radio units. We're gonna stop you, uh, >>classic wireless radios. >>Yes. You >>light up the area with five g connected to the network. That's your choke point. The big of the pipe >>took the bigger pipe. That toxic. I mean, well, there, there's two. There's two things to consider here. There is local connectivity. So devices talking to each other, and there was connectivity back to somewhere else, like the Internet or the cloud. There are use cases, for example. Let's say data video feeds that you want to push up to do some inference in the cloud. In these use cases, you're basically pushing all of the data up. There is no left. There's no East West connectivity locally, and that's where our simplest configuration works best. There are other, uh, use cases where there is a lot of connectivity and devices talk to each other locally, like in this place, for example, right in this. In these cases, we can sip you that second configuration where we actually see Pew, a managed hardware WS managed hardware on premises, and that runs the smart of the network and allows all of your data traffic to remain local. That's >>wavelength Outpost, or both. >>A different configuration of A. W s private five G. It's a managed service. We take. We take care of it. You basically it's very It has a pricing model, which is very customer friendly because you like multi W services. You can start with no upfront fees. You can scale and pay as you scale because >>it's designed to deploy easily. >>Yep, deploys the >>footprint. Just I'm just curious if the poll is it like, it's like an antenna. Is it like so and >>yeah, well, the antenna is, you know, the small cell. They call them small cells in, you know, in in cellular land there, this big. And you can you can hide this. There is actually a demo in the Venetian of the private service. So you can you can actually see it in action, but yeah, that thing can cover 10,000 square feet, just one of them. So you can >>go out and put a five g network downtown and be like the king. >>You could Yes. You could have your own private network. You can monetise that next >>on the Q. >>Great stuff. >>So in terms of industries adopting this, you gave us some examples. Obviously. Convention centres, campuses, universities. I'm just curious, given the amount of acceleration that we've seen in every industry the last 22 months where organisations must become digital. They depend on that for their livelihood. And we saw this all these pivots, right? 22 months ago. How do we survive this? How do we thrive? Are consumers now are whether it's an injury or consumer or enterprise. Have this expectation that we're gonna be able to communicate no matter where we are 24 by seven. Whether it's health care, financial services. I'm just curious if you're seeing any industries in particular that you think are really prime for this private five >>G. Yeah. So manufacturing is a is a really great example because you have to cover large spaces. You have thousands of devices, sensors, etcetera and using other solutions like WiFi does not provide you the depth of capabilities like, for example, you know, advanced security capabilities or even capabilities to prioritise traffic from some devices over others, which is what a five G network can do for you. But also, you know, it involves large spaces both indoors and outdoors. We, you know, actually, Amazon is a really great example of you know of using this. We're working with Amazon fulfilment centres. These are the warehouses that fulfil your orders when you order online. Um, and they are a mix of indoor space and outer space, and you can think of, you know, I don't know if you've seen pictures or videos. There's robots running around their sensors everywhere. There is packing lines, etcetera, all of these things in order to operate performantly, but also securely and safely for the people that are around. You need to be well connected at a very high reliability rate. Right? So, uh, Amazon for two networks is actually using private A W s private five G to connect all of these devices. The really key thing here is you don't have to go drop 1000 of these access points we're talking about you. Can you can. You can probably cover your space with 5 10 of these. So your operational expenses, your maintenance goes down and there is less interruption of your normal operations like you can't. You don't have to stop your manufacturing line for someone to come in and fix your WiFi access. >>It's great for campuses like college campuses, college >>campuses, a great one. We you know, we've worked with college campuses, including the CME University in the past two, you know, with some of our partners to, uh, to to deploy. So >>that's how close you have these distribution, gas systems, distribution, whatever they call it accelerate whatever amplifies into get extra coverage, this seems to be a good fit. Um, for that how you mentioned in the preview? How do people get involved? Is there like a criteria. How was it going to >>be available to get priority? Don't get you >>tell them ready to jump in. Take us through the programme. What's the plants? >>So currently we're you know, we're in that preview mode. So we're keeping you this small configuration, the simpler configuration. You can sign up on the AWS website and you know, we, as we scale our operations are supply chain. Because this involves also, you know, hardware, etcetera. We're gonna go to general availability g A over the next few months and we have both configurations open. So I I encourage everyone who is interested go to the W s website and sign up. We're asking to get that in customers' hands because we're getting overwhelmingly positive feedback on what we built. >>This is transformative. I mean, clearly what you're talking about here is going to transform industry and help organisations transform themselves and outpaced the competitors that are in the rear view mirror Aren't going to be able to take advantage of this were on the show floor. We've got lots of people here. Where can people actually go and see this preview tested up? >>There is an actual demo in the Venetian. I can't remember. Sorry, I can't remember the room. I think it's on the Yes, actually, it's on the floor on the third floor where the meeting rooms are on outside 35 or one. If anyone wants to go, we're >>going to start buying lunch time. >>Yes. Yeah, you can see it in action. And, you know, you could You could see a future where everything, You know, you look around. There's thousands of devices here. You could power all of these devices with a single cell and, you know, really scaled throughput >>in the five G. Just curious, um on the range is better than wifi >>ranges. Better outdoors, >>obviously, or factories. What's the throughput on the >>depending on the spectrum that you choose? And that's actually a really good save way. The device, the service that we built, its spectrum agnostic so it can be used on right now. We're using it on what we call C BRS spectrum, which is the free for all you can. You know, you can you can use it yourself. But also, customers can bring their own spectrum. And we're working with a batch of, uh, CSP operators to build advanced bundles where you can work this on licence spectrum. So if you're going up the spectrum in what they called millimetre wave >>spectrum owner to bring your own licence, >>you could So telco right? You could be a telco, bring your, you know, and work with us as a partner or some actually, actually, manufacturing customers have purchased rights to small spectrum bands so they can use those in combination with this service to deploy. So to your original question, as you're going back up the spectrum, you can drive more and more throughput. You it's not. It's not unheard of to drive one gig. You know what's so >>The low hanging fruit is the the use cases that have critical need for edge connectivity manufacturing? Um, certainly the retail or whatever that they help do the deployment >>we can. We can. We can see this being applicable because because you can start super small. You can see this being applicable even to branch offices, right? Like, uh, let's say I was talking to a customer yesterday. They were thinking or have all these branch offices. I don't even I don't even want to have I thought either he just wants something that's very quickly and easily. You know, I can manage centrally and it just connects. >>Can I should have fixed wireless shot to the wavelength order to have back all with wire >>too. Oh, they actually we are planning to. You know, I talked about where the smarts of the network live in the they can live in a region, they can live in the locals, and they can live in a wave election. So we're combining more and more of these products as well. And it's computing, obviously, is a is an obvious thing that, you know, we should be working on >>incredible work, George, that you and the team have done transforming industries. And I don't know if a feeling there might be a cube to Is it? Would it be too dot >>Oh, John, >>he's ready. Big George, Thank you so much for joining joining me today. It's great >>to be here. Thanks for having that >>for John Ferrier. I'm Lisa Martin. You're watching the Cube, the global leader in live coverage. Mhm