>>it's mhm Okay, we're here in the cube unpacking the HPD Green Lake announcements, Daniel neumann series Principal analyst and founder of your um research Damn. You're good to see you again, >>Dave always going to jump jump on with you. It's good to have a minute sit down. So >>what's your favorite announcement from from Green Lake? What do you, what do you make of what they announced today? >>Well, I love the opportunity for the company to position itself up against a growth monster like snowflake. I mean looking at the ability to handle the breath of the data at scale and offer a data service that can compete in that space. That's exactly the kind of narrative that I think the markets, the outside world is going to want to hear from HP is how you're not just competing with your traditional, the doubles, the Ciscos, the IBM, you're going after the, the mega growth cloud players and data services. And for me that's really attractive because I've been really on top of hb saying, hey, you're doing a lot of the right things, but people have to feel and see the growth. >>To me this is a major move toward the tam expansion strategy. It's kind of the job of every Ceo right, is to expand the tam. And I'm interested to see how HP e plays this and communicates this because, you know, traditionally it's a hardware company, uh moving into data management Data services. That's an enormous market. We'll talk about how important data is but the data management is just huge. And to do it in a cloud like fashion, how do you see that as potentially expanding the total available market for HP? >>Well, first, let's just almost walking back a second, Dave HP is a cloud player. Okay. And that's the story that it is trying to get out there. It is not a hardware player that's tinkering in software. Hp has done software, this isn't its first go. But if you want to be a cloud player, you look at the big hyper scale as you look at the AWS, as you look at the google, you look at as the google built, not just on hardware, it's built that big C and I've had this conversation before, all the things that make up the cloud, it's the hardware, it's the software, it's the services, the platform, you got to put all these things together. And if HP wants to be a public cloud experience, taking advantage of where we're moving with hybrid and offering it private, it has to have that same subset of services. Look at the investment, whether it's been a W S or google or Azure in data services, HP has to be in this space. So, seeing this come to fruition, in my opinion, is directionally the right path, getting it to be well received, winning the right customers and showing the growth from these investments is going to be the next important phase. >>Do you see that as a service model as being more margin friendly for HP and and if so why? Well, I think >>universally we found there's two major improvements that moving to the as a service. One is, it does over time create expanded operational margin. It's just economies of scale. You can utilize every resource more efficiently. Of course there are Capex expenses, You've seen the amount that hyper skeletons have had to spend to expand their their footprints globally. So there is some Capex upfront but that also on the back end creates the depreciation and different bottom line profit creators. At the same time though, as a service is huge for the multiples and evaluation, which by the way is one of the things that has been a real in focus point for H. P. E. Is how does it up that that number, You know, you look at the snowflakes, not even profitable but getting huge. You know, um, you know, huge multiples on revenue. And then you see even the other hyper scale is all getting bigger plays on revenue and on E. P. S. Most of it has to do with the fact that recurring revenue is beloved by investors, but it's also really sticky and creates a ton of stability within the company for the culture of the business to say, hey, we have customers, they're going to stay with us. They're not going anywhere. They're subscribed to our services. They're buying into what we're doing and by the way, net revenue expansion as you get them sticky, you layer in new services. We've seen how this has worked across the board with public cloud, with software with SAS, can HP do it as well? And of course it's been something they're doing, but it's something we need to watch really closely and I think it's an opportunity that the company needs to lean into it. And I think they will, >>you mentioned snowflake a couple times, there's a there's a, there's a discussion in the industry, it was sort of prompted by martin casado and sarah wang about repatriation and particularly as it relates to software, saas companies uh that the the the cloud bill is so high at some point, they're giving away margin, so they're going to have to come back on prem, I'm not sure that to date that has applied to the general audience of customer, although there's a lot of debate as well between the expensive cloud, obviously, you know, egress charges. So it's hard sometimes to squint through that when you think about HP E bringing Green Lake to market at scale bringing repeatable processes, driving automation, etcetera. How do you think that that cloud repatriation argument, which frankly, I haven't seen a huge cloud repatriation in in the macro, but how do you think that will play out over time, Do you feel like the on prem play can be as cost effective or more cost effective or maybe you feel like it is already today? >>Well, I also listen to the injuries and Horowitz uh, repatriation narrative as well. I think there are economies of scale with cloud that companies have to look at closely. But I also think that has a lot to do with why hybrid has been sort of the story of the day. That's why hyper sailors are going on prem or, and that's why I'm primes are moving to the cloud is because it's always going to be some, you know, some group of different placements of workloads to ultimately get to that optimized result. And so, you know, when you look at, you know, sort of what you asked in my opinion, you know, ultimately it's all about the efficiency of your organization trying to accomplish what your business is. And will there be some repatriation of workloads possibly. But there will be a very important hybrid mix. And I think we're gonna continue to see that trend and I think that's exactly where everyone's going in. Hp is going as well. >>All right, then we've got to leave it there. Thanks so much for your insights, appreciate it. We're gonna definitely have you back you and I are going to do some cool stuff together. So we'll talk next time. Thanks all right, and thank you for watching, this is Dave Volonte for the keeps coverage of H P E Green Lakes announcement, keep it right there. Mm

>>From around the globe, it's the Cube with digital coverage of IBM think 2021 brought to you by IBM. Welcome back to the cubes coverage of IBM Think 2021 virtual, I'm john for your host of the cube. We had a great line up here talking sustainability, kary musa ph d general manager of AI applications and block chains going great to see you and wayne, both the vice president of corporate environmental affairs and chief sustainability officer, among other things involved in the products around that. Wait and korean, great to see you. Thanks for coming on. >>Thank you for having us. >>Well, I'll start with you what's driving? IBMS investment in sustainability as a corporate initiative. We know IBM has been active, we've covered this many times, but there's more drivers now as IBM has more of a larger global scope and continues to do that with hybrid cloud, it's much more of a global landscape. What's driving today's investments in sustainability, >>You know, jOHn what drives IBM in this area has always been a longstanding, mature and deep seated belief in corporate responsibility. That's the bedrock foundation. So, you know, IBM 110 year old company, we've always strived to be socially responsible, But what's not as well known is that for the last 50 years, IBM has truly regarded environmental sustainability is a strategic imperative. Okay, It's strategic because hey, environmental problems require a strategic fix. It's a long term imperative because you have to be persistent with environmental problems, you don't necessarily solve them overnight. And it's imperative because business cannot succeed in a world of environmental degradation that really is the main tenant of sustainable development. You can't have successful economies with environmental degradation, you can't solving environmental problems without successful economies. So, and IBM's case as a long standing company, We were advantaged because 50 years ago our ceo at the time, Tom Watson put in place the company's first policy for environmental a stewardship and we've been at it ever since. And he did that in 1971 and that was just six months after the U. S. E. P. A. Was created. It was a year before the Stockholm Conference on the Environment. So we've been added for that long. Um in essence, really it's about recognizing that good environmental management makes good business sense, It's about corporate responsibility and today it's the E of E. S. G. >>You know, wayne. That's a great call out, by the way, referencing thomas Watson, the IBM legend. Um people who don't may not know the history, he was really ahead of its time and that was a lot of the culture they still see around today. So great to see that focus and great, great call out there. But I will ask though, as you guys evolved in today's modern error, how has that evolved in today's focus? Because, you know, we see data centers, carbon footprint, global warming, you now have a I and analytics can measure everything. So I mean you can you can measure everything now. So as the world gets larger in the surface area of what is contributing to the sustainable equation is larger, what's the current IBM focus? >>So these days we continually look at all of the ways in which IBM s day to day business practices intersect with any matter of the environment, whether it's materials, waste water or energy and climate. And IBM actually has 21 voluntary goals that drive us towards leadership. But today john as you know, uh the headline is really climate change and so we're squarely focused like many others on that and that's an imperative. But let me say before I just before I briefly tell you our current goals, it's also important to have context as to where we have been because that helps people understand what we're doing today. And so again, climate change is a topic that the men and women of IBM have paid attention to for a long time. Yeah, I was think about it. It was back in 1992 that the U. S. C. P. A. Created something called Energy Star. People look at that and they said, well, what's that all about? Okay, that's all about climate change. Because the most environmentally friendly energy you can get is the energy that you don't really need to consume. IBM was one of eight companies that helped the U. S. C. P. A. Launched that program 1992. Today we're all disclosing C. 02 emissions. IBM began doing that in 1994. Okay. In 2007, 13 years ago, I'd be unpublished. Its position on climate change, calling for urgent action around the world. He supported the Paris Agreement 2015. We reiterated that support in 2017 for the us to remain a partner. 2019, we became a founding member of Climate Leadership Council which calls for a carbon tax and a carbon dividend. So that's all background context. Today, we're working on our third renewable electricity goal, our fifth greenhouse gas emissions reduction goal and we set a new goal to achieve net zero greenhouse gas emissions. Each of those three compels IBM to near term action. >>That's awesome wayne as corporate environmental affairs and chief sustainable, great vision and awesome work. Karim dr Karim use if I wanna we leave you in here, you're the general manager. You you got to make this work because of the corporate citizenship that IBM is displaying. Obviously world world class, we know that's been been well reported and known, but now it's a business model. People realize that it's good business to have sustainability, whether it's carbon neutral footprints and or intersecting and contributing for the world and their employees who want mission driven companies ai and Blockchain, that's your wheelhouse. This is like you're on the big wave, wow, this is happening, give us your view because you're commercializing this in real time. >>Yeah, look as you've already said and it's the way well articulated, this is a business imperative, right is key to all companies corporate strategies. So the first step when you think about operationalized in this is what we've been doing, is to really step back and kind of break this down into what we call five key needs or focus areas that we've understood that we work with our clients. Remember in this context, Wayne is indeed my clients as well. Right. And so when you think about it, the five needs, as we like to lay them out, we talk about the sustainability strategy first of all, how are you approaching it as you saw from Wayne, identifying your key goals and approaches right against that, you begin to get into various areas and dimensions. Climate risk management is becoming increasingly important, especially in asset heavy industries electrification, energy and emissions management, another key focus area where we can bring technology to bear resilient infrastructure and operations, sustainable supply chain, All of these kind of come together to really connect with our clients business operations and allows us to bring together the technologies and context of ai Blockchain and the key business operations. We can support to kind of begin to address specific news cases in the context of those >>needs. You know, I've covered it in the past and written about and also talked about on the cube about sustainability on the supply chain side with Blockchain, whether it's your tracking, you know, um you know, transport of goods with with Blockchain and making sure that that kind of leads your kind of philosophy works because there's waste involved is also disruption to business, a security issues, but when you really move into the Ai side, how does a company scale that Corinne, because now, you know, I have to one operationalize it and then scale it. Okay, so that's transformed, innovate and scale. How do I take take me through the examples of how that works >>well, I think really key to that, and this is really key to our ethos, it's enabling ai for business by integrating ai directly into business operations and decision making. So it's not really how can I put this? We try to make it so that the client isn't fixating on trying to deploy ai, they're just leveraging Ai. So as you say, let's take some practical examples. You talked about sustainable supply chains and you know, the key needs around transparency and provenance. Right. So we have helped clients like a tear with their seafood network or the shrimp sustainability network where there's a big focus on understanding where are things being sourced and how they're moving through the supply chain. We also have a responsible sourcing business network that's being used for cobalt in batteries as an example from mine to manufacturing and here our technologies are allowing us to essentially track, trace and prove the provenance Blockchain serves as kind of that key shared ledger to pull all this information together. But we're leveraging AI to begin to quickly assess based upon the data inputs, the actual state of inventory, how to connect dots across multiple suppliers and as you on board in an off board them off the network. So that's how we begin to put A I in action so that the client begins to fixate on the work and the decisions they need to make. Not the AI itself. Another quick example would be in the context of civil infrastructure. One of our clients son and Belt large, maximum client of ours he uses maximum too rarely focus on the maintaining sustainable maintenance of their bridges. Think about how much money is spent setting up to do bridge inspections right. When you think about how much they have to invest the stopping of the traffic that scaffolding. We have been leveraging AI to do things like visual inspection. Actually fly drones, take pictures, assess those images to identify cracks and use that to route and prioritized work. Similar examples are occurring in energy and utilities focused on vegetation management where we're leveraging AI to analyse satellite imagery, weather data and bringing it together so that work can be optimally prior authorized and deployed for our >>clients. It's interesting. One of the themes coming out of think that I'm observing is this notion of transformation is innovation and innovation is about scale. Right? So it's not just innovation for innovating sake. You can transform from whether it's bridge inspections to managing any other previous pre existing kind of legacy condition and bring that into a modern error and then scale it with data. This is a common theme. It applies to to your examples. Kareem, that's super valuable. Um how do you how do you tie that together with partnering? Because wayne you were talking about the corporate initiative, that's just IBM we learned certainly in cybersecurity and now these other areas like sustainability, it's a team sport, you have to work on a global footprint with other industries and other leaders. How was I being working across the industry to connect and work with other, either initiatives or companies or governments. >>Sure. And there have been john over the years and at present a number of diverse collaborations that we seek out and we participate in. But before I address that, I just want to amplify something Kareem said, because it's so important, as I look back at the environmental movement over the last 50 years, frankly, since the first earth day in 1970, I, you know, with the benefit of hindsight, I observed there have really been three different hair, it's in the very beginning, global societies had to enact laws to control pollution that was occurring. That was the late 60s 1970s, into the early 1980s and around the early 1980s through to the first part of this century, that era of let's get control of this sort of transformed, oh how can we prevent stuff from happening given the way we've always done business and that area ran for a while. But now thanks to technology and data and things like Blockchain and ai we all have the opportunity to move into this era of innovation which differs from control in which differs from traditional prevention. Innovation is about changing the way you get the same thing done. And the reason that's enabled is because of the tools that you just spoke about with Korean. So how do we socialize these opportunities? Well to your question, we interact with a variety of diverse teams, government, different business associations, Ngos and Academia. Some examples, there's an organization named the Center for Climate and Energy Solutions, which IBM is a founding member of its Business Leadership Council. Its predecessor was the Q Centre on global climate change. We've been involved with that since 1998. That is a cross section of people from all these different constituencies who are looking for solutions to climate. Many Fortune 102000s in there were part of the green grid. The green grid is an organization of companies involved with data centers and it's constantly looking at how do you measure energy efficiency and data centers and what are best practices to reduce consumption of energy at data centers where a member of the renewable energy buyers alliance? Many Fortune 100 200 Zarin that trying to apply scale to procure more renewable electricity to actually come to our facilities I mentioned earlier were part of the Climate Leadership Council calling for a carbon tax were part of the United Nations Environment Programs science Policy business form that gets us involved with many ministers of Environment from countries around the world. We recently joined the new MITt Climate and sustainability consortium. Mitt Premier Research University. Many key leaders are part of that. Looking at how academic research can supercharge this opportunity for innovation and then the last one, I'll just wrap up call for code. You may be familiar with IBM s involvement in call for code. Okay. The current challenge under call for code in 2021 calls for solutions targeted the climate change. So that's, that's a diverse set of different constituents, different types of people. But we try to get involved with all of them because we learn and hopefully we contribute something along the way as well. >>Awesome Wayne. Thank you very much Karim, the last 30 seconds we got here. How do companies partner with IBM if they want to connect in with the mission and the citizenship that you guys are doing? How do they bring that to their company real quick. Give us a quick overview. >>Well, you know, it's really quite simple. Many of these clients are already clients of ours were engaging with them in the marketplace today, right, trying to make sure we understand their needs, trying to ensure that we tune what we've got to offer, both in terms of product and consulting services with our GPS brethren, you know, to meet their needs, linking that in as well to IBM being and what we like to turn client zero. We're also applying these same technologies and capabilities to support IBM efforts. And so as they engage in all these associations, what IBM is doing that also provides a way to really get started. It's really fixate on those five imperatives or needs are laid out, picked kind of a starting point and tie it to something that matters. That changes how you're doing something today. That's really the key. As far as uh we're concerned, >>Karim, we thank you for your time on sustainability. Great initiative, Congratulations on the continued mission. Going back to the early days of IBM and the Watson generation continuing out in the modern era. Congratulations and thanks for sharing. >>Thank you john. >>Okay. It's the cubes coverage. I'm sean for your host. Thanks for watching. >>Mm. Mhm.

>>from around the >>globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, >>Hi. We are the Cube live covering AWS reinvent 2020. I'm Lisa Martin, and I've got one of our cube alumni back with me. Mike Miller is here. General manager of A W s AI Devices at AWS. Mike, welcome back to the Cube. >>Hi, Lisa. Thank you so much for having me. It's really great to join you all again at this virtual reinvent. >>Yes, I think last year you were on set. We have always had to. That's at reinvent. And you you had the deep race, your car, and so we're obviously socially distance here. But talk to me about deepracer. What's going on? Some of the things that have gone on the last year that you're excited >>about. Yeah, I'd love to tell. Tell you a little bit about what's been happening. We've had a tremendous year. Obviously, Cove. It has restricted our ability to have our in person races. Eso we've really gone gone gangbusters with our virtual league. So we have monthly races for competitors that culminate in the championship. Um, at reinvent. So this year we've got over 100 competitors who have qualified and who are racing virtually with us this year at reinvent. They're participating in a series of knockout rounds that are being broadcast live on twitch over the next week. That will whittle the group down to AH Group of 32 which will have a Siris of single elimination brackets leading to eight finalists who will race Grand Prix style five laps, eight cars on the track at the same time and will crown the champion at the closing keynote on December 15th this year. >>Exciting? So you're bringing a reinforcement, learning together with with sports that so many of us have been missing during the pandemic. We talked to me a little bit about some of the things that air that you've improved with Deep Racer and some of the things that are coming next year. Yeah, >>absolutely so, First of all, Deep Racer not only has been interesting for individuals to participate in the league, but we continue to see great traction and adoption amongst big customers on dare, using Deep Racer for hands on learning for machine learning, and many of them are turning to Deep Racer to train their workforce in machine learning. So over 150 customers from the likes of Capital One Moody's, Accenture, DBS Bank, JPMorgan Chase, BMW and Toyota have held Deep Racer events for their workforces. And in fact, three of those customers Accenture, DBS Bank and J. P. Morgan Chase have each trained over 1000 employees in their organization because they're just super excited. And they find that deep racers away to drive that excitement and engagement across their customers. We even have Capital one expanded this to their families, so Capital One ran a deep raise. Their Kids Cup, a family friendly virtual competition this past year were over. 250 Children and 200 families got to get hands on with machine learning. >>So I envisioned some. You know, this being a big facilitator during the pandemic when there's been this massive shift to remote work has have you seen an uptick in it for companies that talking about training need to be ableto higher? Many, many more people remotely but also train them? Is deep Racer facilitator of that? Yeah, >>absolutely. Deep Racer has ah core component of the experience, which is all virtualized. So we have, ah, console and integration with other AWS services so that racers can participate using a three d racing simulator. They can actually see their car driving around a track in a three D world simulation. Um, we're also selling the physical devices. So you know, if participants want to get the one of those devices and translate what they've done in the virtual world to the real world, they can start doing that. And in fact, just this past year, we made our deep race or car available for purchase internationally through the Amazon Com website to help facilitate that. >>So how maney deep racers air out there? I'm just curious. >>Oh, thousands. Um, you know, And there what? What we've seen is some companies will purchase you, know them in bulk and use them for their internal leagues. Just like you know, JP Morgan Chase on DBS Bank. These folks have their own kind of tracks and racers that they'll use to facilitate both in person as well as the virtual racing. >>I'm curious with this shift to remote that we mentioned a minute ago. How are you seeing deepracer as a facilitator of engagement. You mentioned engagement. And that's one of the biggest challenges that so Maney teams develops. Processes have without being co located with each other deep Brister help with that. I mean, from an engagement perspective, I think >>so. What we've seen is that Deep Racer is just fun to get your hands on. And we really lower the learning curve for machine learning. And in particular, this branch called reinforcement Learning, which is where you train this agent through trial and error toe, learn how to do a new, complex task. Um, and what we've seen is that customers who have introduced Deep Racer, um, as an event for their employees have seen ah, very wide variety of employees. Skill sets, um, kind of get engaged. So you've got not just the hardcore deep data scientists or the M L engineers. You've got Web front end programmers. You even have some non technical folks who want to get their hands dirty. Onda learn about machine learning and Deep Racer really is a nice, gradual introduction to doing that. You can get engaged with it with very little kind of coding knowledge at all. >>So talk to me about some of the new services. And let's look at some specific use case customer use cases with each service. Yeah, >>absolutely. So just to set the context. You know, Amazon's got hundreds. A ws has hundreds of thousands of customers doing machine learning on AWS. No customers of all sizes are embedding machine learning into their no core business processes. And one of the things that we always do it Amazon is We're listening to customers. You know, 90 to 95% of our road maps are driven by customer feedback. And so, as we've been talking to these industrial manufacturing customers, they've been telling us, Hey, we've got data. We've got these processes that are happening in our industrial sites. Um, and we just need some help connecting the dots like, how do we really most effectively use machine learning to improve our processes in these industrial and manufacturing sites? And so we've come up with these five services. They're focused on industrial manufacturing customers, uh, two of the services air focused around, um, predictive maintenance and, uh, the other three services air focused on computer vision. Um, and so let's start with the predictive maintenance side. So we announced Amazon Monitor On and Amazon look out for equipment. So these services both enable predictive maintenance powered by machine learning in a way that doesn't require the customer to have any machine learning expertise. So Mono Tron is an end to end machine learning system with sensors, gateway and an ML service that can detect anomalies and predict when industrial equipment will require maintenance. I've actually got a couple examples here of the sensors in the gateway, so this is Amazon monitor on these little sensors. This little guy is a vibration and temperature sensor that's battery operated, and wireless connects to the gateway, which then transfers the data up to the M L Service in the cloud. And what happens is, um, the sensors can be connected to any rotating machinery like pump. Pour a fan or a compressor, and they will send data up to the machine learning cloud service, which will detect anomalies or sort of irregular kind of sensor readings and then alert via a mobile app. Just a tech or a maintenance technician at an industrial site to go have a look at their equipment and do some preventative maintenance. So um, it's super extreme line to end to end and easy for, you know, a company that has no machine learning expertise to take advantage of >>really helping them get on board quite quickly. Yeah, >>absolutely. It's simple tea set up. There's really very little configuration. It's just a matter of placing the sensors, pairing them up with the mobile app and you're off and running. >>Excellent. I like easy. So some of the other use cases? Yeah, absolutely. >>So So we've seen. So Amazon fulfillment centers actually have, um, enormous amounts of equipment you can imagine, you know, the size of an Amazon fulfillment center. 28 football fields, long miles of conveyor belts and Amazon fulfillment centers have started to use Amazon monitor on, uh, to monitor some of their conveyor belts. And we've got a filament center in Germany that has started using these 1000 sensors, and they've already been able to, you know, do predictive maintenance and prevent downtime, which is super costly, you know, for businesses, we've also got customers like Fender, you know, who makes guitars and amplifiers and musical equipment. Here in the US, they're adopting Amazon monitor on for their industrial machinery, um, to help prevent downtime, which again can cost them a great deal as they kind of hand manufacture these high end guitars. Then there's Amazon. Look out for equipment, which is one step further from Amazon monitor on Amazon. Look out for equipment. Um provides a way for customers to send their own sensor data to AWS in order to build and train a model that returns predictions for detecting abnormal equipment behavior. So here we have a customer, for example, like GP uh, E P s in South Korea, or I'm sorry, g S E P s in South Korea there in industrial conglomerate, and they've been collecting their own data. So they have their own sensors from industrial equipment for a decade. And they've been using just kind of rule basic rules based systems to try to gain insight into that data. Well, now they're using Amazon, look out for equipment to take all of their existing sensor data, have Amazon for equipment, automatically generate machine learning models on, then process the sensor data to know when they're abnormalities or when some predictive maintenance needs to occur. >>So you've got the capabilities of working with with customers and industry that that don't have any ML training to those that do have been using sensors. So really, everybody has an opportunity here to leverage this new Amazon technology, not only for predicted, but one of the things I'm hearing is contact list, being able to understand what's going on without having to have someone physically there unless there is an issue in contact. This is not one of the words of 2020 but I think it probably should be. >>Yeah, absolutely. And in fact, that that was some of the genesis of some of the next industrial services that we announced that are based on computer vision. What we saw on what we heard when talking to these customers is they have what we call human inspection processes or manual inspection processes that are required today for everything from, you know, monitoring you like workplace safety, too, you know, quality of goods coming off of a machinery line or monitoring their yard and sort of their, you know, truck entry and exit on their looking for computer vision toe automate a lot of these tasks. And so we just announced a couple new services that use computer vision to do that to automate these once previously manual inspection tasks. So let's start with a W A. W s Panorama uses computer vision toe improve those operations and workplace safety. AWS Panorama is, uh, comes in two flavors. There's an appliance, which is, ah, box like this. Um, it basically can go get installed on your network, and it will automatically discover and start processing the video feeds from existing cameras. So there's no additional capital expense to take a W s panorama and have it apply computer vision to the cameras that you've already got deployed, you know, So customers are are seeing that, um, you know, computer vision is valuable, but the reason they want to do this at the edge and put this computer vision on site is because sometimes they need to make very low Leighton see decisions where if you have, like a fast moving industrial process, you can use computer vision. But I don't really want to incur the cost of sending data to the cloud and back. I need to make a split second decision, so we need machine learning that happens on premise. Sometimes they don't want to stream high bandwidth video. Or they just don't have the bandwidth to get this video back to the cloud and sometimes their data governance or privacy restrictions that restrict the company's ability to send images or video from their site, um, off site to the cloud. And so this is why Panorama takes this machine learning and makes it happen right here on the edge for customers. So we've got customers like Cargill who uses or who is going to use Panorama to improve their yard management. They wanna use computer vision to detect the size of trucks that drive into their granaries and then automatically assign them to an appropriately sized loading dock. You've got a customer like Siemens Mobility who you know, works with municipalities on, you know, traffic on by other transport solutions. They're going to use AWS Panorama to take advantage of those existing kind of traffic cameras and build machine learning models that can, you know, improve congestion, allocate curbside space, optimize parking. We've also got retail customers. For instance, Parkland is a Canadian fuel station, um, and retailer, you know, like a little quick stop, and they want to use Panorama to do things like count the people coming in and out of their stores and do heat maps like, Where are people visiting my store so I can optimize retail promotions and product placement? >>That's fantastic. The number of use cases is just, I imagine if we had more time like you could keep going and going. But thank you so much for not only sharing what's going on with Deep Racer and the innovations, but also for show until even though we weren't in person at reinvent this year, Great to have you back on the Cube. Mike. We appreciate your time. Yeah, thanks, Lisa, for having me. I appreciate it for Mike Miller. I'm Lisa Martin. You're watching the cubes Live coverage of aws reinvent 2020.

>>live from Miami, Florida It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, everybody. We're here at the Intercontinental Hotel. You're watching the Cube? The leader and I live tech covered set. Daubert is here. He's the vice president of data and I and a I and the chief data officer of cloud and cognitive software. And I'd be upset too. Good to see you again. >>Good. See, Dave, thanks for having me >>here. The data in a I form hashtag data. I I It's amazing here. 1700 people. Everybody's gonna hands on appetite for learning. Yeah. What do you see out in the marketplace? You know what's new since we last talked. >>Well, so I think if you look at some of the things that are really need in the marketplace, it's really been around filling the skill shortage. And how do you operationalize and and industrialize? You're a I. And so there's been a real need for things ways to get more productivity out of your data. Scientists not necessarily replace them. But how do you get more productivity? And we just released a few months ago, something called Auto A I, which really is, is probably the only tool out there that automates the end end pipeline automates 80% of the work on the Indian pipeline, but isn't a black box. It actually kicks out code. So your data scientists can then take it, optimize it further and understand it, and really feel more comfortable about it. >>He's got a eye for a eyes. That's >>exactly what is a eye for an eye. >>So how's that work? So you're applying machine intelligence Two data to make? Aye. Aye, more productive pick algorithms. Best fit. >>Yeah, So it does. Basically, you feed it your data and it identifies the features that are important. It does feature engineering for you. It does model selection for you. It does hyper parameter tuning and optimization, and it does deployment and also met monitors for bias. >>So what's the date of scientists do? >>Data scientist takes the code out the back end. And really, there's some tweaks that you know, the model, maybe the auto. Aye, aye. Maybe not. Get it perfect, Um, and really customize it for the business and the needs of the business. that the that the auto A I so they not understand >>the data scientist, then can can he or she can apply it in a way that is unique to their business that essentially becomes their I p. It's not like generic. Aye, aye for everybody. It's it's customized by And that's where data science to complain that I have the time to do this. Wrangling data >>exactly. And it was built in a combination from IBM Research since a great assets at IBM Research plus some cattle masters at work here at IBM that really designed and optimize the algorithm selection and things like that. And then at the keynote today, uh, wonderment Thompson was up there talking, and this is probably one of the most impactful use cases of auto. Aye, aye to date. And it was also, you know, my former team, the data science elite team, was engaged, but wonderment Thompson had this problem where they had, like, 17,000 features in their data sets, and what they wanted to do was they wanted to be able to have a custom solution for their customers. And so every time they get a customer that have to have a data scientist that would sit down and figure out what the right features and how the engineer for this customer. It was an intractable problem for them. You know, the person from wonderment Thompson have prevented presented today said he's been trying to solve this problem for eight years. Auto Way I, plus the data science elite team solve the form in two months, and after that two months, it went right into production. So in this case, oughta way. I isn't doing the whole pipeline. It's helping them identify the features and engineering the features that are important and giving them a head start on the model. >>What's the, uh, what's the acquisition bottle for all the way as a It's a license software product. Is it assassin part >>of Cloudpack for data, and it's available on IBM Cloud. So it's on IBM Cloud. You can use it paper use so you get a license as part of watching studio on IBM Cloud. If you invest in Cloudpack for data, it could be a perpetual license or committed term license, which essentially assassin, >>it's essentially a feature at dawn of Cloudpack for data. >>It's part of Cloudpack per day and you're >>saying it can be usage based. So that's key. >>Consumption based hot pack for data is all consumption based, >>so people want to use a eye for competitive advantage. I said by my open that you know, we're not marching to the cadence of Moore's Law in this industry anymore. It's a combination of data and then cloud for scale. So so people want competitive advantage. You've talked about some things that folks are doing to gain that competitive advantage. But the same time we heard from Rob Thomas that only about 4 to 10% penetration for a I. What? What are the key blockers that you see and how you're knocking them >>down? Well, I think there's. There's a number of key blockers, so one is of access to data, right? Cos have tons of data, but being able to even know what data is, they're being able to pull it all together and being able to do it in a way that is compliant with regulation because you got you can't do a I in a vacuum. You have to do it in the context of ever increasing regulation like GDP R and C, C, P A and all these other regulator privacy regulations that are popping up. So so that's that's really too so access to data and regulation can be blockers. The 2nd 1 or the 3rd 1 is really access to appropriate skills, which we talked a little bit about. Andi, how do you retrain, or how do you up skill, the talent you have? And then how do you actually bring in new talent that can execute what you want on then? Sometimes in some cos it's a lack of strategy with appropriate measurement, right? So what is your A II strategy, and how are you gonna measure success? And you and I have talked about this on Cuban on Cube before, where it's gotta measure your success in dollars and cents right cost savings, net new revenue. That's really all your CFO is care about. That's how you have to be able to measure and monitor your success. >>Yes. Oh, it's so that's that Last one is probably were where most organizations start. Let's prioritize the use cases of the give us the best bang for the buck, and then business guys probably get really excited and say Okay, let's go. But to up to truly operationalize that you gotta worry about these other things. You know, the compliance issues and you gotta have the skill sets. Yeah, it's a scale. >>And sometimes that's actually the first thing you said is sometimes a mistake. So focusing on the one that's got the most bang for the buck is not necessarily the best place to start for a couple of reasons. So one is you may not have the right data. It may not be available. It may not be governed properly. Number one, number two the business that you're building it for, may not be ready to consume it right. They may not be either bought in or the processes need to change so much or something like that, that it's not gonna get used. And you can build the best a I in the world. If it doesn't get used, it creates zero value, right? And so you really want to focus on for the first couple of projects? What are the one that we can deliver the best value, not Sarah, the most value, but the best value in the shortest amount of time and ensure that it gets into production because especially when you're starting off, if you don't show adoption, people are gonna lose interest. >>What are you >>seeing in terms of experimentation now in the customer base? You know, when you talk to buyers and you talk about, you know, you look at the I T. Spending service. People are concerned about tariffs. The trade will hurt the 2020 election. They're being a little bit cautious. But in the last two or three years have been a lot of experimentation going on. And a big part of that is a I and machine learning. What are you seeing in terms of that experimentation turning into actually production project that we can learn from and maybe do some new experiments? >>Yeah, and I think it depends on how you're doing the experiments. There's, I think there's kind of academic experimentation where you have data science, Sistine Data science teams that come work on cool stuff that may or may not have business value and may or may not be implemented right. They just kind of latch on. The business isn't really involved. They latch on, they do projects, and that's I think that's actually bad experimentation if you let it that run your program. The good experimentation is when you start identity having a strategy. You identify the use cases you want to go after and you experiment by leveraging, agile to deliver these methodologies. You deliver value in two weeks prints, and you can start delivering value quickly. You know, in the case of wonderment, Thompson again 88 weeks, four sprints. They got value. That was an experiment, right? That was an experiment because it was done. Agile methodologies using good coding practices using good, you know, kind of design up front practices. They were able to take that and put it right into production. If you're doing experimentation, you have to rewrite your code at the end. And it's a waste of time >>T to your earlier point. The moon shots are oftentimes could be too risky. And if you blow it on a moon shot, it could set you back years. So you got to be careful. Pick your spots, picked ones that maybe representative, but our lower maybe, maybe lower risk. Apply agile methodologies, get a quick return, learn, develop those skills, and then then build up to the moon ship >>or you break that moon shot down its consumable pieces. Right, Because the moon shot may take you two years to get to. But maybe there are sub components of that moon shot that you could deliver in 34 months and you start delivering knows, and you work up to the moon shot. >>I always like to ask the dog food in people. And I said, like that. Call it sipping your own champagne. What do you guys done internally? When we first met, it was and I think, a snowy day in Boston, right at the spark. Some it years ago. And you did a big career switch, and it's obviously working out for you, But But what are some of the things? And you were in part, brought in to help IBM internally as well as Interpol Help IBM really become data driven internally? Yeah. How has that gone? What have you learned? And how are you taking that to customers? >>Yeah, so I was hired three years ago now believe it was that long toe lead. Our internal transformation over the last couple of years, I got I don't want to say distracted there were really important business things I need to focus on, like gpr and helping our customers get up and running with with data science, and I build a data science elite team. So as of a couple months ago, I'm back, you know, almost entirely focused on her internal transformation. And, you know, it's really about making sure that we use data and a I to make appropriate decisions on DSO. Now we have. You know, we have an app on her phone that leverages Cognos analytics, where at any point, Ginny Rometty or Rob Thomas or Arvin Krishna can pull up and look in what we call E P M. Which is enterprise performance management and understand where the business is, right? What what do we do in third quarter, which just wrapped up what was what's the pipeline for fourth quarter? And it's at your fingertips. We're working on revamping our planning cycle. So today planning has been done in Excel. We're leveraging Planning Analytics, which is a great planning and scenario planning tool that with the tip of a button, really let a click of a button really let you understand how your business can perform in the future and what things need to do to get it perform. We're also looking across all of cloud and cognitive software, which data and A I sits in and within each business unit and cloud and cognitive software. The sales teams do a great job of cross sell upsell. But there's a huge opportunity of how do we cross sell up sell across the five different businesses that live inside of cloud and cognitive software. So did an aye aye hybrid cloud integration, IBM Cloud cognitive Applications and IBM Security. There's a lot of potential interplay that our customers do across there and providing a I that helps the sales people understand when they can create more value. Excuse me for our customers. >>It's interesting. This is the 10th year of doing the Cube, and when we first started, it was sort of the beginning of the the big data craze, and a lot of people said, Oh, okay, here's the disruption, crossing the chasm. Innovator's dilemma. All that old stuff going away, all the new stuff coming in. But you mentioned Cognos on mobile, and that's this is the thing we learned is that the key ingredients to data strategies. Comprised the existing systems. Yes. Throw those out. Those of the systems of record that were the single version of the truth, if you will, that people trusted you, go back to trust and all this other stuff built up around it. Which kind of created dissidents. Yeah. And so it sounds like one of the initiatives that you you're an IBM I've been working on is really bringing in the new pieces, modernizing sort of the existing so that you've got sort of consistent data sets that people could work. And one of the >>capabilities that really has enabled this transformation in the last six months for us internally and for our clients inside a cloud pack for data, we have this capability called IBM data virtualization, which we have all these independent sources of truth to stomach, you know? And then we have all these other data sources that may or may not be as trusted, but to be able to bring them together literally. With the click of a button, you drop your data sources in the Aye. Aye, within data. Virtualization actually identifies keys across the different things so you can link your data. You look at it, you check it, and it really enables you to do this at scale. And all you need to do is say, pointed out the data. Here's the I. P. Address of where the data lives, and it will bring that in and help you connect it. >>So you mentioned variances in data quality and consumer of the data has to have trust in that data. Can you use machine intelligence and a I to sort of give you a data confidence meter, if you will. Yeah. So there's two things >>that we use for data confidence. I call it dodging this factor, right. Understanding what the dodging this factor is of the data. So we definitely leverage. Aye. Aye. So a I If you have a date, a dictionary and you have metadata, the I can understand eight equality. And it can also look at what your data stewards do, and it can do some of the remediation of the data quality issues. But we all in Watson Knowledge catalog, which again is an in cloudpack for data. We also have the ability to vote up and vote down data. So as much as the team is using data internally. If there's a data set that had a you know, we had a hive data quality score, but it wasn't really valuable. It'll get voted down, and it will help. When you search for data in the system, it will sort it kind of like you do a search on the Internet and it'll it'll down rank that one, depending on how many down votes they got. >>So it's a wisdom of the crowd type of. >>It's a crowd sourcing combined with the I >>as that, in your experience at all, changed the dynamics of politics within organizations. In other words, I'm sure we've all been a lot of meetings where somebody puts foursome data. And if the most senior person in the room doesn't like the data, it doesn't like the implication he or she will attack the data source, and then the meeting's over and it might not necessarily be the best decision for the organization. So So I think it's maybe >>not the up, voting down voting that does that, but it's things like the E PM tool that I said we have here. You know there is a single source of truth for our finance data. It's on everyone's phone. Who needs access to it? Right? When you have a conversation about how the company or the division or the business unit is performing financially, it comes from E. P M. Whether it's in the Cognos app or whether it's in a dashboard, a separate dashboard and Cognos or is being fed into an aye aye, that we're building. This is the source of truth. Similarly, for product data, our individual products before me it comes from here's so the conversation at the senior senior meetings are no longer your data is different from my data. I don't believe it. You've eliminated that conversation. This is the data. This is the only data. Now you can have a conversation about what's really important >>in adult conversation. Okay, Now what are we going to do? It? It's >>not a bickering about my data versus your data. >>So what's next for you on? You know, you're you've been pulled in a lot of different places again. You started at IBM as an internal transformation change agent. You got pulled into a lot of customer situations because yeah, you know, you're doing so. Sales guys want to drag you along and help facilitate activity with clients. What's new? What's what's next for you. >>So really, you know, I've only been refocused on the internal transformation for a couple months now. So really extending IBM struck our cloud and cognitive software a data and a I strategy and starting to quickly implement some of these products, just like project. So, like, just like I just said, you know, we're starting project without even knowing what the prioritized list is. Intuitively, this one's important. The team's going to start working on it, and one of them is an aye aye project, which is around cross sell upsell that I mentioned across the portfolio and the other one we just got done talking about how in the senior leadership meeting for Claude Incognito software, how do we all work from a Cognos dashboard instead of Excel data data that's been exported put into Excel? The challenge with that is not that people don't trust the data. It's that if there's a question you can't drill down. So if there's a question about an Excel document or a power point that's up there, you will get back next meeting in a month or in two weeks, we'll have an e mail conversation about it. If it's presented in a really live dashboard, you can drill down and you can actually answer questions in real time. The value of that is immense, because now you as a leadership team, you can make a decision at that point and decide what direction you're going to do. Based on data, >>I said last time I have one more questions. You're CDO but you're a polymath on. So my question is, what should people look for in a chief data officer? What sort of the characteristics in the attributes, given your >>experience, that's kind of a loaded question, because there is. There is no good job, single job description for a chief date officer. I think there's a good solid set of skill sets, the fine for a cheap date officer and actually, as part of the chief data officer summits that you you know, you guys attend. We had were having sessions with the chief date officers, kind of defining a curriculum for cheap date officers with our clients so that we can help build the chief. That officer in the future. But if you look a quality so cheap, date officer is also a chief disruption officer. So it needs to be someone who is really good at and really good at driving change and really good at disrupting processes and getting people excited about it changes hard. People don't like change. How do you do? You need someone who can get people excited about change. So that's one thing. On depending on what industry you're in, it's got to be. It could be if you're in financial or heavy regulated industry, you want someone that understands governance. And that's kind of what Gardner and other analysts call a defensive CDO very governance Focus. And then you also have some CDOs, which I I fit into this bucket, which is, um, or offensive CDO, which is how do you create value from data? How do you caught save money? How do you create net new revenue? How do you create new business models, leveraging data and a I? And now there's kind of 1/3 type of CDO emerging, which is CDO not as a cost center but a studio as a p N l. How do you generate revenue for the business directly from your CDO office. >>I like that framework, right? >>I can't take credit for it. That's Gartner. >>Its governance, they call it. We say he called defensive and offensive. And then first time I met Interpol. He said, Look, you start with how does data affect the monetization of my organization? And that means making money or saving money. Seth, thanks so much for coming on. The Cube is great to see you >>again. Thanks for having me >>again. All right, Keep it right to everybody. We'll be back at the IBM data in a I form from Miami. You're watching the Cube?