>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. >>Welcome to the Cubes Coverage of Dell Technologies World 2020. The Digital Experience. I'm Lisa Martin, and I'm pleased to welcome back a Cube alumni and a new Cube member to the program today. China. My Mondal is back with US Director of Solutions Marketing for Dell Technologies China. But it's great to see you at Dell Technologies world, even though we're very specially death. >>Happy to be back. Thank you, Lisa. >>And Thomas Henson is joining us for the first time. Global business development manager for a I and analytics. Thomas, Welcome to the Cube. >>I am excited to be here. It's my first virtual cube. >>Yeah, well, you better make it a good one. All right. I said we're talking about a I so so much has changed John to me. The last time I saw you were probably were sitting a lot closer together. So much has changed in the last 67 months, but a lot has changed with the adoption of Ai Thomas. Kick us off. What are some of the big things feeling ai adoption right now? >>Yeah, I >>would have to >>say the two biggest things right now or as we look at accelerated compute and by accelerated compute we're not just talking about the continuation of Moore's law, but how In Data Analytics, we're actually doing more processing now with GP use, which give us faster insights. And so now we have the ability to get quicker insights in jobs that may have taken, you know, taking weeks to months a song as we were measuring. And then the second portion is when we start to talk about the innovation going on in the software and framework world, right? So no longer do you have toe know C plus plus or a lower level language. You can actually do it in Python and even pull it off of Get Hub. And it's all part of that open source community. So we're seeing Mawr more folks in the field of data science and deep learning that can actually implement some code. And then we've got faster compute to be able to process that. >>Tell me, what are your thoughts? >>Think I want to add? Is the explosive growth off data on that's actually are fulfilling the AI adoption. Think off. Like all the devices we have, the i o t. On age devices are doing data are pumping data into the pipeline. Our high resolution satellite imagery, all social media generating data. No. All of this data are actually helping the adoption off a I because now we have very granular data tow our friend the AI model Make the AI models are much better. Besides, so the combination off both in, uh, data the power off Like GPU, power surfers are coupled with the inefficient in the eye after and tools helping off. Well, the AI growth that we're seeing today >>trying to make one of the things that we've known for a while now is that it's for a I to be valuable. It's about extracting value from that. Did it? You talked about the massive explosion and data, but yet we know for a long time we've been talking about AI for decades. Initiatives can fail. What can Dell Technologies do now to help companies have successfully I project? >>Yeah, eso As you were saying, Lisa, what we're seeing is the companies are trying to add up AI Technologies toe Dr Value and extract value from their data set. Now the way it needs to be framed is there is a business challenge that customers air trying to solve. The business challenge gets transformed into a data science problem. That data scientist is going toe work with the high technology, trained them on it. That data science problem gets to the data science solution on. Then it needs to be mapped to production deployment as a business solution. What happens? Ah, lot off. The time is the companies do not plan for output transition from all scale proof of concept that it a scientists are playing with, like a smaller set of data two, when it goes toe the large production deployment dealing with terabytes toe terabyte self data. Now that's where we come in. At their technologies, we have into end solutions for the, uh for the ai for pollution in the customers journeys starting from proof of concept to production. And it is all a seamless consular and very scalable. >>So if some of the challenges there are just starting with iterations. Thomas question for you as business development manager, those folks that John um I talked about the data scientists, the business. How are you helping them come together from the beginning so that when the POC is initiated, it actually can go on the right trajectory to be successful? >>No, that's a great point. And just to kind of build off of what Shonda my was talking about, You know, we call it that last mile, right? Like, Hey, I've got a great POC. How do I get into production? Well, if you have executive sponsorship and it's like, Hey, everybody was on board, but it's gonna take six months to a year. It's like, Whoa, you're gonna lose some momentum. So where we help our customers is, you know, by partnering with them to show them how to build, you know, from an i t. And infrastructure perspective what that ai architectural looks like, right? So we have multiple solutions around that, and at the end of the day, it's about just like Sean. Um, I was saying, You know, we may start off with a project that maybe it's only half a terabyte. Maybe it's 10 terabytes, but once you go into production, if it turns out to be three petabytes four petabytes. Nobody really, you know, has the infrastructure built unless they built on those solid practices. And that's where our solutions come in. So we can go from small scale laboratory all the way large scale production without having to move any of that data. Right? So, you know, at the heart of that is power scale and giving you that ability to scale your data and no more data migration so that you can handle one PC or multiple PCs as those models continue to improve as you start to move into production >>and I'm sticking with you 1st. 2nd 0, sorry. Trying to go ahead. >>So I was going to add that, uh, just like posthumous said right. So if you were a data scientist, you are working with this data science workstations, but getting the data from, uh, L M c our scales thes scale out platform and, uh, as it is growing from, you see two large kills production data can stay in place with the power scale platform. You can add notes, and it can grow to petabytes. And you can add in not just the workstations, but also our They'll power it, solve our switches building out our enter A I ready solutions are already solution for your production. Giving are very seamless experience from the data scientist with the i t. >>So China may will stick with you then. I'm curious to know in the last 6 to 7 months since 2020 has gone in a very different direction thing we all would have predicted our last Dell Technologies world together. What are you seeing? China. My in terms of acceleration or maybe different industries. What our customers needs, how they changed. I guess I should say in the in 2020. >>So in 2020 we're seeing the adoption off a I even more rapidly. Uh, if you think about customers ranging from like say, uh, media and entertainment industry toe, uh, the customer services off any organization to, uh the healthcare and life sciences with lots off genome analysts is going on in all of these places where we're dealing with large are datasets. We're seeing ah, lot off adoption foster processing off A. I R. Technologies, uh, giving with, say, the all the research that the's Biosciences organizations are happening. Uh, Thomas, I know like you are working with, like, a customer. So, uh, can you give us a little bit more example in there? >>Yes, one of the areas. You know, we're talking about 2021 of the things that we're seeing Mawr and Mawr is just the expansion of Just look at the need for customer support, right arm or folks working remotely their arm or folks that are learning remote. I know my child is going through virtual schools, So think about your I t organization and how Maney calls you're having now to expand. And so this is a great area where we're starting to see innovation within a I and model building to be ableto have you know, let's call it, you know, the next generation of chatbots rights. You can actually build these models off the data toe, augment those soup sports systems >>because you >>have two choices, right? You can either. You know, you you can either expand out your call center right for for we're not sure how long or you can use AI and analytics to help augment to help maybe answer some of those first baseline questions. The great thing about customers who are choosing power scale and Dell Technologies. Their partner is they already have. The resource is to be able to hold on to that data That's gonna help them train those models to help. >>So, Thomas, whenever we're talking about data, the explosions it brings to mind compliance. Protection, security. We've seen ransom where really skyrocket in 2020. Just you know, the other week there was the VA was hit. Um, I think there was also a social media Facebook instagram ticktock, 235 million users because there was an unsecured cloud database. So that vector is expanding. How can you help customers? Customers accelerate their AI projects? Well, ensuring compliance and protection and security of that data. >>Really? That's the sweet spot for power scale. We're talking with customers, right? You know, built on one FS with all the security features in mind. And I, too, came from the analytics world. So I remember in the early days of Hadoop, where, you know, as a software developer, we didn't need security, right? We you know, we were doing researching stuff, but then when we took it to the customer and and we're pushing to production, But what about all the security features. We needed >>the same thing >>for artificial intelligence, right? We want toe. We want to make sure that we're putting those security features and compliance is in. And that's where you know, from from an AI architecture perspective, by starting with one FS is at the heart of that solution. You can know that you're protecting for you know, all the enterprise features that you need, whether it be from compliance, thio, data strategy, toe backup and recovery as well. >>So when we're talking about big data volumes Chanda, mind we have to talk about the hyper scale er's talk to us about, you know, they each offer azure A W s Google cloud hundreds of AI services. So how does DEL help customers use the public cloud the data that's created outside of it and use all of those use that the right AI services to extract that value? >>Yeah. Now, as you mentioned, all of these hyper scholars are they differentiate with our office is like a i m l r Deep Learning Technologies, right? And as our customer, you want toe leverage based off all the, uh, all the cloud has to offer and not stuck with one particular cloud provider. However, we're talking about terabytes off data, right? So if you are happy with what doing service A from cloud provider say Google what you want to move to take advantage off another surface off from Asia? It comes with a very high English p a migration risk on time it will take to move the data itself. Now that's not good, right? As the customer, we should be able to live for it. Best off breed our cloud services for AI and for that matter, for anything across the board. Now, how we help customers is you can have all of your data say, in a managed, uh, managed cloud service provider running on power scale. But then you can connect from this managed cloud service provider directly toe any off the hyper scholars. You can connect toe aws, azure, Google Cloud and even, like even, uh, the in place analytics that power scale offers you can run. Uh, those, uh I mean, run those clouds AI services directly on that data simultaneously from these three, and I'll add like one more thing, right? Thes keep learning. Technologies need GPU power solvers, right? and cloud even within like one cloud is not homogeneous environment. Like sometimes you'll find a US East has or gp part solvers. But like you are in the West and the same for other providers. No, with our still our technologies cloud power scale for multi cloud our scale is sitting outside off those hyper scholars connected directly to our any off this on. Then you can burst into different clouds, take advantage off our spot. Instances on are like leverage. All the GP is not from one particular service provider part. All of those be our hyper scholars. So those are some examples off the work we're doing in the multi cloud world for a I >>So that's day. You're talking about data there. So powers failed for multi cloud for data that's created outside the public club. But Thomas, what about for data that's created inside the cloud? How does Del help with that? >>Yes. So, this year, we actually released a solution, uh, in conjunction with G C. P. So within Google Cloud, you can have power scale for one fs, right? And so that's that native native feature. So, you know, goes through all the compliance and all the features within being a part of that G c p natively eso counts towards your credits and your GP Google building as well. But it's still all the features that you have. And so we've been running some, actually, some benchmarks. So we've got a couple of white papers out there, that kind of detail. You know what we can do from an artificial intelligence perspective back to Sean Demise Example. We were just talking about, you know, being able to use more and more GPU. So we we've done that to run some of our AI benchmarks against that and then also, you know, jumped into the Hadoop space. But because you know, that's 11 area from a power scale, prospective customers were really interested. Um, and they have been for years. And then, really, the the awesome portion about this is for customers that are looking for a hybrid solution. Or maybe it's their first kickoff to it. So back Lisa to those compliance features that we were talking about those air still inherent within that native Google G C P one fs version, but then also for customers that have it on prim. You can use those same features to burst your data into, um, your isil on cluster using all the same native tools that you've been using for years within your enterprise. >>God, it's so starting out for power. Skill for Google Cloud Trying to get back to you Kind of wrapping things up here. What are some of the things that we're going to see next from Dell from an AI Solutions perspective? >>Yes. So we are working on many different interesting projects ranging from, uh, the latest, uh, in video Salford's that they have announced d d x a 100. And in fact, two weeks ago at GTC, uh, Syria announced take too far parts with, uh, it takes a 100 solvers. We're part off that ecosystem. And we are working with, uh, the leading, uh uh, solutions toe benchmark, our ai, uh, environments, uh, for all the storage, uh, ensuring, like we are providing, like, all the throughput and scalability that we have to offer >>Thomas finishing with you from the customer perspective. As we talked about so many changes this year alone as we approach calendar year 2021 what are some of the things that Dell is doing with its customers with its partners, the hyper scale er's and video, for example, Do you think customers are really going to be able to truly accelerate successful AI projects? >>Yeah. So the first thing I'd like to talk about is what we're doing with the D. G. S A 100. So this month that GTC you saw our solution for a reference architecture for the G s, a 100 plus power scale. So you talk about speed and how we can move customers insights. I mean, some of the numbers that we're seeing off of that are really a really amazing right. And so this is gives the customers the ability to still, you know, take all the features and use use I salon and one f s, um, like they have in the past, but now combined with the speed of the A 100 still be ableto speed up. How fast they're using those building out those deep learning models and then secondly, with that that gives them the ability to scale to. So there's some features inherent within this reference architecture that allow for you to make more use, right? So bring mawr data scientists and more modelers GP use because that's one thing you don't see Data scientist turning away right there always like, Hey, you know, I mean, this this project here needs needs a GPU. And so, you know, from a power scale one fs perspective, we want to be able to make sure that we're supporting that. So that as that data continues to grow, which, you know we're seeing is one of the large factors. Whenever we're talking about artificial intelligence is the scale for the data. We wanna them to be able to continue to build out that data consolidation area for all these multiple different workloads. That air coming in. >>Excellent, Thomas. Thanks for sharing that. Hopefully next time we get to see you guys in person and we can talk about a customer who has done something very successful with you guys. Kind of me. Always great to talk to you. Thank you for joining us. >>Thank you. Thank you >>for China. May Mandel and Thomas Henson. I'm Lisa Martin. You're watching the cubes Coverage of Dell Technologies, World 2020