(corporate electronic xylophone jingle intro) >> Good afternoon and welcome back to our very last segment of Tuesday's live broadcast here on theCUBE from AWS re:Invent in fabulous Las Vegas, Nevada. My name is Savannah Peterson and I am joined here by the brilliant Paul Gillin. Paul, end of our first day. You holding up, are you still feeling overwhelmed with fire hose... >> Savannah, yet my feet are killing me. (savannah laughs) >> Yeah, we've done so much walking in these chairs. >> 14,000 steps already today. It's not even dinner time. >> Hey, well, at least you've earned your dinner, Paul. I love that. I love that. I'm very excited about our next guests. We have Krishna and Sowmya joining us from Tata Consultancy Services. Now, I was impressed when I was doing my background research on you all. The Tata Group has locations in 150 different spots, 46 different countries. You have over 600,000 employees on the team. We are talking about absolutely massive scale here but, today we're going to be focused specifically on the Tata Consultancy Services. Sowmya, can you tell me what you all do? What is that team specifically in charge of? >> Yeah, TCS, first of all, thank you very much for inviting us. >> Savannah: Our pleasure. >> Maybe the last session but, we'll make it very lively. >> Savannah: It's going to be the best session. That's the best part of the day. >> Yes, that's the attitude. From a company standpoint, we are a 50 plus year old company. Part of the Tata group. We focus on IT services. We are categorized as industry verticals and we have horizontal services where AWS is one of the horizontal services that we have. And, when I talk about TCS, we focus a lot more on growth and transformation of our customers. That is one of the key objectives of the current company's growth, I would say. So, that is TCS in a nutshell. >> Extraordinarily important topic to be focused on right now. Growth, transformation, pretty much the core topics of the show. I know you're on the hospitality and transportation side of the business, which is very exciting. And, we're going to dig into that a little bit more. Krishna, you're overseeing the world. Tell us a little bit more about your role within the whole ecosystem. >> Yeah, thank you for the opportunity. Great meeting all of you. It's been awesome experience here. re:Invent is coming back, catching up, right? 50,000 people compared to 25,000 last year. So, great to see and meet all of you. Coming to my role, I am responsible for AWS Business Unit within TCS. That means I am responsible for anything that happens on cloud, on AWS. It's a Full Stack unit. I have the global responsibility. That's whether it's a applications, data, infrastructure, transformation that happens, as well as OT at the edge. So, that's my responsibility. >> Savannah: Well, I love talking about the edge. One of my favorite. >> Transformation is a theme of what you do. We heard that the pandemic accelerated digital transformation initiatives at many companies. How did you see the pandemic affecting your business, affecting the customers you were working with? >> Pandemic definitely kind of accelerated a lot of cloud adoption, right? A lot of companies initially focused on resiliency, coming back to handling the pandemic, the situation. But, it also drove a lot of innovation in the business models. They had to think on their feet, re-look at their business models, change the channels and that continued. Pandemic is thankfully gone by but, the transformation actually continued. The way that we actually see on cloud, especially transformation, it has evolved. What we call as Cloud 2.0. Now, cloud is actually more focused on future-proofing the businesses. And, the initial days it was more about future-proofing the technology and technology architecture. But, it has evolved to future-proofing businesses. That means implementing new business models, bringing in agility, measuring the business value. And, that's where we see a significant traction. >> So, it's not about technology then. It's not about infrastructure. >> It is about technology but, really delivering business value. It's about, how can I improve the customer experience? >> Well, can you give us a couple of examples of companies you work with that embody this idea? >> I can imagine in the travel and hospitality zone. Probably few communities more sensitive than when someone's having a disruption or frustration within that process. And, perhaps few time periods less chaotic than the last few years. Tell us about your experience and what you've seen. >> Absolutely. To answer your question, first of all, coming out of pandemic, right? Many customers in the travel and hospitality industry where legacy, did not modernize for the last decade or so because, there have been many ups and downs in the industry. So, during pandemic, post-pandemic, one of the the way they wanted to rebound was, can we do the transformation? First of all, cloud as a technology adoption, but, beyond that, how do customers derive value, business value? That is one of the key aspects of the old transformation. And, if you take, I can give a couple of examples. Avis Car Rental, they had monolith mainframe applications and, that was there for almost couple of decades, right? But, over a period of time, they were not able to have the availability of those applications. There were many outages. As a result, businesses could not do the bookings. Like OTAs, customers could not do the bookings, the application was not available most of the time. And, it's all legacy, right? So, that is where we all came in, TCS. How do we first of all, simplify the complexity of the landscape? That is one. Then, second is, modernize the legacy application. That's the second thing. Third is, how do you scale it? Because, everyone wants to go faster, right? How do you scale it? That is where we partnered with AWS as well, to bring in some specific solutions. One example for Avis', their Rent Shop. Because, of the lack of availability, because, it's monolith application and legacy application. It was not available. So, as a result, we partnered and we brought in our contextual knowledge of the car rental industry to kind of transform, move it to cloud. And, today, as a result of it, Avis was able to save millions of dollars from a MIB standpoint. Second, in terms of availability, that was 99.9% availability. As a result, they had a pick in their business revenue as well. So, this is one of the ways that its helped. The second example I want to quote is, United Airlines. Here again, we've been present for a long time. We have a deep industry knowledge of the airline industry. So, we brought in our airline contextual knowledge and the United landscape to bring in a TCS's solution that we developed. It's called the Aviana. It's an intelligent operations solution for the airline industry, which we have developed. It's on AWS as well, that is being implemented in United. As a result, the ground staff, they have to take decisions on the moment when there is a irregular operation. That could be flight delays, as a result, customers connections will be lost. >> Savannah: Baggage. >> Baggage, right? Baggage delays. >> So many variables. The complexity... >> exactly >> in this matrix is wild. >> So, leveraging the Aviana solution, the ground staff were able to take decisions based on exceptions. They were able to take decisions quickly so that, they improved the customer experience. I think that was one of the key successes for United in the recent times. So, those two are the examples that I would call where customers have the right business value. So, cloud was not just for technology. They all are deriving a lot of business value as well. I would say. >> How important do you think it is for companies facing these unique challenges and scaling to work with partners like TCS? And, I'm sure you would say very important, but, tell me a little bit more why it's so important and those core benefits that they're going to get. Krishna, let's start off with you. Yeah, let me take again the AWS cloud transformation, right? TCS has formed AWS Business Unit two years back. So, we are a covid baby in a way. We have been working with the AWS for more than a decade but, we formed a dedicated Full-Stack Unit to drive cloud transformation on AWS. In these last two years, we've grown three X and customers we have added 400 new customers we have added. >> Nicely done. Just want to see you there. That's huge. Especially during these times. Congratulations. >> So, it's basically about the scale that we bring in. What we have done as a differentiation is, if you look at the entire cloud journey, right from taking a decision which cloud is, right, all the way to the cloud migration modernization and running operations. So, we have built complete platform. AML based platforms, where we have taken our delivery wisdom and codified it onto these platforms. So, we support around thousand plus customers on AWS in varying capacity. All of that knowledge is codified and, that is what we bring to the table, to the customers. And, so, customers obviously appreciate that value that best practices that are coming. And, coupled with that, the industry knowledge that we have on banking, life sciences, healthcare, automotive. So, it's partly the IT, it is the industry transformation as well. Because, we are working on connected cars, for example, in automotive. We are working on accelerated drug development platforms. We're working on complete banks as a platform that we have. TCS has built on AWS. So, 400 customers are there. It's the complete banking and insurance platform. So, this is the combination of the technical expertize that is digitized using platforms, as well as the industry knowledge, is the reason why customers work with us on the cloud transformation. >> So, we're seeing you talk about the vertical industry knowledge. AWS also has its own vertical industry plays. How do you, I guess, coordinate with them or, do you compete with them or, do you stay out of each other's way? >> No, we actually collaborate aggressively. >> Savannah: I like that (laughs) >> Right, so, it's not.. >> Savannah: With vigor. >> With vigor. TCS supports approximately 14 verticals. With AWS, we went with the focused industry play. We said we look at financial services, travel, transportation, hospitality, healthcare, life sciences and automotive, to start with. And, we have Go Big plans with AWS. very focused. The collaboration is actually at the industry solutions because, AWS is a great platform, ever evolving, keeps you on on your toes to really adapt it. But, that is always going on, the collaboration. But, the industry, I'm actually glad AWS last year took a pivot on focusing on industries. Now, we talk the same language when we go in front of a board or a CEO or COO. Present it. We are talking about the future of the industry not just the future of the technology. So, it's a win-win. >> You are also developing products on top of AWS that are not industry verticals, that build on the platform. What kinds of products are those? >> For cloud transformation, for example, consulting. We have a product called Cloud Counsell. We have a decision engine on the data side. We have something called Cloud Foundation, Mason. CloudMason. It's just the foundation, right? And, entire migration and modernization factory. And, the last one on cloud operations is actually Cloud Exponence. So, these are time tested. You have Fortune 500 customers using this regularly actively leveraging that. And, these are all AWS in a well architecture framework certified. So, they work well and they're designed to work on cloud, not only in the native environment, but, also legacy environment. Because, enterprises is not just only native, cloud-native. There is a lot of legacy. Sowmya spoke about the mainframe model... >> So much legacy, we were talking about it. >> So, you have to have a combination of solutions. So, the platforms that we're building, the products we're building, work in both the environments. >> Yeah, and that agility and ability to help customers navigate that prioritization. I mean, there's so many options. We talk about how many new companies there are every year. New solutions. Our adoption of technology is accelerating. As, McKinsey said, we went through 10 years of technological evolution and workplace evolution over the first six months of the pandemic. So, really everything's moving at unprecedented velocity unlike ever before. We have a new game here on theCUBE specifically for this show. And, we are challenging our guests, prompting our guests, to give us a 30 second sizzly sound bite with your hot take on the most important themes of this year's show. Think of it as a thought leadership moment. Opportunity to plug if you really want it. Krishna, you've just given me the nod. I'm going to start with you first and then we'll then we'll pass it along, yeah >> Sure. I think on thought leadership, the way that on cloud, business value is the focus, not the technology. Technology is important, but business value is the focus. And, the way that I see it evolving is with quantum computing coming out more and more, becoming relevant, and Edge is actually becoming quite active as well. All this while on cloud, we focused on business value at the centralized place at the corporate. But, I think the real value of cloud is when you deliver the results, business results, where the customers consume it, that is at the edge. I think that's basically the combination of centralized and the edge is where the real value of cloud is, right. And, I also loud, I know you said 30 seconds but, give me 30 more seconds. >> I like your answer right now. So, I'm going to give you a little more time. Yeah, thank you. >> You've earned more time. (laughs) >> So, I like the way Adam said in the keynote, if you look at it broadly, I categorizes two things. There are a lot of offerings that are becoming comprehensive, like AWS Connect, bringing in workforce management into it, making it a complete end to end product. Similarly, Security Lake, all bringing in the entire security and compliance under one, similarly data. So, there are lot of things that he announced where it is an end to end comprehensiveness of the thing. But, what I love about is, what Amazon is known for, supply chain. So, they rolled out AWS Supply Chain offering. Walk Out technology. So, the Amazon proposition is actually being brought to AWS as a core proposition. I think that's very futuristic and I think we can see more and more customers, enterprise customers, adopting AWS more to drive transformation >> Badly needed right now. Supply chain resiliency. >> Supply chain really having its moment the last two years. File under two words. No one knew, many of us did who worked in it before this. And, here we are, soon as we lost our toilet paper, everyone's freaked out. I love that you talked about business value and also that the end customer is on the edge and, everyone kind of forgets we are essentially the edge device. This is the edge device, it's all around us. And, all the technology that we're all using that you're even talking about is built right inside here from my airlines app to my car rentals to all of it. All right Sowmya, give us your 30 second hot take, roughly. >> Taking the cue from Krishna, right? Today, things are available on AWS Marketplace. So, tomorrow, somebody wants to start an airline, they just have to come and plug and play the apps that are available in the marketplace. Especially your supply chain. The Amazon is known for that. And, a small and medium business they want to start something, right, a .com. It's very easy. So, that's something that we are all looking for. The future is going to be very, very bright and great for the businesses, is what I would say because, most of it could be plug and play with all the solutions. >> Paul: It's already been built. >> On the cloud, so, we are looking forward to it. The second thing I would talk about is, we have to take it to scale. How more and more people can leverage AWS, right? The talent is very important and, that is where partners like us focus on re-scaling our talent. We have 600,000 people, right? We are not just... >> 600,000 people! That's basically as many people live in the San Francisco Bay area for contexts for our listeners. It's how many people work for Walmart? >> It's 1.2 million in Walmart? >> Is it really? >> It is, yes, yes. That's work for Walmart, sidebar. >> So from that standpoint, as the company, we are focusing on re-skilling, up-skilling our talent in order to work AWS cloud and so on, so, that they can go and support our customers. That is something that is very important and that's going to be the future as well. Bring it to scale, go faster. >> I love that you just touched on the fact that you essentially have to practice what you preach because, you've got to think about those 600,000 people in a 100 locations across 40 plus different countries. I love it. Sowmya, I'm going to close on that note. The future is bright, just like your fabulous blazer. >> Thank you so much. Krishna, Sowmya, thank you so much for being here with us. We can't wait to see what happens next, who you help next, and how Tata continues to transform. Thank all of you for tuning in today. A full jam packed day of coverage live here from Las Vegas, Nevada. We are at AWS re:Invent with Paul Gillin. I'm Savannah Peterson. We're theCUBE, the leader in High-Tech Coverage. (corporate electronic xylophone jingle outro)

(upbeat music) >> Welcome back. Day two of theCUBE's coverage of re:MARS in Las Vegas. Amazon re:MARS, it's part of the Re Series they call it at Amazon. re:Invent is their big show, re:Inforce is a security show, re:MARS is the new emerging machine learning automation, robotics, and space. The confluence of machine learning powering a new industrial age and inflection point. I'm John Furrier, host of theCUBE. We're here to break it down for another wall to wall coverage. We've got a great guest here, CUBE alumni from our AWS startup showcase, Krishna Gade, founder and CEO of fiddler.ai. Welcome back to theCUBE. Good to see you. >> Great to see you, John. >> In person. We did the remote one before. >> Absolutely, great to be here, and I always love to be part of these interviews and love to talk more about what we're doing. >> Well, you guys have a lot of good street cred, a lot of good word of mouth around the quality of your product, the work you're doing. I know a lot of folks that I admire and trust in the AI machine learning area say great things about you. A lot going on, you guys are growing companies. So you're kind of like a startup on a rocket ship, getting ready to go, pun intended here at the space event. What's going on with you guys? You're here. Machine learning is the centerpiece of it. Swami gave the keynote here at day two and it really is an inflection point. Machine learning is now ready, it's scaling, and some of the examples that they were showing with the workloads and the data sets that they're tapping into, you know, you've got CodeWhisperer, which they announced, you've got trust and bias now being addressed, we're hitting a level, a new level in ML, ML operations, ML modeling, ML workloads for developers. >> Yep, yep, absolutely. You know, I think machine learning now has become an operational software, right? Like you know a lot of companies are investing millions and billions of dollars and creating teams to operationalize machine learning based products. And that's the exciting part. I think the thing that that is very exciting for us is like we are helping those teams to observe how those machine learning applications are working so that they can build trust into it. Because I believe as Swami was alluding to this today, without actually building trust into AI, it's really hard to actually have your business users use it in their business workflows. And that's where we are excited about bringing their trust and visibility factor into machine learning. >> You know, a lot of us all know what you guys are doing here in the ecosystem of AWS. And now extending here, take a minute to explain what Fiddler is doing for the folks that are in the space, that are in discovery mode, trying to understand who's got what, because like Swami said on stage, it's a full-time job to keep up on all the machine learning activities and tool sets and platforms. Take a minute to explain what Fiddler's doing, then we can get into some, some good questions. >> Absolutely. As the enterprise is taking on operationalization of machine learning models, one of the key problems that they run into is lack of visibility into how those models perform. You know, for example, let's say if I'm a bank, I'm trying to introduce credit risk scoring models using machine learning. You know, how do I know when my model is rejecting someone's loan? You know, when my model is accepting someone's loan? And why is it doing it? And I think this is basically what makes machine learning a complex thing to implement and operationalize. Without this visibility, you cannot build trust and actually use it in your business. With Fiddler, what we provide is we actually open up this black box and we help our customers to really understand how those models work. You know, for example, how is my model doing? Is it accurately working or not? You know, why is it actually rejecting someone's loan application? We provide these both fine grain as well as coarse grain insights. So our customers can actually deploy machine learning in a safe and trustworthy manner. >> Who is your customer? Who you're targeting? What persona is it, the data engineer, is it data science, is it the CSO, is it all the above? >> Yeah, our customer is the data scientist and the machine learning engineer, right? And we usually talk to teams that have a few models running in production, that's basically our sweet spot, where they're trying to look for a single pane of glass to see like what models are running in their production, how they're performing, how they're affecting their business metrics. So we typically engage with like head of data science or head of machine learning that has a few machine learning engineers and data scientists. >> Okay, so those people that are watching, you're into this, you can go check it out. It's good to learn. I want to get your thoughts on some trends that I see emerging, and I want to get your reaction to those. Number one, we're seeing the cloud scale now and integration a big part of things. So the time to value was brought up on stage today, Swami kind of mentioned time to value, showed some benchmark where they got four hours, some other teams were doing eight weeks. Where are we on the progression of value, time to value, and on the scale side. Can you scope that for me? >> I mean, it depends, right? You know, depending upon the company. So for example, when we work with banks, for them to time to operationalize a model can take months actually, because of all the regulatory procedures that they have to go through. You know, they have to get the models reviewed by model validators, model risk management teams, and then they audit those models, they have to then ship those models and constantly monitor them. So it's a very long process for them. And even for non-regulated sectors, if you do not have the right tools and processes in place, operationalizing machine learning models can take a long time. You know, with tools like Fiddler, what we are enabling is we are basically compressing that life cycle. We are helping them automate like model monitoring and explainability so that they can actually ship models more faster. Like you get like velocity in terms of shipping models. For example, one of the growing fintech companies that started with us last year started with six models in production, now they're running about 36 models in production. So it's within a year, they were able to like grow like 10x. So that is basically what we are trying to do. >> At other things, we at re:MARS, so first of all, you got a great product and a lot of markets that grow onto, but here you got space. I mean, anyone who's coming out of college or university PhD program, and if they're into aero, they're going to be here, right? This is where they are. Now you have a new core companies with machine learning, not just the engineering that you see in the space or aerospace area, you have a new engineering. Now I go back to the old days where my parents, there was Fortran, you used Fortran was Lingua Franca to manage the equipment. Little throwback to the old school. But now machine learning is companion, first class citizen, to the hardware. And in fact, and some will say more important. >> Yep, I mean, machine learning model is the new software artifact. It is going into production in a big way. And I think it has two different things that compare to traditional software. Number one, unlike traditional software, it's a black box. You cannot read up a machine learning model score and see why it's making those predictions. Number two, it's a stochastic entity. What that means is it's predictive power can wane over time. So it needs to be constantly monitored and then constantly refreshed so that it's actually working in tech. So those are the two main things you need to take care. And if you can do that, then machine learning can give you a huge amount of ROI. >> There is some practitioner kind of like craft to it. >> Correct. >> As you said, you got to know when to refresh, what data sets to bring in, which to stay away from, certainly when you get to the bias, but I'll get to that in a second. My next question is really along the lines of software. So if you believe that open source will dominate the software business, which I do, I mean, most people won't argue. I think you would agree with that, right? Open source is driving everything. If everything's open source, where's the differentiation coming from? So if I'm a startup entrepreneur or I'm a project manager working on the next Artemis mission, I got to open source. Okay, there's definitely security issues here. I don't want to talk about shift left right now, but like, okay, open source is everything. Where's the differentiation, where do I have the proprietary edge? >> It's a great question, right? So I used to work in tech companies before Fiddler. You know, when I used to work at Facebook, we would build everything in house. We would not even use a lot of open source software. So there are companies like that that build everything in house. And then I also worked at companies like Twitter and Pinterest, which are actually used a lot of open source, right? So now, like the thing is, it depends on the maturity of the organization. So if you're a Facebook or a Google, you can build a lot of things in house. Then if you're like a modern tech company, you would probably leverage open source, but there are lots of other companies in the world that still don't have the talent pool to actually build, take things from open source and productionize it. And that's where the opportunity for startups comes in so that we can commercialize these things, create a great enterprise experience, so actually operationalize things for them so that they don't have to do it in house for them. And that's the advantage working with startups. >> I don't want to get all operating systems with you on theory here on the stage here, but I will have to ask you the next question, which I totally agree with you, by the way, that's the way to go. There's not a lot of people out there that are peaked. And that's just statistical and it'll get better. Data engineering is really narrow. That is like the SRE of data. That's a new role emerging. Okay, all the things are happening. So if open source is there, integration is a huge deal. And you start to see the rise of a lot of MSPs, managed service providers. I run Kubernetes clusters, I do this, that, and the other thing. So what's your reaction to the growth of the integration side of the business and this role of new services coming from third parties? >> Yeah, absolutely. I think one of the big challenges for a chief data officer or someone like a CTO is how do they devise this infrastructure architecture and with components, either homegrown components or open source components or some vendor components, and how do they integrate? You know, when I used to run data engineering at Pinterest, we had to devise a data architecture combining all of these things and create something that actually flows very nicely, right? >> If you didn't do it right, it would break. >> Absolutely. And this is why it's important for us, like at Fiddler, to really make sure that Fiddler can integrate to all varies of ML platforms. Today, a lot of our customers use machine learning, build machine learning models on SageMaker. So Fiddler nicely integrate with SageMaker so that data, they get a seamless experience to monitor their models. >> Yeah, I mean, this might not be the right words for it, but I think data engineering as a service is really what I see you guys doing, as well other things, you're providing all that. >> And ML engineering as a service. >> ML engineering as a- Well it's hard. I mean, it's like the hard stuff. >> Yeah, yeah. >> Hear, hear. But that has to enable. So you as a business entrepreneur, you have to create a multiple of value proposition to your customers. What's your vision on that? What is that value? It has to be a multiple, at least 5 to 10. >> I mean, the value is simple, right? You know, if you have to operationize machine learning, you need visibility into how these things work. You know, if you're CTO or like chief data officer is asking how is my model working and how is it affecting my business? You need to be able to show them a dashboard, how it's working, right? And so like a data scientist today struggles to do this. They have to manually generate a report, manually do this analysis. What Fiddler is doing them is basically reducing their work so that they can automate these things and they can still focus on the core aspect of model building and data preparation and this boring aspect of monitoring the model and creating reports around the models is automated for them. >> Yeah, you guys got a great business. I think it's a lot of great future there and it's only going to get bigger. Again, the TAM's going to expand as the growth rising tide comes in. I want to ask you on while we're on that topic of rising tides, Dave Malik and I, since re:Invent last year have been kind of kicked down around this term that we made up called supercloud. And supercloud was a word that came out of these clouds that were not Amazon hyperscalers. So Snowflake, Buildman Sachs, Capital One, you name it, they're building massive proprietary value on top of the CapEx of Amazon. Jerry Chen at Greylock calls it castles in the cloud. You can create these moats. >> Yeah, right. >> So this is a phenomenon, right? And you land on one, and then you go to the others. So the strategies, everyone goes to Amazon first, and then hits Azure and GCP. That then creates this kind of multicloud so, okay, so super cloud's kind of happening, it's a thing. Charles Fitzgerald will disagree, he's a platformer, he says he's against the term. I get why, but he's off base a little. We can't wait to debate him on that. So superclouds are happening, but now what do I do about multicloud, because now I understand multicloud, I have this on that cloud, integrating across clouds is a very difficult thing. >> Krishna: Right, right, right. >> If I'm Snowflake or whatever, hey, I'll go to Azure, more TAM expansion, more market. But are people actually working together? Are we there yet? Where it's like, okay, I'm going to re-operationalize this code base over here. >> I mean, the reality of it, enterprise wants optionality, right? I think they don't want to be locked in into one particular cloud vendor on one particular software. And therefore you actually have in a situation where you have a multicloud scenario where they want to have some workloads in Amazon, some workloads in Azure. And this is an opportunity for startups like us because we are cloud agnostic. We can monitor models wherever you have. So this is where a lot of our customers, they have some of their models are running in their data centers and some of their models running in Amazon. And so we can provide a universal single pan of glass, right? So we can basically connect all of those data and actually showcase. I think this is an opportunity for startups to combine the data streams come from various different clouds and give them a single pain of experience. That way, the sort of the where is your data, where are my models running, which cloud are there, is all abstracted out from the customer. Because at the end of the day, enterprises will want optionality. And we are in this multicloud. >> Yeah, I mean, this reminds me of the interoperability days back when I was growing into the business. Everything was interoperability and OSI and the standards came out, but what's your opinion on openness, okay? There's a kneejerk reaction right now in the market to go silo on your data for governance or whatever reasons, but yet machine learning gurus and experts will say, "Hey, you want to horizon horizontal scalability and have the best machine learning models, you've got to have access to data and fast in real time or near real time." And the antithesis is siloing. >> Krishna: Right, right, right. >> So what's the solution? Customers control the data plane and have a control plane that's... What do customers do? It's a big challenge. >> Yeah, absolutely. I think there are multiple different architectures of ML, right, you know? We've seen like where vendors like us used to deploy completely on-prem, right? And they still do it, we still do it in some customers. And then you had this managed cloud experience where you just abstract out the entire operations from the customer. And then now you have this hybrid experience where you split the control plane and data plane. So you preserve the privacy of the customer from the data perspective, but you still control the infrastructure, right? I don't think there's a right answer. It depends on the product that you're trying to solve. You know, Databricks is able to solve this control plane, data plane split really well. I've seen some other tools that have not done this really well. So I think it all depends upon- >> What about Snowflake? I think they a- >> Sorry, correct. They have a managed cloud service, right? So predominantly that's their business. So I think it all depends on what is your go to market? You know, which customers you're talking to? You know, what's your product architecture look like? You know, from Fiddler's perspective today, we actually have chosen, we either go completely on-prem or we basically provide a managed cloud service and that's actually simpler for us instead of splitting- >> John: So it's customer choice. >> Exactly. >> That's your position. >> Exactly. >> Whoever you want to use Fiddler, go on-prem, no problem, or cloud. >> Correct, or cloud, yeah. >> You'll deploy and you'll work across whatever observability space you want to. >> That's right, that's right. >> Okay, yeah. So that's the big challenge, all right. What's the big observation from your standpoint? You've been on the hyperscaler side, your journey, Facebook, Pinterest, so back then you built everything, because no one else had software for you, but now everybody wants to be a hyperscaler, but there's a huge CapEx advantage. What should someone do? If you're a big enterprise, obviously I could be a big insurance, I could be financial services, oil and gas, whatever vertical, I want a supercloud, what do I do? >> I think like the biggest advantage enterprise today have is they have a plethora of tools. You know, when I used to work on machine learning way back in Microsoft on Bing Search, we had to build everything. You know, from like training platforms, deployment platforms, experimentation platforms. You know, how do we monitor those models? You know, everything has to be homegrown, right? A lot of open source also did not exist at the time. Today, the enterprise has this advantage, they're sitting on this gold mine of tools. You know, obviously there's probably a little bit of tool fatigue as well. You know, which tools to select? >> There's plenty of tools available. >> Exactly, right? And then there's like services available for you. So now you need to make like smarter choices to cobble together this, to create like a workflow for your engineers. And you can really get started quite fast, and actually get on par with some of these modern tech companies. And that is the advantage that a lot of enterprises see. >> If you were going to be the CTO or CEO of a big transformation, knowing what you know, 'cause you just brought up the killer point about why it's such a great time right now, you got platform as a service and the tooling essentially reset everything. So if you're going to throw everything out and start fresh, you're basically brewing the system architecture. It's a complete reset. That's doable. How fast do you think you could do that for say a large enterprise? >> See, I think if you set aside the organization processes and whatever kind of comes in the friction, from a technology perspective, it's pretty fast, right? You can devise a data architecture today with like tools like Kafka, Snowflake and Redshift, and you can actually devise a data architecture very clearly right from day one and actually implement it at scale. And then once you have accumulated enough data and you can extract more value from it, you can go and implement your MLOps workflow as well on top of it. And I think this is where tools like Fiddler can help as well. So I would start with looking at data, do we have centralization of data? Do we have like governance around data? Do we have analytics around data? And then kind of get into machine learning operations. >> Krishna, always great to have you on theCUBE. You're great masterclass guest. Obviously great success in your company. Been there, done that, and doing it again. I got to ask you, since you just brought that up about the whole reset, what is the superhero persona right now? Because it used to be the full stack developer, you know? And then it's like, then I call them, it didn't go over very well in theCUBE, the half stack developer, because nobody wants to be a half stack anything, a half sounds bad, worse than full. But cloud is essentially half a stack. I mean, you got infrastructure, you got tools. Now you're talking about a persona that's going to reset, look at tools, make selections, build an architecture, build an operating environment, distributed computing operating. Who is that person? What's that persona look like? >> I mean, I think the superhero persona today is ML engineering. I'm usually surprised how much is put on an ML engineer to do actually these days. You know, when I entered the industry as a software engineer, I had three or four things in my job to do, I write code, I test it, I deploy it, I'm done. Like today as an ML engineer, I need to worry about my data. How do I collect it? I need to clean the data, I need to train my models, I need to experiment with what it is, and to deploy them, I need to make sure that they're working once they're deployed. >> Now you got to do all the DevOps behind it. >> And all the DevOps behind it. And so I'm like working halftime as a data scientist, halftime as a software engineer, halftime as like a DevOps cloud. >> Cloud architect. >> It's like a heroic job. And I think this is why this is why obviously these jobs are like now really hard jobs and people want to be more and more machine learning >> And they get paid. >> engineering. >> Commensurate with the- >> And they're paid commensurately as well. And this is where I think an opportunity for tools like Fiddler exists as well because we can help those ML engineers do their jobs better. >> Thanks for coming on theCUBE. Great to see you. We're here at re:MARS. And great to see you again. And congratulations on being on the AWS startup showcase that we're in year two, episode four, coming up. We'll have to have you back on. Krishna, great to see you. Thanks for coming on. Okay, This is theCUBE's coverage here at re:MARS. I'm John Furrier, bringing all the signal from all the noise here. Not a lot of noise at this event, it's very small, very intimate, a little bit different, but all on point with space, machine learning, robotics, the future of industrial. We'll back with more coverage after the short break. >> Man: Thank you John. (upbeat music)

(gentle music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase. The theme is Data as Code; The Future of Enterprise Data and Analytics. This is the season two, episode two of the ongoing series of covering the exciting startups in the AWS ecosystem around data analytics and cloud computing. I'm your host, John Furrier. Today we're joined by great guests here. Three guests. Wen Phan, who's a Director of Product Management at Ahana, Satyam Krishna, Engineering Manager at Blinkit, and we have Akshay Agarwal, Senior Engineer at Blinkit as well. We're going to get into the relationship there. Let's get into. We're going to talk about how Blinkit's using open data lake, data house with Presto on AWS. Gentlemen, thanks for joining us. >> Thanks for having us. >> So we're going to get into the deep dive on the open data lake, but I want to just quickly get your thoughts on what it is for the folks out there. Set the table. What is the open data lakehouse? Why it is important? What's in it for the customers? Why are we seeing adoption around this because this is a big story. >> Sure. Yeah, the open data lakehouse is really being able to run a gamut of analytics, whether it be BI, SQL, machine learning, data science, on top of the data lake, which is based on inexpensive, low cost, scalable storage. And more importantly, it's also on top of open formats. And this to the end customer really offers a tremendous range of flexibility. They can run a bunch of use cases on the same storage and great price performance. >> You guys have any other thoughts on what's your reaction to the lakehouse? What is your experience with it? What's going on with Blinkit? >> No, I think for us also, it has been the primary driver of how as a company we have shifted our completely delivery model from us delivering in one day to someone who is delivering in 10 minutes, right? And a lot of this was made possible by having this kind of architecture in place, which helps us to be more open-source, more... where the tools are open-source, we have an open table format which helps us be very modular in nature, meaning we can pick solutions which works best for us, right? And that is the kind of architecture that we want to be in. >> Awesome. Wen, you know last time we chat with Ahana, we had a great conversation around Presto, data. The theme of this episode is Data as Code, which is interesting because in all the conversations in these episodes all around developers, which administrators are turning into developers, there's a developer vibe with data. And with opensource, it's software. Now you've got data taking a similar trajectory as how software development was with code, but the people running data they're not developers, they're administrators, they're operators. Now they're turning into DataOps. So it's kind of a similar vibe going on with branches and taking stuff out of and putting it back in, and testing it. Datasets becoming much more stable, iterating on machine learning algorithm. This is a movement. What's your guys reaction before we get into the relationships here with you guys. But, what's your reaction to this Data as Code movement? >> Yeah, so I think the folks at Blinkit are doing a great job there. I mean, they have a pretty compact data engineering team and they have some pretty stringent SLAs, as well as in terms of time to value and reliability. And what that ultimately translates for them is not only flexibility but reliability. So they've done some very fantastic work on a lot of automation, a lot of integration with code, and their data pipelines. And I'm sure they can give the details on that. >> Yes. Satyam and Akshay, you guys are engineers' software, but this is becoming a whole another paradigm where the frontline coding and or work or engineer data engineering is implementing the operations as well. It's kind of like DevOps for data. >> For sure. Right. And I think whenever you're working, even as a software engineer, the understanding of business is equally important. You cannot be working on something and be away from business, right? And that's where, like I mentioned earlier, when we realized that we have to completely move our stack and start giving analytics at 10 minutes, right. Because when you're delivering in 10 minutes, your leaders want to take decisions in your real-time. That means you need to move with them. You need to move with business. And when you do that, the kind of flexibility these softwares give is what enables the businesses at the end of the day. >> Awesome. This is the really kind of like, is there going to be a book called agile data warehouses? I don't think so. >> I think so. (laughing) >> The agile cloud data. This is cool. So let's get into what you guys do. What is Blinkit up to? What do you guys do? Can you take a minute to explain the company and your product? >> Sure. I'll take that. So Blinkit is India's biggest 10 minute delivery platform. It pioneered the delivery model in the country with over 10 million Indian shopping on our platform, ranging from everything: grocery staples, vegetables, emergency services, electronics, and much more, right. It currently delivers over 200,000 orders every day, and is in a hurry to bring the future of farmers to everyone in India. >> What's the relationship with Ahana and Blinkit? Wen, what's the tie in? >> Yeah, so Blinkit had a pretty well formed stack. They needed a little bit more flexibility and control. They thought a managed service was the way to go. And here at Ahana, we provide a SaaS managed service for Presto. So they engaged us and they evaluated our offering. And more importantly, we're able to partner. As a early stage startup, we really rely on very strong partners with great use cases that are willing to collaborate. And the folks at Blinkit have been really great in helping us push our product, develop our product. And we've been very happy about the value that we've been able to deliver to them as well. >> Okay. So let's unpack the open data lakehouse. What is it? What's under the covers? Let's get into it. >> Sure. So if bring up a slide. Like I said before, it's really a paradigm on being able to run a gamut of analytics on top of the open data lake. So what does that mean? How did it come about? So on the left hand side of the slide, we are coming out of this world where for the last several decades, the primary workhorse for SQL based processing and reporting and dashboarding use cases was really the data warehouse. And what we're seeing is a shift due to the trends in inexpensive scalable storage, cloud storage. The proliferation of open formats to facilitate using this storage to get certain amounts of reliability and performance, and the adoption of frameworks that can operate on top of this cloud data lake. So while here at Ahana, we're primarily focused on SQL workloads and Presto, this architecture really allows for other types of frameworks. And you see the ML and AI side. And like to Satyam's point earlier, offers a great amount of flexibility modularity for many use cases in the cloud. So really, that's really the lakehouse, and people like it for the performance, the openness, and the price performance. >> How's the open-source open side of it playing in the open-source? It's kind of open formats. What is the open-source angle on this because there's a lot of different approaches. I'm hearing open formats. You know, you have data stores which are a big part of seeing that. You got SQL, you mentioned SQL. There's got a mishmash of opportunities. Is it all coexisting? Is it one tool to rule the world or is it interchangeable? What's the open-source angle? >> There's multiple angles and I'll let definitely Satyam add to what I'm saying. This was definitely a big piece for Blinkit. So on one hand, you have the open formats. And what really the open formats enable is multiple compute engines to work on that data. And that's very huge. 'Cause it's open, you're not locked in. I think the other part of open that is important and I think it was important to Blinkit was the governance around that. So in particular Presto is governed by the Linux Foundation. And so, as a customer of open-source technology, they want some assurances for things like how's it governed? Is the license going to change? So there's that aspect of openness that I think is very important. >> Yeah. Blinkit, what's the data strategy here with lakehouse and you guys? Why are you adopting this type of architecture? >> So adding to what... Yeah, I think adding to Wen said, right. When we are thinking in terms of all these OpenStacks, you have got these open table formats, everything which is deployed over cloud, the primary reason there is modularity. It's as simple as that, right. You can plug and play so many different table formats from one thing to another based on the use case that you're trying to serve, so that you get the most value out of data. Right? I'll give you a very simple example. So for us we use... not even use one single table format. It's not that one thing solves for everything, right? We use both Hudi and Iceberg to solve for different use cases. One is good for when you're working for a certain data site. Icebergs works well when you're in the SQL kind of interface, right. Hudi's still trying to reach there. It's going to go there very soon. So having the ability to plug and play different formats based on the use case helps you to grow faster, helps you to take decisions faster because you now you're not stuck on one thing. They will have to implement it. Right. So I think that's what it is great about this data lake strategy. Keeping yourself cost effective. Yeah, please. >> So the enablement is basically use case driven. You don't have to be rearchitecturing for use cases. You can simply plug can play based on what you need for the use case. >> Yeah. You can... and again, you can focus on your business use case. You can figure out what your business users need and not worry about these things because that's where Presto comes in, helps you stitch that data together with multiple data formats, give you the performance that you need and it works out the best there. And that's something that you don't get to with traditional warehouse these days. Right? The kind of thing that we need, you don't get that. >> I do want to add. This is just to riff on what Satyam said. I think it's pretty interesting. So, it really allowed him to take the best-of-breed of what he was seeing in the community, right? So in the case of table formats, you've got Delta, you've got Hudi, you've got Iceberg, and they all have got their own roadmap and it's kind of organic of how these different communities want to evolve, and I think that's great, but you have these end consumers like Blinkit who have different maybe use cases overlapping, and they're not forced to pick one. When you have an open architecture, they can really put together best-of-breed. And as these projects evolve, they can continue to monitor it and then make decisions and continue to remain agile based on the landscape and how it's evolving. >> So the agility is a key point. Flexibility and agility, and time to valuing with your data. >> Yeah. >> All right. Wen, I got to get in to why the Presto is important here. Where does that fit in? Why is Presto important? >> Yeah. For me, it all comes down to the use cases and the needs. And reporting and dashboarding is not going to go away anytime soon. It's a very common use case. Many of our customers like Blinkit come to us for that use case. The difference now is today, people want to do that particular use case on top of the modern data lake, on top of scalable, inexpensive, low cost storage. Right? In addition to that, there's a need for this low latency interactive ability to engage with the data. This is often arises when you need to do things in a ad hoc basis or you're in the developmental phase of building things up. So if that's what your need is. And latency's important and getting your arms around the problems, very important. You have a certain SLA, I need to deliver something. That puts some requirements in the technology. And Presto is a perfect for that ideal use case. It's ideal for that use case. It's distributed, it's scalable, it's in memory. And so it's able to really provide that. I think the other benefit for Presto and why we're bidding on Presto is it works well on the data lakes, but you have to think about how are these organizations maturing with this technology. So it's not necessarily an all or nothing. You have organizations that have maybe the data lake and it's augmented with other analytical data stores like Snowflake or Redshift. So Presto also... a core aspect is its ability to federate or connect and query across different data sources. So this can be a permanent thing. This could also be a transitionary thing. We have some customers that are moving and slowly shifting their data portfolio from maybe all data warehouse into 80% data lake. But it gives that optionality, it gives that ability to transition over a timeframe. But for all those reasons, the latency, the scalability, the federation, is why Presto for this particular use case. >> And you can connect with other databases. It can be purpose built database, could be whatever. Right? >> Sure. Yes, yes. Presto has a very pluggable architecture. >> Okay. Here's the question for the Blinkit team? Why did you choose Presto and what led you to Ahana? >> So I'll take this better, over this what Presto sits well in that reach is, is how it is designed. Like basically, Presto decouples your storage with the compute. Basically like, people can use any storage and Presto just works as a query engine for them. So basically, it has a constant connectors where you can connect with a real-time databases like Pinot or a Druid, along with your warehouses like Redshift, along with your data lake that's like based on Hudi or Iceberg. So it's like a very landscape that you can use with the Presto. And consumers like the analytics doesn't need to learn the SQL or different paradigms of the querying for different sources. They just need to learn a single source. And, they get a single place to consume from. They get a single consumer on their single destination to write on also. So, it's a homologous architecture, which allows you to put a central security like which Presto integrates. So it's also based on open architecture, that's Apache engine. And it has also certain innovative features that you can see based on caching, which reduces a lot of the cost. And since you have further decoupled your storage with the compute, you can further reduce your cost, because now the biggest part of our tradition warehouse is a storage. And the cost goes massively upwards with the amount of data that you've added. Like basically, each time that you add more data, you require more storage, and warehouses ask you to write the data in their own format. Over here since we have decoupled that, the storage cost have gone down. It's literally that your cost that you are writing, and you just pay for the compute, and you can scale in scale out based on the requirements. If you have high traffic, you scale out. If you have low traffic, you scale in. So all those. >> So huge cost savings. >> Yeah. >> Yeah. Cost effectiveness, for sure. >> Cost effectiveness and you get a very good price value out of it. Like for each query, you can estimate what's the cost for you based on that tracking and all those things. >> I mean, if you think about the other classic Iceberg and what's under the water you don't know, it's the hidden cost. You think about the tooling, right, and also, time it takes to do stuff. So if you have flexibility on choice, when we were riffing on this last time we chatted with you guys and you brought it up earlier around, you can have the open formats to have different use cases in different tools or different platforms to work on it. Redshift, you can use Redshift here, or use something over there. You don't have to get locking >> Absolutely. >> Satyam & Akshay: Yeah. >> Locking is a huge problem. How do you guys see that 'cause sounds like here there's not a lot of locking. You got the open formats, and you got choice. >> Yeah. So you get best of the both worlds. Like you get with Ahana or with the Presto, you can get the best of the both worlds. Since it's cloud native, you can easily deploy your clusters very easily within like five minutes. Your cluster is up, you can start working on it. You can deploy multiple clusters for multiple teams. You get also flexibility of adding new connectors since it's open and further it's also much more secure since it's based on cloud native. So basically, you can control your security endpoints very well. So all those things comes in together with this architecture. So you can definitely go more on the lakehouse architecture than warehousing when you want to deliver data value faster. And basically, you get the much more high value out of your data in a sorted template. >> So Satyam, it sounds like the old warehousing was like the application person, not a lot of usage, old, a lot of latency. Okay. Here and there. But now you got more speed to deploy clusters, scale up scale down. Application developers are as everyone. It's not one person. It's not one group. It's whenever you want. So, you got speed. You got more diversity in the data opportunities, and your coding. >> Yeah. I think data warehouses are a way to start for every organization who is getting into data. I don't think data warehousing is still a solution and will be a solution for a lot of teams which are still getting into data. But as soon as you start scaling, as you start seeing the cost going up, as you start seeing the number of use cases adding up, having an open format definitely helps. So, I would say that's where we are also heading into and that's how our journey as well started with Presto as well, why we even thought about Ahana, right. >> (John chuckles) >> So, like you mentioned, one of the things that happened was as we were moving to the lakehouse and the open table format, I think Ahana is one of the first ones in the market to have Hudi as a first class citizen completely supported with all the things which are not even present at the time of... even with Presto, right. So we see Ahana working behind the scenes, improving even some of the things already over the open-source ecosystem. And that's where we get the most value out of Ahana as well. >> This is the convergence of open-source magic and commercialization. Wen, because you think about Data as Code, reminds me, I hear, "Data warehouse, it's not going to go away." But you got cloud scale or scale. It reminds me of the old, "Oh yeah, I have a data center." Well, here comes the cloud. So, doesn't really kill the data center, although Amazon would say that the data center's going to be eliminated. No, you just use it for whatever you need it for. You use it for specific use cases, but everyone, all the action goes to the cloud for scale. The same things happen with data, and look at the open-source community. It's kind of coming together. Data as Code is coming together. >> Yeah, absolutely. >> Absolutely. >> I do want to again to connect on another dot in terms of cost and that. You know, we've been talking a little bit about price performance, but there's an implicit cost, and I think this was also very important to Blinkit, and also why we're offering a managed service. So one piece of it. And it really revolves around the people, right? So outside of the technology, the performance. One thing that Akshay brought up and it's another important piece that I should have highlighted a little bit more is, Presto exposes the ability to interact your data in a widely adopted way, which is basically ANSI SQL. So the ability for your practitioners to use this technology is huge. That's just regular Presto. In terms of a managed service, the guys at Blinkit are a great high performing team, but they have to be very efficient with their time and what they manage. And what we're trying to do is provide leverage for them. So take a lot of the heavy lifting away, but at the same time, figuring out the right things to expose so that they have that same flexibility. And that's been the balancing point that we've been trying to balance at Ahana, but that goes back to cost. How do I total cost of ownership? And that not doesn't include just the actual querying processing time, but the ability for the organization to go ahead and absorb the solution. And what does it cost in terms of the people involved? >> Yeah. Great conversation. I mean, this brings up the question of back in the data center, the cloud days, you had the concept of an SRE, which is now popular, site reliability engineer. One person does all the clusters and manages all the scale. Is the data engineer the new SRE for data? Are we seeing a similar trajectory? Just want to get your reaction. What do you guys think? >> Yes, so I would say, definitely. It depends on the teams and the sizes of that. We are high performing team so each automation takes bits on the pieces of the architecture, like where they want to invest in. And it comes out with the value of the engineer's time and basically like how much they can invest in, how much they need to configure the architecture, and how much time it'll take to time to market. So basically like, this is what I would also highlight as an engineer. I found Ahana like the... I would say as a Presto in a cloud native environment, or I think so there's the one in the market that seamlessly scales and then scales out. And further, with a team of us, I would say our team size like three to four engineers managing cluster day in day out, conferring, tuning and all those things takes a lot of time. And Ahana came in and takes it off our plate and the hands in a solution which works out of box. So that's where this comes in. Ahana it's also based on open-source community. >> So the time of the engineer's time is so valuable. >> Yeah. >> My take on it really in terms of the data engineering being the SRE. I think that can work, it depends on the actual person, and we definitely try to make the process as easy as possible. I think in Blinkit's case, you guys are... There are data platform owners, but they definitely are aware of the pipelines. >> John: Yeah. >> So they have very intimate knowledge of what data engineers do, but I think in their case, you guys, you're managing a ton of systems. So it's not just even Presto. They have a ton of systems and surfacing that interface so they can cater to all the data engineers across their data systems, I think is the big need for them. I know you guys you want to chime in. I mean, we've seen the architecture and things like that. I think you guys did an amazing job there. >> So, and to adding to Wen's point, right. Like I generally think what DevOps is to the tech team. I think, what is data engineer or the data teams are to the data organization, right? Like they play a very similar role that you have to act as a guardrail to ensure that everyone has access to the data so the democratizing and everything is there, but that has to also come with security, right? And when you do that, there are (indistinct) a lot of points where someone can interact with data. We have... And again, there's a mixed match of open-source tools that works well, as well. And there are some paid tools as well. So for us like for visualization, we use Redash for our ad hoc analysis. And we use Tableau as well whenever we want to give a very concise reporting. We have Jupyter notebooks in place and we have EMRs as well. So we always have a mixed batch of things where people can interact with data. And most of our time is spent in acting as that guardrail to ensure that everyone should have access to data, but it shouldn't be exploited, right. And I think that's where we spend most of our time in. >> Yeah. And I think the time is valuable, but that your point about the democratization aspect of it, there seems to be a bigger step function value that you're enabling and needs to be talked out. The 10x engineer, it's more like 50x, right? If you get it done right, the enablement downstream at the scale that we're seeing with this new trend is significant. It's not just, oh yeah, visualization and get some data quicker, there's actually real advantages on a multiple with that engineering. So, and we saw that with DevOps, right? Like, you do this right and then magic happens on the edges. So, yeah, it's interesting. You guys, congratulations. Great environment. Thanks for sharing the insight Blinkit. Wen, great to see you. Ahana again with Presto, congratulations. The open-source meets data engineering. Thanks so much. >> Thanks, John. >> Appreciate it. >> Okay. >> Thanks John. >> Thanks. >> Thanks for having us. >> This season two, episode two of our ongoing series. This one is Data as Code. This is theCUBE. I'm John furrier. Thanks for watching. (gentle music)

>> You're watching theCUBE. Welcome to our continuous coverage of AWS re-Invent 2021. I'm Dave Nicholson. We've got an amazing event that's been going on for the last four days with two live sets, two studios, more than 100 guests, and two very distinguished gentlemen here on the set with us live in Las Vegas. I'd like to welcome Krishna Mohan, Vice President and Global Head of TCS's AWS Business Unit. Welcome Krishna. >> Thank you Dave. >> Dave: And also with us Aditya Jagapal Nagarajan. >> Thank you. >> Dave: I hope I did your name justice. >> Perfect. >> Right, I tried. And Aditya is Head of Strategy and Business Operations for the TCS AWS Business Unit. Krishna, starting with you, tell us about TCS and AWS over the last year. What's been going on. >> Yeah. >> Thank you Dave for having me here. It's great to be in person actually, back in re-Invent, back in person, 25,000 people, but still we have pretty good measures, health measures that way. So I'm very happy to be here. TCS AWS business unit was formed three quarters back and we actually had always AWS partnership, but we actually felt that it's important to kind of have a separate business unit, which is the full stack, multi dimensional unit providing cloud migration modernization across applications, data, and infrastructure, and also main focus on industry solutions. So it has been a great three quarters, and our partnership only enhanced significantly, predominantly what we're actually seeing in the last one year. The cloud overall transformation, I think it kind of taken a different shape. It used to be cloud migration, modernization, cloud native development, but from there it has moved to enterprise transformation, that's happening on cloud, and specifically AWS majority of the time. So with that, we actually see a lot of customers. Broadly you can categorize them into three, cloud for IT, cloud for business, and cloud for innovation. And we're definitely seeing maximum traction there with our customers across the three categories. So I'm super excited to be here at the re-Invent, you know, a couple of our customers were in the keynote, Abort and Adam and Doug. In the Western Union was the keynote, Shelly covered at Western union transformation in the partner keynote with Doug, and very happy to see Linda Cower, the transformation in the United Headlines with Adam. So it's really great to see how we are helping the customers on the transformation. That's definitely, you know, the way that we see. And we have made significant progress on the overall in the last three quarters. And these kinds of wins and business transformation that has actually happened is what resulted in TCS getting the Raising Star GSA award for us. So I'm pretty happy to actually carry this little thing here. >> Is that what this is? >> Absolutely. So it means a lot because our customer in our kind of reinforcing the value, the TCS, along with AWS is bringing to the customer. >> So I wasn't going to say anything. I just assumed that you were a 2001 Space Odyssey fan and you just brought, you know, a version of the monolith with you. I wasn't sure. Congratulations. >> Thank you. That's a quite an achievement especially in the relatively short period of time. And especially with the constraints that have been placed upon all of us. Did they give you like a schwag bag with a bunch of, with, you know, like they do at the academy awards? Are you familiar with that. >> We had a great fun event on Monday afternoon. >> Fantastic. >> Yeah. >> Aditya, talk about, you're a consultancy, your organization is a consultancy. Talk about how you engage with the customers that you are helping to bridge the divide between what their business requirements are, and the technology that AWS is delivering. Because I think we all agree that everything we're seeing here from AWS is wonderful, but without an organization like yours, actual end users, actual customers, have a hard time driving benefits. So, how do you approach that? >> Gladly thank you, Dave, and thank you for theCUBE for having us here. And just borrowing from what Krishna talked about, the three layers of value creation, the cloud for IT, cloud for business and cloud for innovation. We see the journeys clients take, to start with how they look at IT modernization, and go all the way to business transformation, and look at ecosystem transformation as well. For example, we just heard about Western Union and we just came off of one with SWBC where they have completely modernized the payment systems on AWS and TCS has been the partner for transforming that for them. And that not only just means the technology layers, but also re imagining business processes in the cloud. Moving on from the financial side, if you look at the digital farming, for example, we have been working with some of the leading, the transmitter players in the healthcare industry and in the manufacturing space to look at helping farmers with AI. Right? And helping them look at how they can ensure better analytics and drone capabilities for digital farming. Drug trial development and acceleration for time to market has been a front and center for all of us in the last two years where I've been helping pharmacy organizations get better and will bring up drug trials and reach the end customers better with cloud. So there's various examples here. >> I want to poke on that a little bit. >> Aditya: Yeah. So when TCS is engaging a customer, say in farming versus pharma, how much of your interaction with them is specialized by industry vertical or specific area expertise versus the generic workings that are going to be supporting that effort in the background? What does that look like? Are you going in first with a pharma discussion, first with the farm discussion, as opposed to an overall discussion? >> It's a great point you mentioned Dave because that's the sort of essence of TCS. Because the way we look at it, we actually appeal to the industry specific. So our domain and contextual knowledge is very important to appeal to the customers and to the various stakeholders, no longer are the days where you talk about technology as a means to an end. We talk about how end customers can benefit in that context of what they're going through in that industry. And how can then technology be part of that strategy, right? So, hence, as you rightly said, domain and context first, followed by technology powering the outcome. >> Even though farm and pharma sound a lot alike. >> Right, I showed you the very difference. >> And they may share some things in common. Yes, very, very different. Krishna, talk about your go to market motion. How are clients aware of TCS? Do you have teams that engage clients directly and then bring AWS into the conversation? Or are you being brought in by AWS? Is it a combination? What does that look like? >> So, very good relevant question. So our GTM strategies is TCS has been in the, you know, serving the enterprise customers and IT transformation for 52 years now. So we have a huge base. But specifically from an AWS BU perspective, we are focusing on selective verticals, banking financial services and insurance is large, life sciences, health care, and travel, transportation and hospitality. So these are the verticals that we're actually focusing on, and given our presence in the enterprise sector, we already have a direct sales teams who are engaging with the customers directly on enterprise transformation and business transformation. And once we have that conversation, we actually take all these solutions that we have built on AWS and along with AWS. There are few customers in the last three quarters, after farming the AWS business unit, one thing that we did is with AWS we're proactively going and identifying the logos and the customers. And with the focus not on technology, with the focus on how to solve their problems on the business side and how to create new business models. So it's kind of both. We bring in, AWS brings in logos as well, so Greenfield accounts, and as well as our contextual knowledge of the industry is how the GTM is working out, and working out pretty good. >> You mentioned, you've been at this for 52 years. >> Aditya: Yeah. >> You must've been very young when you started doing this. Talk about the internal dynamics. So think of TCS, the larger organization. You represent the AWS business unit. TCS has been doing this for a long time, predating what we think now of as cloud. I'm sure that you have long existing relationships with customers, where you've been doing things for them that aren't cloudy, and those things keep the lights on at TCS, right? Important sources of revenue. Yet you're going in and you're consulting and saying, hey, you know, it might be better for you, Mr. Customer, to work with AWS and TCS, as opposed to maybe being at a data center that TCS manages, I mean, how do you manage that internal dynamic? You've got to have people at TCS who are saying, stay away, that's my revenue, don't move my cheese. What does that look like? >> Very valid question Dave. So the way that TCS is actually looking at is, twin engine strategy. There's a cost and optimization strategy, which we have. We sell the customers and operations, running the BAU if you will, business as usual, then you have something called growth and transformation. So as a strategy that we are very clear that the path of business transformation is growth and transformation channel. So we as a company are very comfortable cannibalizing our C and O in a business because we want to be relevant to the market, relevant to the customer, and relevant to the partner ecosystem. So the only way you are relevant is actually to challenge yourself, cannibalize your own business, and for the long, you know, strategy of looking at how to grow. And that's how our twin engine strategy is working. And there are a lot of customers where we have developer with contextual knowledge serving 20 years, 25 years of the customers. We know how they work, what their business is actually, you know, what's going to be the future of the business. So we are in a better position to actually transform them. And as a company, we already took cannibalize our revenue. >> So Adi, give us an example of working with a customer and give us an idea of what that customer's perspective is in terms of their place on the spectrum of, I don't want to move anything if I don't have to versus, hey, you guys can't move fast enough to deliver what I want. Where are you seeing that spectrum of customer requirements at this point? Do you feel like you're having to lead people to water still? Where are we with that? >> Well, if you asked me this question a couple of years ago, it would be about, hey, look, here's a beautiful water and the lake looks good, why don't we spend by the side and see what it tastes like? Now the question is, how much water to drink? Right? So the point being that customers have fast realized that cloud is not just an IT decision, it's a business transformation decision. So if I may just call it back what Krishna talked about, the dual engine strategy. A clear Testament to that is some of our relationships, most of our relationships are the matter has been over two decades with our clients. And that's a perfect indication of being constantly relevant for them because as their models change, as their markets change, customer expectations change, we need to constantly innovate ourselves. >> You're innovating your business just like that. >> Absolutely. >> Correct. >> So you know, as we say, you're in the boat with them and you're going through the same changes. >> And so coming back to the question which you asked, the point was we give them a point of what experience they can have with cloud by each stakeholder. The CIO wants to look at how we can look at better sustainability of their operations, keep the lights on as you said, enhance stability with more automatable capabilities, looking at DevOps, the business is completely looking at how can cloud fundamentally change my business model. And you have both these stakeholders coexisting with the same outcome towards enterprise transformation. And that's the experience which we work with them to shape. To say what the starting point is? Where would they like to go? And how can we go to them in the journey? What's interesting here is, nobody has all the answers. Neither is AWS nor customer the TCS, but we are here to create a culture of discovering the right goal and the right answers. It's very important. That's the approach to getting it working. >> Krishna and our last minute together. You've just received the Rising Star Award, 2022 is rapidly approaching, this doesn't put any pressure on you at all for 2022 because people are going to ask, what are those rising stars do again in 2022? What's on the horizon, what are the two of you excited about for next year? >> I think we are super excited with how AWS, you know, definitely in Adam's keynote, if I had to take a couple of points that I'm taking away is in addition to enhancing their core cloud capabilities, but if there's pivoted on industry solutions, you know, the fin space that they have announced, and the industrial solutions that they have announced. So that is where it very clearly aligns to our strategy of TCS, helping customers look for change their business models, implement new business models, create ecosystem play. And that's basically where we are really super excited. And another point which I took from Adam is the, they're focused on Edge with IOT and private 5G. And that's very, very important especially when you look at it both IT, as well as the IOT transformation. So we are super excited with the potential, all the new bells and whistles AWS is rolled out in last four days, And looking forward for few more of this. >> Congratulations again. It's a fantastic acknowledgement of what you've been able to do over the last, just three quarters as you mentioned, closing out 2021 in a very, very good way. Looking forward to 2022. Thank you gentlemen for joining us today here on theCUBE, and thank all of you for joining us, for continuing continuous Cube coverage of AWS re-Invent 2021. We are the leader in hybrid technology event coverage. I'm Dave Nicholson stay tuned for more from theCUBE.

>>Hey, welcome to this continuing coverage of the H P E Aruba. Pensando announcement. I'm lisa martin. Hopefully you've seen by now the announcement from john and Antonio, we're going to get into some technical details. Now I've got two guests joining me. Please welcome Krishna Otopeni, the VP of engineering at Pensando and frank Reich stein, senior Director platform engineering from HP Aruba guys welcome to the program. >>Hi lisa. >>Hi lisa. Thanks for having us. >>Sure. So we're going to, we're going to dig in here. You guys are tasked with bringing these two worlds together, christian. Let's go ahead and start with you talk to me about the announcement why this is so significant and then we'll dig into the technical details. >>Yeah. So as you know, right, Pensando has been in the market for a couple of years right now. Um, and we heard a lot of success with the cloud providers and we're also working with be a million project Montreat. Um, so what we learned in the last couple of years, we're trying to take all the lessons and I was a little bit going to what, what we learned with the crop, your providers. So we took a dsC card, which is a B C, a form factor, the customer takes dsC card inserts into the, into server with various forces and hypervisors. So it's really exciting that the BSE is in production with some of the providers already and some of them were taking to production in this calendar quarter and we also have in connection with that first generation BSC cards a couple of years and some of the biggest banks and storage platform providers. So, so this is kind of a big deal for us because we are starting with what we call a D P U. Uh that Pensando is bailing which is the latest generation of it is called code named Alba which delivers the software in silicon program ability while matching the performance of hardware. So internally the DPU has the tight integration between special purpose processors that consent of what we call mps and a general purpose processor like arm course where we do the management and control software and with tied together with offload engines like encryption and compression. The key takeaway from this platform. Their consent of belt. It's it's programmable at all layers Either by Pensando or our customers whether it's in data plane using P four or control and management plane. All right. So what we learned while developing this platform and taking this production with the public cloud providers, we realize that the platform and architecture is not only very highly scalable with very high performance with respect to, you know, packets per second or stable connections per second or NBA me I ops but it's also adaptable like a very rapid paced. And another key lesson that we learn from our cloud partners is that the new devoPS model operations is as important as functionality. For example, the importance of creating the DPU pipeline the subsequent guarantees or providing Hatch uh first fateful connections so that in some cases the component fails, there is hardware or software customer doesn't have any disruption in his network or storage operations. So we took all the ski lessons that we learned over the last few years. And then we are building a new platform partnering with Aruba team which is very high scale with very high performance at the same time, tied with very good operations um that you know it comes the best of both both platforms from the pew side and from the Aruba side frank they want to add on the Aruba platform side. >>Sure, yeah. So the Aruba networking team has been building network switches for the past 25 years and we've been following all of the trends and evolutions over that time frame. And as we've gone through a few years ago we decided to make an evolution of our operating system to scale it up for the modern needs of the modern world. And this included doing things like designing with a micro services oriented architecture to provide for a high degree of resiliency throughout the product line. And then being able to extend that single network operating system from the core to the edge of the network. As we've been partnering with Pensando, it came very clear that the evolution of the network the next step was this form of a deep, you integrated into that top of rack switch to provide a deeper and richer feature set and what has traditionally been available in your top of rack switch. And so this partnership has enabled us to leapfrog but has been traditional top of rack functionality and add to it. Things that previously were not attainable in that layer of the network >>frank. Continuing on with you. Talk to me about some of the technology requirements and challenges of designing and engineering and delivering the industry's first distributed service switch. What were some of those? >>Sure, sure. So a lot of the challenges around integrating this type of solution come down to how to ensure that you have the highest performance possible and maintaining high speed of performance when you're now introducing an additional pay hop within the network topology inside of the switch, a lot of that came down to integrating the background and skill setting capabilities that come along with osc x that were made it quick for us to enable a new piece of functionality within the architecture and then a lot of credit has to go to the Pensando team for the richness of the feature setting capability set that they have within that DPU product as it stands >>christian, let's go ahead and dig through some of those core features and capabilities that are really going to be benefiting customers. >>Yeah, so basically right, uh taking a little bit of step back, we started with the dsc market from Pensando perspective where we wanted to put gPU in every survey and we obviously have success in enterprise customers and cloud customers that we discussed earlier. But we also learned a few lessons while deploying DSC and enterprise markets in the sense that enterprise markets do not need the performance of every DSC at 200 G full duplex network services for every survey. And also you know what makes historic key is that you know, there are a lot of brownfield service in current enterprise data centers where customers do not want to open up a server to put the DSC in. So we wanted to give a product with the form factor that frank is talking about and technology that's very familiar to every IT department given the Aruba Lois uh in a deployment in data centers. And also as I said earlier, what we lessons that we learned, we came up with this taking this production very deep you software and hardware which is deployed in public clouds. And combined with those features that that have been rapidly evolving uh through multiple Aruba releases into enterprise data centers in a switch form factors. So what we think is by doing this taking the best of both worlds. We're creating a new product category that is not that is for the features and capabilities are not available in the market from any vendor specifically providing state full services at every tour without the complexity of the service redirection because today's data centers if you want to install services. It's a it's a lot of effort operator to bring in those services. This obviously also has a great operational model, great TCO and the functionality that customers that you never see in tar before. For example, in the first release we are providing state full firewall with the visibility at every floor level that goes through the tower which never existed in the market before. >>New product category. That's a big deal christian. Talk to me a little bit about how long you guys have been at this, you were in stealth mode crack that open for us. >>I mean it has been a less than a year but of development that both teams have been doing and we work very closely together and we meet I mean for sure at least more than a week uh you know, more than once the once a week between uh frank's team and you know, and send it to them and there's a coordination between the sales team and the marketing team and the go to market team and then how we sell it and the manufacturing team, there's a lot goes on in building this product. I mean we believe this is the fastest uh tard new generational product that we built because because we could do that because the experience of both the teams trying they want anything more to this one. >>Yeah, I think that that really goes to the point here. The capabilities and maturity of the deep you solution that Pensando was bringing into the solution really allowed for a very fast and seamless integration on top of that Aruba, OsC X and the platform that we built there with automated Api generation and integration with our Aruba fabric composer orchestration layer really created the capability to make things go as fast as possible for this development effort And so to really take a new product and define a new product space within a 12 month time frame has been a really exciting and impressive feat by both teams. >>Very impressive considering the challenges and the dynamics in the market and the global market that we've had frank. How big of a lead do you think you have on incumbents here? >>I think we have a substantial lead on the incumbents here. I think what we're doing is a fundamentally different take on how you do a top of rack switch and the capabilities that we're bringing to bear at the top of rack are fundamentally new and differentiated from what the competition has been thinking about. So I believe we have a substantial lead on the competition. >>Excellent chris to talk to me about what's next? What's the future? I have some secret sources that tell me that john and Antonio are meeting regularly pushing you guys, what does the future hold. >>Yeah. So I mean obviously this is the start of an exciting journey. There's a first platform you're bringing to the market jointly and obviously we like a bunch of form factors without upcoming road map. So additionally I mean the software in silicon performance that with all the services that we deliver a software means that scope and scale of the state will services that we can deliver and evolve over time whether you talk about security or encryption or state flat or load balancing or d does all of the services and then you know hybrid connectivity. So obviously you know there's a lot that we can do with this platform that will be driven by with the partnership with our customers. We also see that you know the market of all where you know all the customers we'll have some customers will have deep us in the service and some customers will use the new platform that we're bringing together. So we won't have all the management start to make sure all of them can be managed uniformly and any time you know you this is a major step for a new category of platform and architecture we're developing jointly with the rubber and I believe this will be a huge opportunity for both the companies and our customers and this is exciting times ahead for us >>and talk to me both of your opinions here where can customers go to find more information, how can they get started frank will go ahead and start with you. >>Yeah you can jump straight to Aruba networks dot com and dig into the feature sets and packages that we have available with the Aruba 10-K product line direct from there. >>Fantastic christian anything to add >>that is correct actually. So we are treating it as one product coming from both the companies. All the documentation is where you know, frank pointed out in Aruba website, we put all the documentation at the same place and we're supporting it as one unified product from both the companies. >>Are you seeing any? We've seen so much change in the last year and a half. Last question. I'm just wondering if if either of the HPV riverside or the pence underside is seeing any industries that might be really prime to take advantage of knowing how many industries all have been affected by the events of the last year and a half christian any thoughts there? >>Yeah, I mean if you look at it right and obviously all of us are working from home and now everything happens, you know, mostly at the edge, right? You know, and we are in that this platform will help us get there where we get security to the edge and we get more visibility and more services to the edge. Right? So I mean that's what you know Pensando is all about and hoping that you know, this is uh this journey that we started with the D. P us, we go with this platform and it will ever all and it will help customers, our customers and our partners leverage all the functionality that, you know, Pensando and the rubber can bring together. >>Well guys, congratulations on an enormous feat accomplished in not just a 12 month time period, but a very challenging 12 month time period. We appreciate you guys breaking down the HP Aruba Pensando announcement and more technical detail. Those can go to learn more information and again, congratulations. >>Thank you. >>Thank you very much lisa >>for my guests. I'm lisa martin. You're watching this HP Aruba Pensando announcement. Thanks for watching. >>Mhm >>mm.

(upbeat music) >> Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, the Next Big Thing in AI, Security & Life Sciences. Today we're joined by Abacus Insights for the Life Sciences track, I'm your host, Natalie Ehrlich. Now we're going to be speaking about creating an innovation enabling data environment to accelerate your healthcare analytics journey, and we're now joined by our guests Krishna Kottapalli, chief commercial officer as well as Sumant Rao, chief product officer, both working at Abacus Insights, thank you very much for joining us. >> Thank you for having us. >> Well let's kick off with our theme Krishna, how can we create innovation enabling data environments in order to facilitate the healthcare analytics? >> Yeah, so I think if you sort of think about this that is a lot of data proliferating inside the healthcare system, and whether it's through the internal sources, external sources, devices, patient monitoring platforms, and so on, and all of this carries yeah all of these essentially carry, have useful data and intelligence, right, and essentially the users are looking to get insights out of it to solve problems. And we're also seeing that the journey that our clients are going through is actually a transformation journey, right so they are thinking about how do we seamlessly interact with our stakeholders, so their stakeholders being members and providers, so that they don't get frustrated and feel like they're interacting with multiple parts of the health plan, right, we typically when you call the health plan you feel like you're calling five different departments, so they want to have a seamless experience, and finally, I think the whole, you know, the data being you know, in the ecosystem within the patients, payers, and providers being able to operate and interact has intelligence. So what we, what we think about this is how do we take all of this and help our clients you know, digitize their, you know, path forward and create a way to deliver, you know enable them to do meaningful analytics. >> Well Sumant, when you think about your customers what are the key benefits that Abacus is providing? >> So that's a good question, so primarily speaking, we approach this as, you know a framework that drives innovation that enables data and analytics. I mean, that's really what we're trying to do here. What Abacus does though, is this is slightly different is how we think about this. So we firmly believe that data analytics is not a linear journey, I mean, you cannot say that, oh I'll build my data foundation first and then, you know have the data and then they shall come that's not how it works. So for us, the way Abacus approaches this is, we focus really heavily on the data foundation part of it first. But along the way in the process, a big part of our value statement is we engage and make sure we are driving business value throughout this piece. So, so general message is, you know make sure innovation for the sake of innovation data is not how you're approaching this, but think about your business users, get them engaged, have it small, milestone driven progress that you make along the way. So, so generally speaking, it's we're not tryna be just a platform who moves bits and bytes of information. The way we think about this is you know we'll help you along this journey, there are steps that happen that take you there. And because of which, the message to most of our customers is you focus on your core competence. You know your business, you have nuances in the data, you have nuances on needs that your customers need, you focus on that. The scale that Abacus brings because this is what we do day in day out is more along area of re-usability. So if within our customers, they've got data assets how do we reuse some of that? How does Abacus re-use the fact that because of our of what we do, we actually have data assets that, you know, we can bring data to life quickly. So, so general guidelines, right, so first is don't innovate for the sake of innovating. I mean, that's not going to get you far, respect the process that this is not a linear path, there's always value that's happening throughout the process, and that's, you know, Abacus will work closely with you to make sure you recognize that value. The second part is within your organization, you have assets. There's like major data assets, there's IP, there's things that can leverage that Abacus will do. And because we are a platform, what we focus on is configurability. We've done this for, I mean, a lot of us on the Abacus team come from healthcare space, we have got big payer DNA, we get this, and what we also know is data rules change. I mean, you know, it's really hard when you build a system that's tightly built and you cannot change and you cannot adapt as data rules change, so we've made that part of it easier. We have, we understand data governance, so we work closely with our payers data governance teams to make sure that part of it happens. And I think the last part of this which is really important, this in the context of this conversation is, all of this is good stuff, I mean, you've got massive data foundation, you've got, you know, healthcare expertise flowing in, you've got partnerships with data governance, all that is great. If you don't have best-in-class infrastructure supporting all of that, then you really, you will really have issues Erlich. I mean, that's just the way it works, and this is why, you know, we're built on the AWS stack which kind of helps us, and also helps our clients along with their cloud journey. So it's kind of an interesting set of events in terms of you know, again, I'm going to repeat this because it's important that we don't innovate for the sake of innovating, re-use your assets, leverage your existing IP, make things configurable, data changes, and then leverage best in class infrastructure, so Abacus strategy progresses across those four dimensions. >> And I mean, that's an excellent point about healthcare data being really nuanced and you know, Krishna would love to get your insights on what you see are the biggest opportunities in healthcare analytics now. >> Yeah, so the biggest opportunities are, you know there are two, we think about it in two dimensions, right, one is really around sort of the analytics use cases, and second is around the operational use cases, right, so if you think about a payer they're trying to solve both, and we see because of, you know, our the way we think about data, which is close to near real time, we are able to essentially serve up our clients with, you know helping them solve both their use cases. So think of this that, when you're a patient, you go to you know, you go to a CVS to do something, and then you go to your doctor's office to do something, right, to be, to be able to take a test. If all of these are known, to your payer care management team, if you will, in close to near real time then know, right, where you've been, what you can do how to be able to sort of intervene and so on and so forth, so from a next best action and operational use cases we see a lot of them emerging, new thanks to the cloud as well as thanks to infrastructure, which can do sort of near real time. So that's our own sort of operational use cases if you will. If, when you think about the analytics right, so, you know, every, all payers struggle with this, Which is you have limited dollars to be able to intervene with you know, a large set of population, right so every piece of data that you know, have about your patient, about the specific provider so on and so forth is able to actually, you know give you analytics to be able to intervene or engage if you will, with the patient in a very one-to-one manner. And what we find is at the end of the day if the patient is not engaged in this and the member or the patient is not engaged, you know in the healthcare, you know, value chain, if you will, then your dollars go to waste, and we feel that, in essentially both of these type of use cases can be sorted up really well with, with a unified data platform, as well as with upstack analytics. >> And now Sumant, I'd love to hear from you, you know you're really involved with the product, how do you see the competitive landscape? How do you make sure that your product is the best out there? >> So I think, I think a lot of that is we think about ourselves across three, three vectors. Talk about it as core platform, which is at a very minimal level of description, it's really moving bits and bytes from point A to point B. That's one part of it, right, and I think there's a, it's a pretty crowded space, it's a whole bunch of folks out there trying to, you know demonstrate that they can successfully land data from one point to the other. We do that too, we do that at scale. Where you'd start differentiating and pulling away from the pack is the second vector, which is enrichment. Now, this is where again, it's you have to understand healthcare data to really build a level of respect for how messy it can get. And you have to understand it and build it in a way where it's easy to keep up with the changes. We spend a lot of time, you know in building out a platform to do that so that we can implement data quickly. I mean, you know, for Abacus to bring a data source to life in less than 45 days, it's pretty straightforward. And it's you're talking on an average 6 or 12 months across the rest. Because we get this, we've got a library of rules, we understand how to bring this piece, so we start pulling away from the competitors, if you may. More along the enrichment vector, because that's where we think, getting high quality rules, getting these re-used, all of this is part of it, but then we bring another level of enrichment where we have, you know, we use public data sets, we use a reference data sets, we tie this, we fill in the blanks in the data. All of this is the end state, let's make the data shovel-ready for analytics. So we do all of that along the way, so now applying our expertise, cleansing data, making sure it's the gaps are all filled out and getting this ready and then comes the next part where we tie this data out. Cause it's one thing to bring in multiple sources quickly at scale high speed and all that good stuff, which is hard work, but you know, it's, it's expected now at the same time how do you put all that together in a meaningful manner with which we can actually, you know, land it and keep it ready? So that's two parts. So first is, the platform, the nuts and bolts, the pipes, all that is good stuff, the second is the enrichment. The third side, which is really where we start differentiating is distribution. We have a philosophy that, you know, really the mission of the whole company was to get data available. To solve use cases like the one Krishna just talked about. So rather than make this a massive change management program that takes five years to implement, and really scares your end users away, our philosophy is like let's have incremental use case all on the way, but let's talk to the users, let them interact with data as easy as they can. So we've built our partnerships on our distribution hub, which makes it easy, so an example is if you have someone in the marketing team, who really wants to analyze a particular population to reach out to them, and all they know is tableau, it is great. It should be as simple as saying, look what's the sliver of data you need to get your job done, how do you interact? So we've our distribution hub, is really is the part where, users come in, interact with the data with you know, we will meet you where you are is the underlying principle and that's how it operates. So, so I think on the first level of platform, yeah a crowded space everyone's fighting for that piece, the second part of it is enrichment where we really start pulling away using our expertise, and then at the end of it you've got the distribution part where you know you just want to make it available to users, and, you know, a lot of work has gone into getting this done but that's how we work. >> And if I could add a couple more things, Natalie, so the other thing is security, right so the reason that healthcare, healthcare players have not gone to the cloud until about three four years back, is the whole concern about security so we have invested a ton of resources and money to make sure that our platform is run in the most secure manner, and giving confidence to our clients, and it's an expensive process, right, even though you're on AWS you have to have your own certification that, so that that gives us a huge differentiator, and the last but not least is how we actually approach the whole data management deployment process, which is, our clients think about us in two dimensions, total cost of ownership, but typically 50 to 60% of what it would cost internally, and secondly, time to value, right, you can't have an infinitely long deployment cycle. So we think about those two and actually put our skin in the game and tie our, you know, tie our success to total cost of ownership and time to value. >> Well, just really quick in 1-2 sentences, would love to get your insight on Abacus's defining contribution to the future of cloud scale. >> Go ahead, Sumant. >> So as I see it, I think so part of it is we've got some of our clients who are payers and we've got them along their cloud journey trusting one of their key assets which is data, and letting us drive it. And this is really driven by domain expertise, a good understanding of data governance, and a great understanding of security, I mean, combining all of this, we've actually got our clients sitting and operating on, you know pretty significant cloud infrastructure successfully day in day out. So I think we've done our part as far as, you know helping folks along that journey. >> Yeah and just to close it out I would say it is speed, right, it is speed to deployment, you don't have to wait. You know, we have set up the infrastructure, set up the cloud and the ability to get things up and running is literally we think about it in weeks, and not months. >> Terrific, well, thank you both very much for insights, fantastic to have you on the show, really fascinating to hear about how Abacus is leveraging healthcare data expertise on its platform , to drive robust analytics, and of course, here we were joined by Abacus Insights, Krishna Kottapalli, the chief commercial officer, as well as Sumant Rao, the chief product officer, thank you again very much for your insights on this program and this session of the AWS Startup Showcase. (upbeat music)

(upbeat music) >> Hello and welcome to theCUBE as we present AWS Startup Showcase, The Next Big Thing in AI, Security & Life Sciences, the hottest startups. And today's session is really the next big thing in AI Security & Life Sciences. As to the AI track is really a big one most important. And we have a feature in company, fiddler.ai. I'm your host, John Furrier with theCUBE. And we're joined by the founders, Krishna Gade, founder and CEO, and Amit Paka, founder and Chief Product Officer. Great to have the founders on. Gentlemen, thank you for coming on this Cube segment for the AWS Startup Showcase. >> Thanks, John... >> Good to be here. >> So the topic of this session is staying compliant and accelerating AI adoption and model performance monitoring. Basically, bottom line is how to be innovative with AI and stay (John laughs) within the rules of the road, if you will. So, super important topic. Everyone knows the benefits of what AI can do. Everyone sees machine learning being embedded in every single application, but the business drivers of compliance and all kinds of new kinds of regulations are popping up. So we don't. The question is how do you stay compliant? Which is essentially how do you not foreclose the future opportunities? That's really the question on everyone's mind these days. So let's get into it. But before we start let's take a minute to explain what you guys do. Krishna, we'll start with you first. What does fiddler.ai do? >> Absolutely, yeah. Fiddler is a model performance management platform company. We help, you know, enterprises, mid-market companies to build responsible AI by helping them continuously monitoring their AI, analyzing it, explaining it, so that they know what's going on with their AI solutions at any given point of time. And they can be like, ensuring that, you know businesses are intact and they're compliant with all the regulations that they have in their industry. >> Everyone thinks AI is a secret sauce. It's magic beans and automatically will just change over the company. (John laughs) So it's kind of like this almost like it's a hope. But the reality is there is some value there but there's something that has to be done first. So let's get into what this model performance management is because it's a concept that needs to be understood well but also you got to implement it properly. There's some foundational things you've got to you know, walk, crawl before you walk and walk before you run kind of thing. So let's get into it. What is model performance management? >> Yeah, that's a great question. So the core software artifact most an AI system is called an AI model. So it essentially represents the patterns inside data accessing manner so that it can actually predict the future. Now, for example, let's say I'm trying to build an AI based credit underwriting system. What I would do is I would look at the historical you know, loans data. You know, good loans and bad loans. And then, I will build it a model that can capture those patterns so that when a new customer comes in I can actually predict, you know, how likely they are going to default on the loan much more activity. And this helps me as a bank or center company to produce more good loans for my company and ensure that my customer is not, you know, getting the right customer service. Now, the problem though is this AI model is a black box. Unlike regular software code you cannot really open up and read its code and read its patterns and how it is doing. And so that's where the risks around the AI models come along. And so you need a ways to innovate to actually explain it. You need to understand it and you need to monitor it. And this is where the model performance management system like Fiddler can help you look into that black box. Understand how it's doing it, monitor its predictions continuously so that you know what these models are doing at any given point of time. >> I mean, I'd love to get your thoughts on this because on the product side I could, first of all, totally awesome concept. No one debates that. But now you've got more and more companies integrating with each other more data's being shared. And so the, you know, everyone knows what an app sec review is, right? But now they're thinking about this concept of how do you do review of models, right? So understanding what's inside the black box is a huge thing. How do you do this? What does it mean? >> Yeah, so typically what you would do is it's just like software where you would validate software code going through QA and like analysis. In case of models you would try to prove the model in like different granularities to really understand how the model is behaving. This could be at a model prediction like level in case of the loans example, Krishna just gave. Why is my model saying high-risk to in particular loan? Or it might be in case of explaining groups of loans. For example, why is my model making high-risk predictions to loans made in California or loans made to all men? Was it loans made to all women? And it could also be at the global level. What are the key data factors important to my model? So the ability to prove the model deeper and really opening up the black box and then using that knowledge to explain how the model is working to non-technical folks in compliance. Or to folks who are regulators, who just want to ensure that they know how the model works to make sure that it's keeping up with kind of lending regulations to ensure that it's not biased and so on. So that's typically the way you would do it with the machine learning model. >> Krishna, talk about the potential embarrassments that could happen. You just mentioned some of the use cases you heard from a mid-saying you know, female, male. I mean, machines, aren't that smart. (John laughs) >> Yeah. >> If they don't have the data. >> Yeah. >> And data is fragmented you've got silos with all kinds of challenges just on the data problem, right? >> Yeah. >> So nevermind the machine learning problems. So, this is huge. I mean, the embarrassment opportunities. >> Yeah. >> And the risk management on whether it's a hack or something else. So you've got public embarrassment by doing something really went wrong. And then, you've got the real business impact that could be damaging. >> Absolutely. You know, AI has come forward a lot, right? I mean, you know, you have lots of data these days. You have a lot of computing power an amazing algorithms that you can actually build really sophisticated models. Some of these models were known to beat humans in image recognition and whatnot. However, the problem is there are risks in using AI, you know, without properly testing it, without properly monitoring it. For example, a couple of years ago, Apple and Goldman Sachs launched a credit card, right? And for their users where they were using algorithms presumably AI or machine learning algorithms to set credit limits. What happened was within the same household husband and wife got 10 times difference in the credit limits being set for them. And some of these people had similar FICO scores, similar salary ranges. And some of them went online and complained about it and that included the likes of Steve Wozniak as well. >> Yeah. >> So this was, these kind of stories are usually embarrassing when you could lose customer trust overnight, right? And, you know, you have to do a lot of PR damage. Eventually, there was a regulatory probate with Goldman Sachs. So there are these problems if you're not properly monitoring area systems, properly validating and testing them before you launch to the users. And that is why tools like Fiddler are coming forward so that you know, enterprises can do this. So that they can ensure responsible AI for both their organization as well as their customers. >> That's a great point, I want to get into this. What it kind of means and the kind of the industry side of it? And then, how that impacts customers? If you guys don't mind, machine learning opposite a term MLOps has been coined in the industry as you know. Basically, operations around machine learning, which kind of gets into the workflows and development life cycles. But ultimately, as you mentioned, this black box and this model being made. There's a heavy reliance on data. So Amit, what does this mean? Because now is it becomes operational with MLOps. There is now internal workflows and activities and roles and responsibilities. How is this changing organizations, you know separate the embarrassment, which is totally true. Now I've got an internal operational aspect and there's dev involved. What's the issue? >> Yeah, so typically, so if you look at the whole life cycle of machine learning ops, in some ways mirrors the traditional life cycle of kind of DevOps but in some ways it introduces new complexities. Specifically, because the models can be a black box. That's one thing to kind of watch out for. And secondly, because these models are probabilistic artifact, which means they are trained on data to grab relationships for what kind of potentially making high accuracy predictions. But the data that they see in life might actually differ and that might hurt their performance especially because machine learning is applied towards these high ROI use cases. So this process of MLOps needs to change to incorporate the fact that machine learning models can be black boxes and machine learning models can decay. And so the second part I think that's also relevant is because machine learning models can decay. You don't just create one model you create multiple versions of these models. And so you have to constantly stay on top of how your model is deviating from your reality and actual reality and kind of bring it back to that representation of reality. >> So this is interesting, I like this. So now there's a model for the model. So this is interesting. You guys have innovated on this model performance management idea. Can you explain the framework and how you guys solve that regulatory compliance piece? Because if you can be a model of the model, if you will. >> Then. >> Then you can then have some stability around maintaining the code basis or the integrity of the model. >> Okay. >> How does that? What do you guys offer? Take us through the framework and how it works and then how it ties to that regulatory piece? >> So the MPM system or the model performance management system really sits at the heart of the machine learning workflow. Keeping track of the data that is flowing through your ML life cycle, keeping track of the models that are going, you know, we're getting created and getting deployed and how they're performing. Keeping track of the whole parts of the models. So it gives you a centralized way of managing all of these information in one place, right? It gives you an oversight from a compliance standpoint from an operational standpoint of what's going on with your models in production. Imagine you're a bank you're probably creating hundreds of these models, but a variety of use cases, credit risk, fraud, anti-money laundering. How are you going to know which models are actually working very well? Which models are stale? Which models are expired? How do you know which models are underperforming? You know, are you getting alerts? So this is what this kind of governance, this performance management is what the system offers. It's a visual interface, lots of dashboards, the developers, operations folks, compliance folks can go and look into. And then they would get alerts when things go wrong with respect to their models. In terms of how it can be helpful to meet in compliance regulations. For example, let's say I'm starting to create a new credit risk model in a bank. Now I'm innovating on different AI algorithms here immediately before I even deploy that model I have to validate it. I have to explain it and create a report so that I can submit to my internal risk management team which can then review it, you know, understand all kinds of risks around it. And then potentially share it with the audit team and then keep a log of these reports so that when a regulator comes visits them, you know they can share these reports. These are the model reports. Is that how the model was created? Fiddler helps them create these reports, keep all of these reports in one place. And then once the model is deployed, you know, it basically can help them monitor these models continuously. So that they don't just have one ad hoc report when it was created upfront, they can a continuous monitoring continuous dashboard in terms of what it was doing in the last one whatever number of months it was running for. >> You know what? >> Historically, if you were to regulate it like all AI applications in the U.S. the legacy regulations are the ones that today are applied as to the equal credit opportunity or the Fed guidelines of like SR 11-7 that kind of comment that's applicable to all banks. So there is no purpose-built AI regulation but the EU released a proposed regulation just about three weeks back. That classifies risk within applications, and specifically for high-risk applications. They propose new oversight and the ads mandating explainability helping teams understand how the models are working and monitoring to ensure that when a model is trained for high accuracy, it maintains that. So now those two mandatory needs of high risk application, those are the ones that are solved by Fiddler. >> Yeah, this is, you mentioned explainable AI. Could you just quickly define that for the audience? Because this is a trend we're seeing a lot more of. Take a minute to explain what is explainable AI? >> Yeah, as I said in the beginning, you know AI model is a new software artifact that is being created. It is the core of an AI system. It's what represents all the patterns in the data and coach them and then uses that knowledge to predict the future. Now how it encodes all of these patterns is black magic, right? >> Yeah. >> You really don't know how the model is working. And so explainable AI is a set of technologies that can help you unlock that black box. You know, quote-unquote debug that model, looking to the model is introspected inspected, probate, whatever you want to call it, to understand how it works. For example, let's say I created an AI model, that again, predicts, you know, loan risk. Now let's say some person, a person comes to my bank and applies for a $10,000 loan, and the bank rejects the loan or the model rejects the loan. Now, why did it do it, right? That's a question that can explain the way I can answer. They can answer, hey, you know, the person's, you know salary range, you know, is contributing to 20% of the loan risk or this person's previous debt is contributing to 30% of the loan risk. So you can get a detailed set of dashboards in terms of attribution of taking the loan risk, the composite loan risk, and then attributing it to all the inputs that the model is observing. And so therefore, you now know how the moral is treating each of these inputs. And so now you have an idea of like where the person is getting effected by this loaner's mark. So now as a human, as an underwriter or a loan officer lending officer, I have knowledge about how the model is working. I can then have my human intuition or lap on it. I can approve the model sometimes I can disapprove the model sometimes. I can use this feedback and deliver it to the data science team, the AI team, so they can actually make the model better over time. So this unlocking black box has several benefits throughout their life cycle. >> That's awesome. Great definition. Great call. I want to grab get that on the record for the audience. Also, we'll make a clip out of that too. One of the things that I meant you brought up I love and want to get into is this MLOps impact. So as we were just talking earlier debugging module models and production, totally cool, relevant, unpacked a black box. But model decay, that's an interesting concept. Can you explain more? Because this to me, I think is potentially a big blind spot for the industry, because, you know, I talked to Swami at Amazon, who runs their AI group and, you know, they want to make AI easier and ML easier with SageMaker and other tools. But you can fall into a trap of thinking everything's done at one and done. It's iterative is you've got leverage here. You got to keep track of the performance of the models, not just debugging them. Are they actually working? Is there new data? This is a whole another practice. Could you explain this concept of model decay? >> Yeah, so let's look at the lending example Krishna was just talking about. If you expect your customers to be your citizens, right? So you will have examples in your training set which might have historical loans made to people that the needs of 40, and let's say 70. And so you will train your model and your model will be trained our highest accuracy in making loans to these type of applicants. But now let's say introduced a new loan product that you're targeting, let's say younger college going folks. So that model is not trained to work well in those kinds of scenarios. Or it could also happen that you could get a lot more older people coming in to apply for these loans. So the data that the model can see in life might not represent the data that you train the model with. And the model has recognized relationships in this data and it might not recognize relationships in this new data. So this is a constant, I would say, it's an ongoing challenge that you would face when you have a live model in ensuring that the reality meets your representation of the reality when you train the model. And so this is something that's unique to machine learning models and it has not been a problem historically in the world of DevOps. But it is a very key problem in the DevOps. >> This is really great topic. And most people who are watching might want to might know of some of these problems when they see the main mainstream press talk about fairness in black versus white skin and bias and algorithms. I mean, that's kind of like the press state that talk about those kinds of like big high level topics. But what it really means is that the data (John laughs) of practiced fairness and bias and skewing and all kinds of new things that come up that the machines just can't handle. This is a big deal. So this is happening to every part of data in an organization. So, great problem statement. I guess the next segue would be, why Fiddler, why now? What are you guys doing? How are you solving these problems? Take us through some use cases. How people engage with you guys? How you solve the problem and how you guys see this evolving? >> Great, so Fiddler is a purpose-built platform to solve for model explainability of modern monitoring and moderate bias detection. This is the only thing that we do, right? So we are super focused on building this tool to be useful across a variety of, you know, AI problems, from financial services to retail, to advertising to human resources, healthcare and so on and so forth. And so we have found a lot of commonalities around how data scientists are solving these problems across these industries. And we've created a system that can be plugged into their workflows. For example, I could be a bank, you know, creating anti-money laundering models on a modern AI platform like TensorFlow. Or I could be like a retail company that is building a recommendation models in, you know, PyTorch, like library. You can bring all of those models into one under one sort of umbrella, like using Fiddler. We can support a variety of heterogeneous types of models. And that is a very very hard technical problem to solve. To be able to ingest and digest all these different types of monotypes and then provide a single pane of glass in terms of how the model is performing. How explaining the model, tracking the model life cycle throughout its existence, right? And so that is the value prop that Fiddler offers, the MLOps team, so they can get this oversight. And so this plugs in nicely with their MLOps so they don't have to change anything and give the additional benefit... >> So, you're basically creating faster outcomes because the teams can work on real problems. >> Right. >> And not have to deal with the maintenance of model management. >> Right. >> Whether it's debugging or decay evaluations, right? >> Right, we take care of all of their model operations from a monitoring standpoint, analysis standpoint, debugability, alerting. So that they can just build the right kind of models for their customers. And we give them all the insights and intelligence to know the problems with behind those models behind their datasets. So that they can actually build more accurate models more responsible models for their customers. >> Okay, Amit, give us the secret sauce. What's going on in the product? How does it all work? What's the secret sauce? >> So there are three key kind of pillars to Fiddler product. One is of course, we leverage the latest research, and we actually productize that in like amazing ways where when you explain models you get the explanation within a second. So this activates new use cases like, let's say counterfactual analysis. You can not only get explanations for your loan, you can also see hypothetically. What if this the loan applicant was, you know, had a higher income? What would the model do? So, that's one part productizing latest research. The second part is infrastructure at scale. So we are not just building something that would work for SMBs. We are building something that works on enterprise scale. So billions and billions of predictions, right? Flowing through the system. We want to make sure that we can handle as larger scale as seamlessly as kind of possible. So we are trying to activate that and making sure we are the best enterprise grade product on the market. And thirdly, user experience. What you'll see when you use Fiddler. Finally, when we do demos to kind of customers what they really see is the product. They don't see that the scale right, right, right then and there. They don't see the deep reason. What they see, what they see are these like beautiful experiences that are very intuitive to them. Where we've merged explainability and monitoring and bias detection in like seamless way. So you get the most intuitive experiences that are not just designed for the technical user, but also for the non-technical user. Who are also stakeholders within AI. >> So the scale thing is a huge point, by the way. I think that's something that you see successful companies. That's a differentiator and frankly, it's the new sustainability. So new lock-in, if you will, not to be in a bad way but in a good way. You do a good job. You get scale, you get leverage. I want to just point out and get your guys' thoughts on your approach on the frame. Where you guys are centralized. >> Right. >> So as decentralization continues to be a wave you guys are taking much more of a centralized approach. Why is that done? Take us through the decision on that. >> Yeah. So, I mean, in terms of, you know decentralization in terms of running models on different you know, containers and, you know, scoring them on multiple number of nodes, that's absolutely makes sense, right? When from a deployment standpoint from a inference standpoint. But when it comes to actually you know, understanding how the models are working. Visualizing them, monitoring them, knowing what's going on with the models. You need a centralized dashboard that a lapsed user can actually use or a head of AI governance inside a bank and use what are all the models that my team is shipping? You know, which models carry risk, you know? How are these models performing last week? This, you need a centralized repository. Otherwise, it'll be very very hard to track these models, right? Because the models are going to grow really really fast. You know, there are so many open source libraries, open source model architecture has been produced. And so many data scientists coming out of grad schools and whatnot. And the number of models in enterprise is just going to grow many many fold in the coming years. Now, how are you going to track all of these things without having a centralized platform? And that's what we envisaged a few years ago that every team will need an oversight tool like Fiddler. Which can keep track of all of their models in one place. And that's what we are finding from our customers. >> As long as you don't get in the way of them creating value, which is the goal, right? >> Right. >> And be frictionless take away the friction. >> Yeah. >> And enable it. Love the concept. I think you guys are on something big there, great products. Great vision. The question I have for you to kind of wrap things up here. Is that this is all new, right? And new, it's all goodness, right? If you've got scale in the Cloud, all these new benefits. Again, more techies coming out of grad school and Computer Science and Engineering, and just data analysis in general is changing. And there's more people to be democratized to be contributing. >> Right. >> How do you operationalize it? How do companies get this going? Because you've got a new thing happening. It's a new wave. >> Okay. >> But it's still the same game, make business run better. >> Right. >> So you've got to deploy something new. What's the operational playbook for companies to get started? >> Absolutely. First step is to, if a company is trying to install AI, incorporate AI into their workflow. You know, most companies I would say, they're in still early stages, right? There a lot of enterprises are still, you know, developing these models. Some of them may have been in labs. ML operationalization is starting to happen and it probably started in a year or two ago, right? So now when it comes to, you know, putting AI into practice, so far, you know, you can have AI models in labs. They're not going to hurt anyone. They're not going to hurt your business. They're not going to hurt your users. But once you operationalize them then you have to do it in a proper manner, in a responsible manner, in a trustworthy manner. And so we actually have a playbook in terms of how you would have to do this, right? How are you going to test these models? How are you going to analyze and validate them before they actually are deployed? How are you going to analyze, you know, look into data bias and training set bias, or test set bias. And once they are deployed to production are you tracking, you know, model performance or time? Are you tracking drifting models? You know, the decay part that we talked about. Do you have alerts in place when model performance goes all over the place? Now, all of a sudden, suddenly you get a lot of false positives in your fraud models. Are you able to track them? We have the personnel in place. You have the data scientists, the ML engineers, the MLOps engineers, the governance teams in place if it's in a regulated industry to use these tools. And then, the tools like Fiddler, will add value, will make them, you know, do their job, institutionalize this process of responsible AI. So that they're not only reaping the benefits of this great technology. There's no doubt about the AI, right? It's actually, it's going to be game changing but then they can also do it in a responsible and trustworthy manner. >> Yeah, it's really get some wins, get some momentum, see it. This is the Cloud way. It gets them some value immediately and grow from there. I was talking to a friend the other day, Amit, about IT the lecture. I don't worry about IT and all the Cloud. I go, there's no longer IT, IT is dead. It's an AI department now. (Amit laughs) So and this is kind of what you guys are getting at. This now it's data now it's AI. It's kind of like what IT used to be enabling organizations to be successful. You guys are looking at it from the perspective of the same way it's enabled success. You put it out that you provision (John laughs) algorithms instead of servers they're algorithms now. This is the new model. >> Yeah, we believe that all companies in the future as it happened to this wave of data are going to be AI companies, right? So it's really just a matter of time. And the companies that are first movers in this are going to have a significant advantage like we're seeing that in like banking already. Where the banks that have made the leap into AI battles are reaping benefits of enabling a lot more models at the same risk profile using deep learning models. As long as you're able to like validate these to ensure that they're meeting kind of like the regulations. But it's going to give significant advantages to a lot of companies as they move faster with respect to others in the same industry. >> Yeah, quickers too, saw a friend too on the compliance side. You mentioned trust and transparency with the whole EU thing. Some are saying that, you know, to be a public company, you're going to have to have AI disclosure soon. You're going to have to have on the disclosure in your public statements around how you're explaining your AI. Again, fantasy today. But pretty plausible. >> Right, absolutely. I mean, the real reality today is, you know less than 10% of the CEOs care about ethical AI, right? And that has to change. And I think, you know, and I think that has to change for the better, because at the end of the day, if you are using AI, if you're not using in a responsible and trustworthy manner then there is like regulation. There is compliance risk, there's operational business risk. You know, customer trust. Losing customers trust can be huge. So I think, you know, we want to provide that you know, insurance, or like, you know like a preventative mechanism. So that, you know, if you have these tools in place then you're less likely to get into those situations. >> Awesome. Great, great conversation, Krishna, Amit. Thank you for sharing both the founders of Fiddler.ai. Great company. On the right side of history in my opinion, the next big thing in AI. AI departments, AI compliance, AI reporting. (John laughs) Explainable AI, ethical AI, all part of this next revolution. Gentlemen, thank you for joining us on theCUBE Amazon Startup Showcase. >> Thanks for having us, John. >> Okay, it's theCUBE coverage. Thank you for watching. (upbeat music)

(upbeat pop music) >> Well, hi everyone, John Walls here on "theCUBE" as we continue our CUBE conversations as part of the "AWS Startup Showcase". And we welcome in today Krishna Gade who is the founder and the CEO of Fiddler AI. and Krishna, good to see you today. Thanks for joining us here on the "theCUBE". >> Hey John, thanks so much for inviting us and I'm glad to be here, and looking forward to our conversation. >> Yeah me two, and first off, I want to say congratulations as I look at your company's, this tremendous roster, this list of awards that just keep coming your way. Most recently recognized by "Forbes" as one of the Top 50 AI Companies To Watch here in 2021. I know Gartner called you one of their Cool Companies not too long ago. World Economic Forum also giving you a shout out. So whatever it is you're doing, you're doing it very well, but it's got to feel good I would think, some validation to get all this kind of recognition. >> Absolutely, I know we've been very fortunate to get all the recognition. You know, part of it is also because of the space we are playing in, right? A lot of companies are, you know, operationalizing AI and therefore, you know, this whole point of, you know, explainability monitoring and governance of AI is like forefront and it's in the news for various different reasons. So there's a lot of, you know, good sort of talk that is going on in the press around how one should bear responsible AI. And we are very fortunate to be, you know, in the space and pioneering, you know, some of the technologies here. >> Right. And talking about machine learning monitoring, obviously, in the AI space, and you mentioned explainability. So let's just talk about that concept broadly first off and explain to our viewers what you mean by explainability in this particular context. >> Yeah, that's a good question. So if you think about an AI system, one of the main differences between it and a traditional software system is that it's a black box in the sense that you cannot open it up and read it's code like a traditional software system. The reason is, you know, the AI systems that are built using data and training models which are represented in this non-human readable format. And you cannot really understand how a model is actually making a prediction at any given point of time. So therefore what happens is when you are deploying these AI systems at scale for a variety of use cases, let's say credit underwriting or, you know, screening resumes, or clinical diagnosis which are extremely, you know, important for general human beings. There is a need to understand how the AI system is working. You know, why did it approve a positive person's loan or reject someone's loan? Or why did it reject someone's, you know, resume from, you know, a job screening pipeline? How is it working overall? Right? And so this is where explainability becomes important because you need to understand the AI system, you need a way to probe it, to interrogate it, to understand how the system is making predictions, how is it being influenced by various inputs you're supplying to the system. And so this gamut of technologies or the algorithms that have come across in the last, you know, few years have really matured to a point where, you know, products like Fiddler are developing them and productizing them for the general enterprise to you know, put it in their machine learning and AI workflows. >> So you're talking about context basically, right? I mean, trying to give everybody an idea. This is, you know, kind of where this inputs coming, this is where the problem is, this is where the bottleneck might be, whatever it is, and and doing that in real time. Very efficient operation here. Well, let's talk about the ML world right now and in terms of how it relates to artificial intelligence and this interaction you know, that we're seeing and the, I guess, the problem that you are trying to fix, if you will, in terms of machine learning monitoring. So let's just deal with that first off. When you look at somebody's architecture and somebody set up, what do you see? What are you looking for? And what kind of problems are you trying to solve for your clients? >> Yeah. So just following up what I said. The two main problems with operationalizing AI is one is the black box nature of AI, which I already talked about. The other problem is that the AI system is fundamentally a stochastic system or a probabilistic system. By that, I mean that its performance, you know, its predictions can change over time based on the data it is receiving. So it's not a deterministic system like traditional software systems where you expect the same output all the time, right? So when you have a system that is stochastic in nature where its performance can vary based on the data it is receiving, then you are in a situation where you have uncertainty, right? You know, you let's say you have an AI system that is deployed for serving a credit underwriting model or a fraud, you know, detection use case. And you see that, okay, sometimes accuracy is up, sometimes accuracy is down. You know, when do you want, when do you trust your predictions, when you're not. How do you know if the model is actually performing in the same manner that you trained it? All of these issues open up the need for continuous monitoring of these AI systems, because without which you may have AI systems making bad predictions for your users, hurting your business metrics, potentially making biased decisions that can put your company into a compliance or a brand reputation risk scenario. To avoid all of these things you can actually monitor these AI systems continuously so that you know exactly if they're performing the way you expect them to be. Do you to retrain them right now, right? Or do you need to shut them down because they are actually not predicting the way that you expect them to be? So this is actually very important. And so that's what Fiddler tries to solve for our customers by helping them operationalize AI with full visibility and explainability, right? So you can essentially install Fiddler in your workflow to continuously monitor your AI systems and analyze and explain them when you have questions about how they're working. >> I mean, you talked about governance earlier a little bit, you know, compliance, obviously a great critical issue, big concern, fraud detection. Security, just in general here, as we know, I mean, we keep almost every day it seems like we're hearing about some kinds of security intrusion. So, in terms of identifying vulnerabilities or in terms of identifying anomalies, whatever it might be, what kind of work are you doing in that space to give your client base the kind of comfort and the peace of mind that everybody's searching for these days? >> Right, I mean, if you step back a little bit, John, we are truly living in the age of algorithms, right? So everything that we interact with on a day-to-day basis, the movies we watch, or when we request an Uber driver, or when we go to a financial institution and request for a loan application or a mortgage, there are algorithms behind the scenes that are processing our requests and delivering the experiences that we have. Now, increasingly these algorithms are becoming AI based algorithms. And when you have these AI based algorithms, they're trained on this data that's available, that an institution may collect from their users, or they may buy from other third parties. And when you develop these AI systems based on this data, if this data is not equally distributed amongst all different ethnicity backgrounds, people coming from different cultures, different religions, different races, different genders, you may actually build systems that can make very different decisions for different individuals based on like this bias that could creep into them. And so this actually needs, this means that at the end of the day, you can actually create a dystopian world where, you know, some people get like really great decisions from your systems, where some people are left out, right? So therefore, you know, this aspect of governing your AI systems so that you're validating what you're building upfront. You're validating the data that you're using to train the systems. You're continuously monitoring the systems there so that they're actually producing the right outcomes for your users. And then you can actually explain if some customer asks you or some regulator or a third party asks you how your system is working. It's very very important. This is an emerging area in industry, certain sectors already have this, for example, financial services. It's in companies like banks, where it is mandated to have model governance, so that every model that they are deploying needs to be validated and needs to be monitored. And we are seeing the emergence of generally AI governance creeping into other sectors as well. And so this is like a broader topic that covers explainability, covers monitoring, covers detecting bias in your AI systems and ensuring that you're building safe and responsible AI for your customers and your organization. >> Yeah, I find the bias point really interesting, actually, because I hadn't really thought about these prejudices or subjectivities, you know, it might bring to our work with us in terms of what we look at, what we ignore, what we process, how we don't. But it's a really interesting point you just raised. So thank you for that. And then there's also the kind of issue with data drift too a little bit, right? It's like, where did it go (laughing)? >> Right. >> What are we doing here? What happened to it? So maybe if you could talk about that a little bit in terms of all this data that's coming in and corralling it, right? Making sure that it stays organized and stays in a way that you can analyze and process it, and then glean insight from. >> Yeah, data drift is one of the main reasons why AI systems deteriorate in performance. So for example, let's say I'm trying to build a recommendation system that predicts the items that you want to buy when you go to an E-commerce website. Now, if I have used data pre-COVID, then the user behavior was very different, right? That kind of items people were probably buying before you know, February, 2020 was like probably much different than the kind of items that people were buying after it. So what happens is when you train your AI systems on datasets that are older but then that data has changed ever since because of an event like COVID-19 has happened, or some other seasonality has kicked in, then your AI systems are seeing different distribution data. For example, you may see that suddenly, you know, people who were shopping, let's say, in March or April last year, people were shopping for all kinds of, you know, toilet paper and all kinds of things to stock up, you know, to be ready for lockdown, right? And maybe they were not buying similar amounts in there previously. So therefore, if you have an inventory management system based on AI or an E-commerce recommendation system based on AI, you know, they would see data drift, because the buying patterns are different. The amount of stuff that people are buying in terms of toilet paper has completely shifted. And so their model is actually, may not be predicting as accurately as it would, right? So therefore identifying this data drift and alerting your AI engineer so that they can be prepared for this is very important. Otherwise, what you would see is if you're an E-commerce company, this has actually happened, you know? Instacart, a grocery delivery company and another company www.etsy.com, they blogged about it where they have seen their models go down in accuracy from 90% to 65% when this data shift happened, you know, especially during COVID-19. And so you need the ability to continuously monitor for drift so that when you can catch these things earlier, and then, you know, save your business from losing, you know, in terms of business metrics like such as number of sales that you may be making, number of bad recommendations that your systems are making to your users. >> So we've talked a lot about these various components of monitoring of which, you know, all of which you do extremely well. And I was reading earlier, just a little bit about the company, and we talked about accountability. We've already talked about that. We talked about fraud detection, we talked about reliability. There was also a point about ethical considerations, you know, and so I was interested in that, hearing from you about that in terms of why that's a pillar of your service or what exactly that was pointed toward in terms of monitoring, and what you can do. >> Right. So, I guess I'll just go back to like a famous quote from Marc Andreessen. He mentioned, you know, a few years ago that software is eating the world, right? Now, what's happening is AI is eating software. All the software that we are consuming is becoming AI based software, because basically at the end of the day some intelligence is being baked into the software to make it, you know, predict more interesting things for you to make those decisions. Instead of rule-based decisions, make it more AI based decisions. And so therefore it is very important that when we are building the software, we need to use ethical practices. You know, we need to know how, where you're collecting the data from. It can be very dangerous if you don't do it and you can land into trouble. And we have seen these incidents many times, right? For example, in 2019, when Apple and Goldman Sachs came up with a credit card, a lot of customers complained about gender bias with respect to the credit card limits that the algorithm was setting. You know, in the same household, the husband and wife were getting 10 times in terms of a difference between the credit limit between a male and a female, right? Even though they probably had similar salary ranges, similar FICO scores, right? So if you do not actually make sure that, you know, you're collecting data from the right sources that your datasets are not outbalanced. If your models, if your algorithms are tested for bias you know, before hand, before you deploy them and then you're continuously monitoring them, these are all ethical practices. These are all the responsible ways of building your AI. You can actually, you know, land into trouble. Your customers will complain about it. You know, you would lose your brand reputation. And at the end of the day you'll be essentially, and instead of actually adding value to the customers, you may be actually hurting them, right? And so this is actually why it's so important, and it's become more important when the more stakes, the higher the stakes are, right? You know, for example, when it's being used for criminal justice scenarios or when it's being used for clinical diagnosis scenarios. Being able to ensure that the system is making unbiased decisions is very, very important. >> Well, before I let you go, too, I like you to touch base on your AWS relationship about, you know, what was the Genesis of that. And currently what it is that you're working on together to provide this great value to your customers. >> Absolutely. So the follow-up to this ethical AI is like Amazon as a company is interested in pursuing, you know, the responsible AI but, you know, they have a lot of AI products. So they are looking for, you know, fostering a community and ecosystem of AI technologies. And in that hypothesis they actually invested in Fiddler last year in terms of enabling us to develop this explainable AI and ethical AI technology. And so we are working with Alexa Fund and also like AWS ecosystem in terms of partnering with how effectively Fiddler can be delivered to other AWS customers through, like, through their marketplace and other sort of areas that we can distribute the software. So it's a great partnership. We are very, very excited about the opportunity to work with Alexa Fund as well as the AWS ecosystem. It increases another opportunity for us to enable a lot more customers than we than we can otherwise. So this is a great win-win situation for both Amazon and Fiddler. >> Well, it sure is. And congratulations on that and developing that partnership. I know it's working well for your clients and it's working well for Fiddler AI obviously by the number of recognitions that have been coming your way. So Krishna, we wish you continued success and thanks for the time here today on "theCUBE". >> Yep. Thank you so much, John. It was a pleasure talking to you today. >> I enjoyed it. Thank you. John Walls here wrapping up our conversation with Fiddler AI's Krishna Gade, talking today about machine learning monitoring on the "AWS Startup Showcase". (upbeat pop music)

>> From the Cube Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> Hi everyone, this is Dave Vellante and welcome back to the Cube's coverage of the MIT CDOIQ. God, we've been covering this show since probably 2013, really trying to understand the intersection of data and organizations and data quality and how that's evolved over time. And with me to discuss these issues is Krishna Cheriath, who's the Vice President and Chief Data Officer, Bristol-Myers Squibb. Krishna, great to see you, thanks so much for coming on. >> Thank you so much Dave for the invite, I'm looking forward to it. >> Yeah first of all, how are things in your part of the world? You're in New Jersey, I'm also on the East coast, how you guys making out? >> Yeah, I think these are unprecedented times all around the globe and whether it is from a company perspective or a personal standpoint, it is how do you manage your life, how do you manage your work in these unprecedented COVID-19 times has been a very interesting challenge. And to me, what is most amazing has been, I've seen humanity rise up and so to our company has sort of snap to be able to manage our work so that the important medicines that have to be delivered to our patients are delivered on time. So really proud about how we have done as a company and of course, personally, it has been an interesting journey with my kids from college, remote learning, wife working from home. So I'm very lucky and blessed to be safe and healthy at this time. So hopefully the people listening to this conversation are finding that they are able to manage through their lives as well. >> Obviously Bristol-Myers Squibb, very, very strong business. You guys just recently announced your quarter. There's a biologics facility near me in Devon's, Massachusetts, I drive by it all the time, it's a beautiful facility actually. But extremely broad portfolio, obviously some COVID impact, but you're managing through that very, very well, if I understand it correctly, you're taking a collaborative approach to a COVID vaccine, you're now bringing people physically back to work, you've been very planful about that. My question is from your standpoint, what role did you play in that whole COVID response and what role did data play? >> Yeah, I think it's a two part as you rightly pointed out, the Bristol-Myers Squibb, we have been an active partner on the the overall scientific ecosystem supporting many different targets that is, from many different companies I think. Across biopharmaceuticals, there's been a healthy convergence of scientific innovation to see how can we solve this together. And Bristol-Myers Squibb have been an active participant as our CEO, as well as our Chief Medical Officer and Head of Research have articulated publicly. Within the company itself, from a data and technology standpoint, data and digital is core to the response from a company standpoint to the COVID-19, how do we ensure that our work continues when the entire global workforce pivots to a kind of a remote setting. So that really calls on the digital infrastructure to rise to the challenge, to enable a complete global workforce. And I mean workforce, it is not just employees of the company but the all of the third-party partners and others that we work with, the whole ecosystem needs to work. And I think our digital infrastructure has proven to be extremely resilient than that. From a data perspective, I think it is twofold. One is how does the core book of business of data continue to drive forward to make sure that our companies key priorities are being advanced. Secondarily, we've been partnering with a research and development organization as well as medical organization to look at what kind of real world data insights can really help in answering the many questions around COVID-19. So I think it is twofold. Main summary; one is, how do we ensure that the data and digital infrastructure of the company continues to operate in a way that allows us to progress the company's mission even during a time when globally, we have been switched to a remote working force, except for some essential staff from lab and manufacturing standpoint. And secondarily is how do we look at the real-world evidence as well as the scientific data to be a good partner with other companies to look at progressing the societal innovations needed for this. >> I think it's a really prudent approach because let's face it, sometimes one shot all vaccine can be like playing roulette. So you guys are both managing your risk and just as I say, financially, a very, very successful company in a sound approach. I want to ask you about your organization. We've interviewed many, many Chief Data Officers over the years, and there seems to be some fuzziness as to the organizational structure. It's very clear with you, you report in to the CIO, you came out of a technical bag, you have a technical degree but you also of course have a business degree. So you're dangerous from that standpoint. You got both sides which is critical, I would think in your role, but let's start with the organizational reporting structure. How did that come about and what are the benefits of reporting into the CIO? >> I think the Genesis for that as Bristol-Myers Squibb and when I say Bristol-Myers Squibb, the new Bristol-Myers Squibb is a combination of Heritage Bristol-Myers Squibb and Heritage Celgene after the Celgene acquisition last November. So in the Heritage Bristol-Myers Squibb acquisition, we came to a conclusion that in order for BMS to be able to fully capitalize on our scientific innovation potential as well as to drive data-driven decisions across the company, having a robust data agenda is key. Now the question is, how do you progress that? Historically, we had approached a very decentralized mechanism that made a different data constituencies. We didn't have a formal role of a Chief Data Officer up until 2018 or so. So coming from that realization that we need to have an effective data agenda to drive forward the necessary data-driven innovations from an analytic standpoint. And equally importantly, from optimizing our execution, we came to conclusion that we need an enterprise-level data organization, we need to have a first among equals if you will, to be mandated by the CEO, his leadership team, to be the kind of an orchestrator of a data agenda for the company, because data agenda cannot be done individually by a singular CDO. It has to be done in partnership with many stakeholders, business, technology, analytics, et cetera. So from that came this notion that we need an enterprise-wide data organization. So we started there. So for awhile, I would joke around that I had all of the accountabilities of the CDO without the lofty title. So this journey started around 2016, where we create an enterprise-wide data organization. And we made a very conscious choice of separating the data organization from analytics. And the reason we did that is when we look at the bowl of Bristol-Myers Squibb, analytics for example, is core and part of our scientific discovery process, research, our clinical development, all of them have deep data science and analytic embedded in it. But we also have other analytics whether it is part of our sales and marketing, whether it is part of our finance and our enabling functions they catch all across global procurement et cetera. So the world of analytics is very broad. BMS did a separation between the world of analytics and from the world of data. Analytics at BMS is in two modes. There is a central analytics organization called Business Insights and Analytics that drive most of the enterprise-level analytics. But then we have embedded analytics in our business areas, which is research and development, manufacturing and supply chain, et cetera, to drive what needs to be closer to the business idea. And the reason for separating that out and having a separate data organization is that none of these analytic aspirations or the business aspirations from data will be met if the world of data is, you don't have the right level of data available, the velocity of data is not appropriate for the use cases, the quality of data is not great or the control of the data. So that we are using the data for the right intent, meeting the compliance and regulatory expectations around the data is met. So that's why we separated out that data world from the analytics world, which is a little bit of a unique construct for us compared to what we see generally in the world of CDOs. And from that standpoint, then the decision was taken to make that report for global CIO. At Bristol-Myers Squibb, they have a very strong CIO organization and IT organization. When I say strong, it is from this lens standpoint. A, it is centralized, we have centralized the budget as well as we have centralized the execution across the enterprise. And the CDO reporting to the CIO with that data-specific agenda, has a lot of value in being able to connect the world of data with the world of technology. So at BMS, their Chief Data Officer organization is a combination of traditional CDO-type accountabilities like data risk management, data governance, data stewardship, but also all of the related technologies around master data management, data lake, data and analytic engineering and a nascent AI data and technology lab. So that construct allows us to be a true enterprise horizontal, supporting analytics, whether it is done in a central analytics organization or embedded analytics teams in the business area, but also equally importantly, focus on the world of data from operational execution standpoint, how do we optimize data to drive operational effectiveness? So that's the construct that we have where CDO reports to the CIO, data organization separated from analytics to really focus around the availability but also the quality and control of data. And the last nuance that is that at BMS, the Chief Data Officer organization is also accountable to be the Data Protection Office. So we orchestrate and facilitate all privacy-related actions across because that allows us to make sure that all personal data that is collected, managed and consumed, meets all of the various privacy standards across the world, as well as our own commitments as a company from across from compliance principles standpoint. >> So that makes a lot of sense to me and thank you for that description. You're not getting in the way of R&D and the scientists, they know data science, they don't need really your help. I mean, they need to innovate at their own pace, but the balance of the business really does need your innovation, and that's really where it seems like you're focused. You mentioned master data management, data lakes, data engineering, et cetera. So your responsibility is for that enterprise data lifecycle to support the business side of things, and I wonder if you could talk a little bit about that and how that's evolved. I mean a lot has changed from the old days of data warehouse and cumbersome ETL and you mentioned, as you say data lakes, many of those have been challenging, expensive, slow, but now we're entering this era of cloud, real-time, a lot of machine intelligence, and I wonder if you could talk about the changes there and how you're looking at and thinking about the data lifecycle and accelerating the time to insights. >> Yeah, I think the way we think about it, we as an organization in our strategy and tactics, think of this as a data supply chain. The supply chain of data to drive business value whether it is through insights and analytics or through operation execution. When you think about it from that standpoint, then we need to get many elements of that into an effective stage. This could be the technologies that is part of that data supply chain, you reference some of them, the master data management platforms, data lake platforms, the analytics and reporting capabilities and business intelligence capabilities that plug into a data backbone, which is that I would say the technology, swim lane that needs to get right. Along with that, what we also need to get right for that effective data supply chain is that data layer. That is, how do you make sure that there is the right data navigation capability, probably you make sure that we have the right ontology mapping and the understanding around the data. How do we have data navigation? It is something that we have invested very heavily in. So imagine a new employee joining BMS, any organization our size has a pretty wide technology ecosystem and data ecosystem. How do you navigate that, how do we find the data? Data discovery has been a key focus for us. So for an effective data supply chain, then we knew that and we have instituted our roadmap to make sure that we have a robust technology orchestration of it, but equally important is an effective data operations orchestration. Both needs to go hand in hand for us to be able to make sure that that supply chain is effective from a business use case and analytic use standpoint. So that has led us on a journey from a cloud perspective, since you refer that in your question, is we have invested very heavily to move from very disparate set of data ecosystems to a more converse cloud-based data backbone. That has been a big focus at the BMS since 2016, whether it is from a research and development standpoint or from commercialization, it is our word for the sales and marketing or manufacturing and supply chain and HR, et cetera. How do we create a converged data backbone that allows us to use that data as a resource to drive many different consumption patterns? Because when you imagine an enterprise of our size, we have many different consumers of the data. So those consumers have different consumption needs. You have deep data science population who just needs access to the data and they have data science platforms but they are at once programmers as well, to the other end of the spectrum where executives need pre-packaged KPIs. So the effective orchestration of the data ecosystem at BMS through a data supply chain and the data backbone, there's a couple of things for us. One, it drives productivity of our data consumers, the scientific researchers, analytic community or other operational staff. And second, in a world where we need to make sure that the data consumption appalls ethical standards as well as privacy and other regulatory expectations, we are able to build it into our system and process the necessary controls to make sure that the consumption and the use of data meets our highest trust advancements standards. >> That makes a lot of sense. I mean, converging your data like that, people always talk about stove pipes. I know it's kind of a bromide but it's true, and allows you to sort of inject consistent policies. What about automation? How has that affected your data pipeline recently and on your journey with things like data classification and the like? >> I think in pursuing a broad data automation journey, one of the things that we did was to operate at two different speed points. In a historically, the data organizations have been bundled with long-running data infrastructure programs. By the time you complete them, their business context have moved on and the organization leaders are also exhausted from having to wait from these massive programs to reach its full potential. So what we did very intentionally from our data automation journey is to organize ourselves in two speed dimensions. First, a concept called Rapid Data Lab. The idea is that recognizing the reality that the data is not well automated and orchestrated today, we need a SWAT team of data engineers, data SMEs to partner with consumers of data to make sure that we can make effective data supply chain decisions here and now, and enable the business to answer questions of today. Simultaneously in a longer time horizon, we need to do the necessary work of moving the data automation to a better footprint. So enterprise data lake investments, where we built services based on, we had chosen AWS as the cloud backbone for data. So how do we use the AWS services? How do we wrap around it with the necessary capabilities so that we have a consistent reference and technical architecture to drive the many different function journeys? So we organized ourselves into speed dimensions; the Rapid Data Lab teams focus around partnering with the consumers of data to help them with data automation needs here and now, and then a secondary team focused around the convergence of data into a better cloud-based data backbone. So that allowed us to one, make an impact here and now and deliver value from data to the dismiss here and now. Secondly, we also learned a lot from actually partnering with consumers of data on what needs to get adjusted over a period of time in our automation journey. >> It makes sense, I mean again, that whole notion of converged data, putting data at the core of your business, you brought up AWS, I wonder if I could ask you a question. You don't have to comment on specific vendors, but there's a conversation we have in our community. You have AWS huge platform, tons of partners, a lot of innovation going on and you see innovation in areas like the cloud data warehouse or data science tooling, et cetera, all components of that data pipeline. As well, you have AWS with its own tooling around there. So a question we often have in the community is will technologists and technology buyers go for kind of best of breed and cobble together different services or would they prefer to have sort of the convenience of a bundled service from an AWS or a Microsoft or Google, or maybe they even go best of breeds for all cloud. Can you comment on that, what's your thinking? >> I think, especially for organizations, our size and breadth, having a converged to convenient, all of the above from a single provider does not seem practical and feasible, because a couple of reasons. One, the heterogeneity of the data, the heterogeneity of consumption of the data and we are yet to find a single stack provider who can meet all of the different needs. So I am more in the best of breed camp with a few caveats, a hybrid best of breed, if you will. It is important to have a converged the data backbone for the enterprise. And so whether you invest in a singular cloud or private cloud or a combination, you need to have a clear intention strategy around where are you going to host the data and how is the data is going to be organized. But you could have a lot more flexibility in the consumption of data. So once you have the data converged into, in our case, we converged on AWS-based backbone. We allow many different consumptions of the data, because I think the analytic and insights layer, data science community within R&D is different from a data science community in the supply chain context, we have business intelligence needs, we have a catered needs and then there are other data needs that needs to be funneled into software as service platforms like the sales forces of the world, to be able to drive operational execution as well. So when you look at it from that context, having a hybrid model of best of breed, whether you have a lot more convergence from a data backbone standpoint, but then allow for best of breed from an analytic and consumption of data is more where my heart and my brain is. >> I know a lot of companies would be excited to hear that answer, but I love it because it fosters competition and innovation. I wish I could talk for you forever, but you made me think of another question which is around self-serve. On your journey, are you at the point where you can deliver self-serve to the lines of business? Is that something that you're trying to get to? >> Yeah, I think it does. The self-serve is an absolutely important point because I think the traditional boundaries of what you consider the classical IT versus a classical business is great. I think there is an important gray area in the middle where you have a deep citizen data scientist in the business community who really needs to be able to have access to the data and I have advanced data science and programming skills. So self-serve is important but in that, companies need to be very intentional and very conscious of making sure that you're allowing that self-serve in a safe containment sock. Because at the end of the day, whether it is a cyber risk or data risk or technology risk, it's all real. So we need to have a balanced approach between promoting whether you call it data democratization or whether you call it self-serve, but you need to balance that with making sure that you're meeting the right risk mitigation strategy standpoint. So that's how then our focus is to say, how do we promote self-serve for the communities that they need self-serve, where they have deeper levels of access? How do we set up the right safe zones for those which may be the appropriate mitigation from a cyber risk or data risk or technology risk. >> Security pieces, again, you keep bringing up topics that I could talk to you forever on, but I heard on TV the other night, I heard somebody talking about how COVID has affected, because of remote access, affected security. And it's like hey, give everybody access. That was sort of the initial knee-jerk response, but the example they gave as well, if your parents go out of town and the kid has a party, you may have some people show up that you don't want to show up. And so, same issue with remote working, work from home. Clearly you guys have had to pivot to support that, but where does the security organization fit? Does that report separate alongside the CIO? Does it report into the CIO? Are they sort of peers of yours, how does that all work? >> Yeah, I think at Bristol-Myers Squibb, we have a Chief Information Security Officer who is a peer of mine, who also reports to the global CIO. The CDO and the CSO are effective partners and are two sides of the coin and trying to advance a total risk mitigation strategy, whether it is from a cyber risk standpoint, which is the focus of the Chief Information Security Officer and whether it is the general data consumption risk. And that is the focus from a Chief Data Officer in the capacities that I have. And together, those are two sides of a coin that the CIO needs to be accountable for. So I think that's how we have orchestrated it, because I think it is important in these worlds where you want to be able to drive data-driven innovation but you want to be able to do that in a way that doesn't open the company to unwanted risk exposures as well. And that is always a delicate balancing act, because if you index too much on risk and then high levels of security and control, then you could lose productivity. But if you index too much on productivity, collaboration and open access and data, it opens up the company for risks. So it is a delicate balance within the two. >> Increasingly, we're seeing that reporting structure evolve and coalesce, I think it makes a lot of sense. I felt like at some point you had too many seats at the executive leadership table, too many kind of competing agendas. And now your structure, the CIO is obviously a very important position. I'm sure has a seat at the leadership table, but also has the responsibility for managing that sort of data as an asset versus a liability which my view, has always been sort of the role of the Head of Information. I want to ask you, I want to hit the Escape key a little bit and ask you about data as a resource. You hear a lot of people talk about data is the new oil. We often say data is more valuable than oil because you can use it, it doesn't follow the laws of scarcity. You could use data in infinite number of places. You can only put oil in your car or your house. How do you think about data as a resource today and going forward? >> Yeah, I think the data as the new oil paradigm in my opinion, was an unhealthy, and it prompts different types of conversations around that. I think for certain companies, data is indeed an asset. If you're a company that is focused on information products and data products and that is core of your business, then of course there's monetization of data and then data as an asset, just like any other assets on the company's balance sheet. But for many enterprises to further their mission, I think considering data as a resource, I think is a better focus. So as a vital resource for the company, you need to make sure that there is an appropriate caring and feeding for it, there is an appropriate management of the resource and an appropriate evolution of the resource. So that's how I would like to consider it, it is a personal end of one perspective, that data as a resource that can power the mission of the company, the new products and services, I think that's a good, healthy way to look at it. At the center of it though, a lot of strategies, whether people talk about a digital strategy, whether the people talk about data strategy, what is important is a company to have a pool north star around what is the core mission of the company and what is the core strategy of the company. For Bristol-Myers Squibb, we are about transforming patients' lives through science. And we think about digital and data as key value levers and drivers of that strategy. So digital for the sake of digital or data strategy for the sake of data strategy is meaningless in my opinion. We are focused on making sure that how do we make sure that data and digital is an accelerant and has a value lever for the company's mission and company strategy. So that's why thinking about data as a resource, as a key resource for our scientific researchers or a key resource for our manufacturing team or a key resource for our sales and marketing, allows us to think about the actions and the strategies and tactics we need to deploy to make that effective. >> Yeah, that makes a lot of sense, you're constantly using that North star as your guideline and how data contributes to that mission. Krishna Cheriath, thanks so much for coming on the Cube and supporting the MIT Chief Data Officer community, it was a really pleasure having you. >> Thank you so much for Dave, hopefully you and the audience is safe and healthy during these times. >> Thank you for that and thank you for watching everybody. This is Vellante for the Cube's coverage of the MIT CDOIQ Conference 2020 gone virtual. Keep it right there, we'll right back right after this short break. (lively upbeat music)

>> Narrator: From around the globe it's theCUBE, covering HPE Discover Virtual Experience brought to you by HPE. >> Hi everybody. Welcome back. This is Dave Vellante for theCUBE, and this is our coverage of discover 2020, the virtual experience of HPE discover. We've done many, many discoveries, as usually we're on the show floor, theCUBE has been virtualized and we talk a lot at HPE discovers, a lot of storage and server and infrastructure and networking which is great. But the conversation we're going to have now is really, we're going to be talking about helping the world solve some big problems. And I'm very excited to welcome back to theCUBE Dr. Eng Lim Goh. He's a senior vice president of and CTO for AI, at HPE. Hello, Dr. Goh. Great to see you again. >> Hello. Thank you for having us, Dave. >> You're welcome. And then our next guest is Professor Joachim Schultze, who is the Professor for Genomics, and Immunoregulation at the university of Bonn amongst other things Professor, welcome. >> Thank you all. Welcome. >> And then Prasad Shastry, is the Chief Technologist for the India Advanced Development Center at HPE. Welcome, Prasad. Great to see you. >> Thank you. Thanks for having me. >> So guys, we have a CUBE first. I don't believe we've ever had of three guests in three separate times zones. I'm in a fourth time zone. (guests chuckling) So I'm in Boston. Dr. Goh, you're in Singapore, Professor Schultze, you're in Germany and Prasad, you're in India. So, we've got four different time zones. Plus our studio in Palo Alto. Who's running this program. So we've got actually got five times zones, a CUBE first. >> Amazing. >> Very good. (Prasad chuckles) >> Such as the world we live in. So we're going to talk about some of the big problems. I mean, here's the thing we're obviously in the middle of this pandemic, we're thinking about the post isolation economy, et cetera. People compare obviously no surprise to the Spanish flu early part of last century. They talk about the great depression, but the big difference this time is technology. Technology has completely changed the way in which we've approached this pandemic. And we're going to talk about that. Dr. Goh, I want to start with you. You've done a lot of work on this topic of swarm learning. If we could, (mumbles) my limited knowledge of this is we're kind of borrowing from nature. You think about, bees looking for a hive as sort of independent agents, but somehow they come together and communicate, but tell us what do we need to know about swarm learning and how it relates to artificial intelligence and we'll get into it. >> Oh, Dave, that's a great analogy using swarm of bees. That's exactly what we do at HPE. So let's use the of here. When deploying artificial intelligence, a hospital does machine learning of the outpatient data that could be biased, due to demographics and the types of cases they see more also. Sharing patient data across different hospitals to remove this bias is limited, given privacy or even sovereignty the restrictions, right? Like for example, across countries in the EU. HPE, so I'm learning fixers this by allowing each hospital, let's still continue learning locally, but at each cycle we collect the lumped weights of the neural networks, average them and sending it back down to older hospitals. And after a few cycles of doing this, all the hospitals would have learned from each other, removing biases without having to share any private patient data. That's the key. So, the ability to allow you to learn from everybody without having to share your private patients. That's swarm learning, >> And part of the key to that privacy is blockchain, correct? I mean, you you've been too involved in blockchain and invented some things in blockchain and that's part of the privacy angle, is it not? >> Yes, yes, absolutely. There are different ways of doing this kind of distributed learning, which swarm learning is over many of the other distributed learning methods. Require you to have some central control. Right? So, Prasad, and the team and us came up together. We have a method where you would, instead of central control, use blockchain to do this coordination. So, there is no more a central control or coordinator, especially important if you want to have a truly distributed swamp type learning system. >> Yeah, no need for so-called trusted third party or adjudicator. Okay. Professor Schultze, let's go to you. You're essentially the use case of this swarm learning application. Tell us a little bit more about what you do and how you're applying this concept. >> I'm actually by training a physician, although I haven't seen patients for a very long time. I'm interested in bringing new technologies to what we call precision medicine. So, new technologies both from the laboratories, but also from computational sciences, married them. And then I basically allow precision medicine, which is a medicine that is built on new measurements, many measurements of molecular phenotypes, how we call them. So, basically that process on different levels, for example, the genome or genes that are transcribed from the genome. We have thousands of such data and we have to make sense out of this. This can only be done by computation. And as we discussed already one of the hope for the future is that the new wave of developments in artificial intelligence and machine learning. We can make more sense out of this huge data that we generate right now in medicine. And that's what we're interesting in to find out how can we leverage these new technologies to build a new diagnostics, new therapy outcome predictors. So, to know the patient benefits from a disease, from a diagnostics or a therapy or not, and that's what we are doing for the last 10 years. The most exciting thing I have been  through in the last three, four, five years is really when HPE introduced us to swarm learning. >> Okay and Prasad, you've been helping Professor Schultze, actually implements swarm learning for specific use cases that we're going to talk about COVID, but maybe describe a little bit about what you've been or your participation in this whole equation. >> Yep, thank. As Dr Eng Lim Goh, mentioned. So, we have used blockchain as a backbone to implement the decentralized network. And through that we're enabling a privacy preserved these centralized network without having any control points, as Professor explained in terms of depression medicines. So, one of the use case we are looking at he's looking at the blood transcriptomes, think of it, different hospitals having a different set of transcriptome data, which they cannot share due to the privacy regulations. And now each of those hospitals, will clean the model depending upon their local data, which is available in that hospital. And shared the learnings coming out of that training with the other hospitals. And we played to over several cycles to merge all these learnings and then finally get into a global model. So, through that we are able to kind of get into a model which provides the performance is equal of collecting all the data into a central repository and trying to do it. And we could really think of when we are doing it, them, could be multiple kinds of challenges. So, it's good to do decentralized learning. But what about if you have a non ID type of data, what about if there is a dropout in the network connections? What about if there are some of the compute nodes we just practice or probably they're not seeing sufficient amount of data. So, that's something we tried to build into the swarm learning framework. You'll handle the scenarios of having non ID data. All in a simple word we could call it as seeing having the biases. An example, one of the hospital might see EPR trying to, look at, in terms of let's say the tumors, how many number of cases and whereas the other hospital might have very less number of cases. So, if you have kind of implemented some techniques in terms of doing the merging or providing the way that different kind of weights or the tuneable parameters to overcome these set of challenges in the swarm learning. >> And Professor Schultze, you you've applied this to really try to better understand and attack the COVID pandemic, can you describe in more detail your goals there and what you've actually done and accomplished? >> Yeah. So, we have actually really done it for COVID. The reason why we really were trying to do this already now is that we have to generate it to these transcriptomes from COVID-19 patients ourselves. And we realized that the scene of the disease is so strong and so unique compared to other infectious diseases, which we looked at in some detail that we felt that the blood transcriptome would be good starting point actually to identify patients. But maybe even more important to identify those with severe diseases. So, if you can identify them early enough that'd be basically could care for those more and find particular for those treatments and therapies. And the reason why we could do that is because we also had some other test cases done before. So, we used the time wisely with large data sets that we had collected beforehand. So, use cases learned how to apply swarm learning, and we are now basically ready to test directly with COVID-19. So, this is really a step wise process, although it was extremely fast, it was still a step wise probably we're guided by data where we had much more knowledge of which was with the black leukemia. So, we had worked on that for years. We had collected many data. So, we could really simulate a Swarm learning very nicely. And based on all the experience we get and gain together with Prasad, and his team, we could quickly then also apply that knowledge to the data that are coming now from COVID-19 patients. >> So, Dr. Goh, it really comes back to how we apply machine intelligence to the data, and this is such an interesting use case. I mean, the United States, we have 50 different States with 50 different policies, different counties. We certainly have differences around the world in terms of how people are approaching this pandemic. And so the data is very rich and varied. Let's talk about that dynamic. >> Yeah. If you, for the listeners who are or viewers who are new to this, right? The workflow could be a patient comes in, you take the blood, and you send it through an analysis? DNA is made up of genes and our genes express, right? They express in two steps the first they transcribe, then they translate. But what we are analyzing is the middle step, the transcription stage. And tens of thousands of these Transcripts that are produced after the analysis of the blood. The thing is, can we find in the tens of thousands of items, right? Or biomarkers a signature that tells us, this is COVID-19 and how serious it is for this patient, right? Now, the data is enormous, right? For every patient. And then you have a collection of patients in each hospitals that have a certain demographic. And then you have also a number of hospitals around. The point is how'd you get to share all that data in order to have good training of your machine? The ACO is of course a know privacy of data, right? And as such, how do you then share that information if privacy restricts you from sharing the data? So in this case, swarm learning only shares the learnings, not the private patient data. So we hope this approach would allow all the different hospitals to come together and unite sharing the learnings removing biases so that we have high accuracy in our prediction as well at the same time, maintaining privacy. >> It's really well explained. And I would like to add at least for the European union, that this is extremely important because the lawmakers have clearly stated, and the governments that even non of these crisis conditions, they will not minimize the rules of privacy laws, their compliance to privacy laws has to stay as high as outside of the pandemic. And I think there's good reasons for that, because if you lower the bond, now, why shouldn't you lower the bar in other times as well? And I think that was a wise decision, yes. If you would see in the medical field, how difficult it is to discuss, how do we share the data fast enough? I think swarm learning is really an amazing solution to that. Yeah, because this discussion is gone basically. Now we can discuss about how we do learning together. I'd rather than discussing what would be a lengthy procedure to go towards sharing. Which is very difficult under the current privacy laws. So, I think that's why I was so excited when I learned about it, the first place with faster, we can do things that otherwise are either not possible or would take forever. And for a crisis that's key. That's absolutely key. >> And is the byproduct. It's also the fact that all the data stay where they are at the different hospitals with no movement. >> Yeah. Yeah. >> Learn locally but only shared the learnings. >> Right. Very important in the EU of course, even in the United States, People are debating. What about contact tracing and using technology and cell phones, and smartphones to do that. Beside, I don't know what the situation is like in India, but nonetheless, that Dr. Goh's point about just sharing the learnings, bubbling it up, trickling just kind of metadata. If you will, back down, protects us. But at the same time, it allows us to iterate and improve the models. And so, that's a key part of this, the starting point and the conclusions that we draw from the models they're going to, and we've seen this with the pandemic, it changes daily, certainly weekly, but even daily. We continuously improve the conclusions and the models don't we. >> Absolutely, as Dr. Goh explained well. So, we could look at like they have the clinics or the testing centers, which are done in the remote places or wherever. So, we could collect those data at the time. And then if we could run it to the transcripting kind of a sequencing. And then as in, when we learn to these new samples and the new pieces all of them put kind of, how is that in the local data participate in the kind of use swarm learning, not just within the state or in a country could participate into an swarm learning globally to share all this data, which is coming up in a new way, and then also implement some kind of continuous learning to pick up the new signals or the new insight. It comes a bit new set of data and also help to immediately deploy it back into the inference or into the practice of identification. To do these, I think one of the key things which we have realized is to making it very simple. It's making it simple, to convert the machine learning models into the swarm learning, because we know that our subject matter experts who are going to develop these models on their choice of platforms and also making it simple to integrate into that complete machine learning workflow from the time of collecting a data pre processing and then doing the model training and then putting it onto inferencing and looking performance. So, we have kept that in the mind from the beginning while developing it. So, we kind of developed it as a plug able microservices kind of packed data with containers. So the whole library could be given it as a container with a kind of a decentralized management command controls, which would help to manage the whole swarm network and to start and initiate and children enrollment of new hospitals or the new nodes into the swarm network. At the same time, we also looked into the task of the data scientists and then try to make it very, very easy for them to take their existing models and convert that into the swarm learning frameworks so that they can convert or enabled they're models to participate in a decentralized learning. So, we have made it to a set callable rest APIs. And I could say that the example, which we are working with the Professor either in the case of leukemia or in the COVID kind of things. The noodle network model. So we're kind of using the 10 layer neural network things. We could convert that into the swarm model with less than 10 lines of code changes. So, that's kind of a simply three we are looking at so that it helps to make it quicker, faster and loaded the benefits. >> So, that's an exciting thing here Dr. Goh is, this is not an R and D project. This is something that you're actually, implementing in a real world, even though it's a narrow example, but there are so many other examples that I'd love to talk about, but please, you had a comment. >> Yes. The key thing here is that in addition to allowing privacy to be kept at each hospital, you also have the issue of different hospitals having day to day skewed differently. Right? For example, a demographics could be that this hospital is seeing a lot more younger patients, and other hospitals seeing a lot more older patients. Right? And then if you are doing machine learning in isolation then your machine might be better at recognizing the condition in the younger population, but not older and vice versa by using this approach of swarm learning, we then have the biases removed so that both hospitals can detect for younger and older population. All right. So, this is an important point, right? The ability to remove biases here. And you can see biases in the different hospitals because of the type of cases they see and the demographics. Now, the other point that's very important to reemphasize is what precise Professor Schultze mentioned, right? It's how we made it very easy to implement this.Right? This started out being so, for example, each hospital has their own neural network and they training their own. All you do is we come in, as Pasad mentioned, change a few lines of code in the original, machine learning model. And now you're part of the collective swarm. This is how we want to easy to implement so that we can get again, as I like to call, hospitals of the world to uniting. >> Yeah. >> Without sharing private patient data. So, let's double click on that Professor. So, tell us about sort of your team, how you're taking advantage of this Dr. Goh, just describe, sort of the simplicity, but what are the skills that you need to take advantage of this? What's your team look like? >> Yeah. So, we actually have a team that's comes from physicians to biologists, from medical experts up to computational scientists. So, we have early on invested in having these interdisciplinary research teams so that we can actually spend the whole spectrum. So, people know about the medicine they know about them the biological basics, but they also know how to implement such new technology. So, they are probably a little bit spearheading that, but this is the way to go in the future. And I see that with many institutions going this way many other groups are going into this direction because finally medicine understands that without computational sciences, without artificial intelligence and machine learning, we will not answer those questions with this large data that we're using. So, I'm here fine. But I also realize that when we entered this project, we had basically our model, we had our machine learning model from the leukemia's, and it really took almost no efforts to get this into the swarm. So, we were really ready to go in very short time, but I also would like to say, and then it goes towards the bias that is existing in medicine between different places. Dr. Goh said this very nicely. It's one aspect is the patient and so on, but also the techniques, how we do clinical essays, we're using different robots a bit. Using different automates to do the analysis. And we actually try to find out what the Swan learning is doing if we actually provide such a bias by prep itself. So, I did the following thing. We know that there's different ways of measuring these transcriptomes. And we actually simulated that two hospitals had an older technology and a third hospital had a much newer technology, which is good for understanding the biology and the diseases. But it is the new technology is prone for not being able anymore to generate data that can be used to learn and then predicting the old technology. So, there was basically, it's deteriorating, if you do take the new one and you'll make a classifier model and you try old data, it doesn't work anymore. So, that's a very hard challenge. We knew it didn't work anymore in the old way. So, we've pushed it into swarm learning and to swarm recognize that, and it didn't take care of it. It didn't care anymore because the results were even better by bringing everything together. I was astonished. I mean, it's absolutely amazing. That's although we knew about this limitations on that one hospital data, this form basically could deal with it. I think there's more to learn about these advantages. Yeah. And I'm very excited. It's not only a transcriptome that people do. I hope we can very soon do it with imaging or the DCNE has 10 sites in Germany connected to 10 university hospitals. There's a lot of imaging data, CT scans and MRIs, Rachel Grimes. And this is the next next domain in medicine that we would like to apply as well as running. Absolutely. >> Well, it's very exciting being able to bring this to the clinical world And make it in sort of an ongoing learnings. I mean, you think about, again, coming back to the pandemic, initially, we thought putting people on ventilators was the right thing to do. We learned, okay. Maybe, maybe not so much the efficacy of vaccines and other therapeutics. It's going to be really interesting to see how those play out. My understanding is that the vaccines coming out of China, or built to for speed, get to market fast, be interested in U.S. Maybe, try to build vaccines that are maybe more longterm effective. Let's see if that actually occurs some of those other biases and tests that we can do. That is a very exciting, continuous use case. Isn't it? >> Yeah, I think so. Go ahead. >> Yes. I, in fact, we have another project ongoing to use a transcriptome data and other data like metabolic and cytokines that data, all these biomarkers from the blood, right? Volunteers during a clinical trial. But the whole idea of looking at all those biomarkers, we talking tens of thousands of them, the same thing again, and then see if we can streamline it clinical trials by looking at it data and training with that data. So again, here you go. Right? We have very good that we have many vaccines on. In candidates out there right now, the next long pole in the tenth is the clinical trial. And we are working on that also by applying the same concept. Yeah. But for clinical trials. >> Right. And then Prasad, it seems to me that this is a good, an example of sort of an edge use case. Right? You've got a lot of distributed data. And I know you've spoken in the past about the edge generally, where data lives bringing moving data back to sort of the centralized model. But of course you don't want to move data if you don't have to real time AI inferencing at the edge. So, what are you thinking in terms of other other edge use cases that were there swarm learning can be applied. >> Yeah, that's a great point. We could kind of look at this both in the medical and also in the other fields, as we talked about Professor just mentioned about this radiographs and then probably, Using this with a medical image data, think of it as a scenario in the future. So, if we could have an edge note sitting next to these medical imaging systems, very close to that. And then as in when this the systems producers, the medical immediate speed could be an X-ray or a CT scan or MRI scan types of thing. The system next to that, sitting on the attached to that. From the modernity is already built with the swarm lending. It can do the inferencing. And also with the new setup data, if it looks some kind of an outlier sees the new or images are probably a new signals. It could use that new data to initiate another round up as form learning with all the involved or the other medical images across the globe. So, all this can happen without really sharing any of the raw data outside of the systems but just getting the inferencing and then trying to make all of these systems to come together and try to build a better model. >> So, the last question. Yeah. >> If I may, we got to wrap, but I mean, I first, I think we've heard about swarm learning, maybe read about it probably 30 years ago and then just ignored it and forgot about it. And now here we are today, blockchain of course, first heard about with Bitcoin and you're seeing all kinds of really interesting examples, but Dr. Goh, start with you. This is really an exciting area, and we're just getting started. Where do you see swarm learning, by let's say the end of the decade, what are the possibilities? >> Yeah. You could see this being applied in many other industries, right? So, we've spoken about life sciences, to the healthcare industry or you can't imagine the scenario of manufacturing where a decade from now you have intelligent robots that can learn from looking at across men building a product and then to replicate it, right? By just looking, listening, learning and imagine now you have multiple of these robots, all sharing their learnings across boundaries, right? Across state boundaries, across country boundaries provided you allow that without having to share what they are seeing. Right? They can share, what they have lunch learnt You see, that's the difference without having to need to share what they see and hear, they can share what they have learned across all the different robots around the world. Right? All in the community that you allow, you mentioned that time, right? That will even in manufacturing, you get intelligent robots learning from each other. >> Professor, I wonder if as a practitioner, if you could sort of lay out your vision for where you see something like this going in the future, >> I'll stay with the medical field at the moment being, although I agree, it will be in many other areas, medicine has two traditions for sure. One is learning from each other. So, that's an old tradition in medicine for thousands of years, but what's interesting and that's even more in the modern times, we have no traditional sharing data. It's just not really inherent to medicine. So, that's the mindset. So yes, learning from each other is fine, but sharing data is not so fine, but swarm learning deals with that, we can still learn from each other. We can, help each other by learning and this time by machine learning. We don't have to actually dealing with the data sharing anymore because that's that's us. So for me, it's a really perfect situation. Medicine could benefit dramatically from that because it goes along the traditions and that's very often very important to get adopted. And on top of that, what also is not seen very well in medicine is that there's a hierarchy in the sense of serious certain institutions rule others and swarm learning is exactly helping us there because it democratizes, onboarding everybody. And even if you're not sort of a small entity or a small institutional or small hospital, you could become remembering the swarm and you will become as a member important. And there is no no central institution that actually rules everything. But this democratization, I really laugh, I have to say, >> Pasad, we'll give you the final word. I mean, your job is very helping to apply these technologies to solve problems. what's your vision or for this. >> Yeah. I think Professor mentioned about one of the very key points to use saying that democratization of BI I'd like to just expand a little bit. So, it has a very profound application. So, Dr. Goh, mentioned about, the manufacturing. So, if you look at any field, it could be health science, manufacturing, autonomous vehicles and those to the democratization, and also using that a blockchain, we are kind of building a framework also to incentivize the people who own certain set of data and then bring the insight from the data into the table for doing and swarm learning. So, we could build some kind of alternative monetization framework or an incentivization framework on top of the existing fund learning stuff, which we are working on to enable the participants to bring their data or insight and then get rewarded accordingly kind of a thing. So, if you look at eventually, we could completely make dais a democratized AI, with having the complete monitorization incentivization system which is built into that. You may call the parties to seamlessly work together. >> So, I think this is just a fabulous example of we hear a lot in the media about, the tech backlash breaking up big tech but how tech has disrupted our lives. But this is a great example of tech for good and responsible tech for good. And if you think about this pandemic, if there's one thing that it's taught us is that disruptions outside of technology, pandemics or natural disasters or climate change, et cetera, are probably going to be the bigger disruptions then technology yet technology is going to help us solve those problems and address those disruptions. Gentlemen, I really appreciate you coming on theCUBE and sharing this great example and wish you best of luck in your endeavors. >> Thank you. >> Thank you. >> Thank you for having me. >> And thank you everybody for watching. This is theCUBE's coverage of HPE discover 2020, the virtual experience. We'll be right back right after this short break. (upbeat music)

(upbeat music) >> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi, I'm Stu Miniman, and welcome to this CUBE conversation. We're digging in with Pensando. Talking about the technologies that they're using. And happy to welcome to the program, two of Pensando's technical leaders. We have Krishna Doddapaneni, he's the Vice President of Software. And we have here Pirabhu Raman, he's a Principal Engineer, both with Pensando. Thank you so much for joining us. >> Thank you Stu. >> All right. >> Thank you for having us here >> Krishna, you run the Software Team. So let's start there and talk about really the mission and shortly obviously, bring us through a little bit of architecturally what Pensando was doing. >> To get started, Pensando we are building a platform, which can automate and manage the network storage and security services. So when we talk about software here, it's like the better software as you start from all the way from bootloader, to all the way it goes to microservices controller. So the fundamentally the company is building a domain specific processor called a DSP, that goes on the card called DSC. And that card goes into a server in a PCIe slot. Since we go into a server and we act as a NIC, we have to do drivers for Windows, all the OS' Windows, Linux, ESX and FreeBSD. And on the card itself, the chip itself, there are two fundamental pieces of the chip. One is the P4 pipelines, where we run all our applications, if you can think like in the firewalls, in the virtualization, all security applications. And then there's Arm SoC, which we have to bring up the platform and where we run the control plane and data and management plane so that's one piece of the software. The other big piece of software is called PSM. Which kind of, if you think about it in data center, you don't want to manage, one DSC at a time or one server at a time. We want to manage all thousands of servers, using a single management and control point. And that's where the test for the PSM comes from. >> Yeah, excellent. You talked about a pretty complex solution there. One of the big discussion points in the networking world and I think in general has been really the role of software. I think we all know, it got a little overblown. The discussion of software, does not mean that hardware goes away. I wrote a piece, many years ago, if you look at how hyperscalars do things, how they hyper optimize. They don't just buy the cheapest, most generic thing. they tend to configure things and they just roll it out in massive scale. So your team is well known for, really from a chip standpoint, I think about the three Cisco spin-ins. If you dug underneath the covers, yes there was software, but there was an Async there. So, when I look at what you're doing in Pensando, you've got software and there is a chip, at the end of the day. It looks, the first form factor of this looks like, a network card, the NIC that fits in there. So give us in there some of the some of the challenges of software and there's so much diversity in hardware these days. Everything getting ready for AI and GPUs. And you listed off a bunch of pieces when you were talking about the architecture. So give us that software/hardware dynamic, if you would. >> I mean, if you look at where the industry has been going towards, right, I mean, the Moore's law has been ending and Dennard scale is a big on Dennard scaling. So if you want to set all the network in certain security services on x86, you will be wasting a bunch of x86 cycles. The customer, why does he buy x86? He buys x86 to run his application. Not to run IO or do security for IO or policies for IO. So where we come in is basically, we do this domain specific processor, which will take away all the IO part of it, and the computer, just the compute of the application is left for x86. The rest is all offloaded to what we call Pensando. So NIC is kind of one part of what we do. NIC is how we connect to the server. But what we do inside the card is, firewalls, all the networking functions: SDNs, load balancing in all the storage functions, NVMe virtualization, and encryption of all the packets, data of data at rest and data of data in motion. All these services is what we do in this part. And you know, yes, it's an Async. But if you look at what we do inside, it's not a fixed Async. We did work on the previous spin-ins as you said, with Async, but there's a fundamental difference between that Async can this Async. In those Asyncs for example, there's a hard coded routing table or there's a hard coded ACL table. This Async is a completely programmable. It's more like it's a programmable software that we have domain specific language called P4. We use that P4 to program the Async. So the way I look at it, it's an Async, but it's mostly software driven completely. And from all the way from controllers, to what programs you run on the chip, is completely software driven. >> Excellent. Pirabhu of course, the big announcement here, HPE. You've now got the product. It's becoming generally available this month. We'd watch from the launch of Pensando, obviously, having HPE as not only an investor, but they're an OEM of the product. They've got a huge customer base. Maybe help explain, from the enterprise standpoint, if I'm buying ProLion, where now does, am I going to be thinking about Pensando? What specific use cases? How does this translate to the general and enterprise IP buyer? >> We cover of whole breadth of use cases, at the very basic level, if your use cases or if your company is not ready for all the different features, you could buy it as a basic NIC and start provisioning it, and you will get all the basic network functions. But at the same time in addition to the standard network functions, you will get always on telemetry. Like you will get rich set of metrics, you will get packet capture capabilities, which will help you very much in troubleshooting issues, when they happen, or you can leave them always on as well. So, you can do some of these tap kind of functionalities, which financial services do. And all these things you will get without any impact on the workload performance. Like the customers' application don't see any performance impact when any of these capabilities are turned on. So once this is as a standard network function, but beyond this when you are ready for enforcing policies at the edge or you're ready for enforcing stateful firewalls, distributed firewalling capabilities, connection tracking, some of the other things, like Krishna touched upon NVMe virtualization, there are all sorts of other features you can add on top of. >> Okay, so it sounds like what we're really democratizing some of those cloud services or cloud like services for the network, down to the end device, if I have this right. >> Exactly. >> Maybe if you could, networking, we know, our friends in network. We tend to get very acronym driven, to overlays and underlays and various layers of the stack there. When we talk about innovation, I'd love to hear from both of you, what are some of those kind of key innovations, if you were to highlight just one or two? Pirabhu, maybe you can go first and then Krishna would would love your follow up from that. >> Sure, there are many innovations, but just to highlight a few of them, right. Krishna touched upon P4, but even on the P4, P4 is very much focused on manipulating the packets, packets in and packets out, but we enhanced it so that we can address it in such a way that from memory in-packet out, packet in-memory out. Those kind of capabilities so that we can interface it with the host memory. So those innovations we are taking it to the standard and they are in the process of getting standardized as well. In addition to this, our software stack, we touched upon the always on telemetry capabilities. You could do flow based packet captures, NetFlow, you could get a lot of visibility and troubleshooting information. The management plane in itself, has some of the state of the art capabilities. Like it's distributed, highly available, and it makes it very easy for you to manage thousands of these servers. Krishna, do you want to add something more? >> Yes, the biggest thing of the platform is that when we did underlays and overlays, as you said there, everything was like fixed. So tomorrow, you wake up and come with a new protocol, or you may come up with a new way to do storage, right? Normally, in the hardware world, what happens is, Oh, you have to I have to sell you this new chip. That is not what we are doing. I mean, here, whatever we ship on this Async, you can continue to evolve and continue to innovate, irrespective of changing standards. If NVMe goes from one dot two to one dot three, or you come up with a new encapsulation of VXLAN, you do whatever encapsulations, whatever TLVs you would want to, you don't need to change the hardware. It's more about downloading new firmware, and upgrading the new firmware and you get the new feature. That is that's one of the key innovation. That's why most of the cloud providers like us, that we are not tied to hardware. It's more of software programmable processor that we can keep on adding features in the future. >> So one way to look at it, is like, you get the best of both worlds kind of a thing. You get power and performance of Async, but at the same time you get the flexibility of closer to that of a general purpose processor. >> Yeah, so Krishna, since you own the software piece of thing, help us understand architecturally, how you can deploy something today but be ready for whatever comes in the future. That's always been the challenge is, Gee, maybe if I wait another six months, there'll be another generation something, where I don't want to make sure that I miss some window of opportunity. >> Yeah, so it's a very good question. I mean, basically you can keep enhancing your features with the same performance and power and latency and throughput. But the other important thing is how you upgrade the software. I mean today whenever you have Async. When you have changed the Async, obviously, you have to pull the card out and you put the new card in. Here, when you're talking upgrading software, we can upgrade software while traffic is going through. With very minimal disruption, in the order of sub second. Right, so you can change your protocol, for example, tomorrow, we change from VXLAN to your own innovative protocol, you can upgrade that without disrupting any existing network or storage IO. I mean, that's where the power of the platform is very useful. And if you look at it today, where cloud providers are going right, and the cloud providers, you don't want to, because there are customers who are using that server, and they're deploying their application, they don't want to disturb that application, just because you decided to do some new innovative feature. The platform capability is that you could upgrade it, and you can change your mind sometime in the future. But whatever existing traffic is there, the traffic will continue to flow and not disrupt your app. >> All right, great. Well, you're talking about clouds one of the things we look at is multi cloud and multi vendor. Pirabhu, we've got the announcement with HPE now, ProLion and some of their other platforms. Tell us how much work will it be for you to support things like Dell servers or I think your team's quite familiar with the Cisco UCS platform. Two pieces on that number one: how easy or hard is it to do that integration? And from an architectural design? Does a customer need to be homogeneous from their environment or is whatever cloud or server platform they're on independent, and we should be able to work across those? >> Yeah, first off, I should start with thanking HPE. They have been a great partner and they have been quick to recognize the synergy and the potential of the synergy. And they have been very helpful towards this integration journey. And the way we see it, a lot of the work has already been done in terms of finding out the integration issues with HPE. And we will build upon this integration work that has been done so that we can quickly integrate with other manufacturers like Dell and Cisco. We definitely want to integrate with other server manufacturers as well, because that is in the interest of our customers, who want to consume Pensando in a heterogenous fashion, not just from one server manufacturer. >> Just want to add one thing to what Pirabhu's saying. Basically, the way we think about it is that, there's x86 and then the all the IO, the infrastructure services, right. So for us, as long as you get power from the server, and you can get packets and IO across the PCIe bus, we are kind of, we want to make it a uniform layer. So the Pensando, if you think about it, is a layer that can work across servers, and could work inside the public cloud and when we have, one of our customers using this in hybrid cloud. So we want to be the base where we can do all the storage network and security services, irrespective of the server and where the server is placed. Whether it's placed in the call log, it's placed in the enterprise data center, or it's placed in the public cloud. >> All right, so I guess Krishna, you said first x86. Down the road, is there opportunity to go beyond Intel processors? >> Yes. I mean, we already support AMD, which is another form of x86. But other architecture doesn't prevent us from any servers. As long as you follow the PCIe standard, we should, it's more of a testing matrix issue. It's not about support of any other OS, we should be able to support it. And initially, we also tested once on PowerPC. So any kind of CPU architecture, we should be able to support. >> Okay, so walk me up the application stack a little bit though. Things like virtualization, containerization. There's the question of does it work but does it optimize? Any of us live through those waves of, Oh, okay, well it kind of worked, but then there was a lot of time to make things like the origin networking work well in virtualization and then in containerization. So how about your solution? >> I mean you should look at, a good example is AWS, like what AWS does with Nitro. So on Nitro, you do EBS, you do security, and you do VPC. In all the services is effectively, we think about it, all of those can be encapsulated in one DSC card. And obviously, when it comes to this kind of implementation on one card, right, the first question you would ask what happens to the noisy neighbor? So we have the right QOS mechanisms to make sure all the services go through the same card, at the same time giving guarantees to the customer that (mumbles) especially in the multi-tenant environment, whatever you're doing on one VPC will not affect the other VPC. And the advantage of the platform that what we have is very highly scalable and highly performing. Scale will not be the issue. I mean, if you look at existing platforms, even if you look at the cloud, because when you're doing this product, obviously, we'll do benchmarking with the cloud and enterprises. With respect to scale, performance and latency, we did the measurements and we are order of magnitude compared to (sneezes) given the existing clouds and currently whatever enterprise customers have. >> Excellent, so Pirabhu, I'm curious, from the enterprise standpoint, are there certain applications, I think about like, from an analytic standpoint, Splunk is so heavily involved in data that might be a natural fit or other things where it might not be fully tested out with anything kind of that ISV world that we need to think about. >> So if we're talking in terms of partner ecosystems, our enterprise customers do use many of the other products as well. And we are trying to integrate with other products so that we can get the maximum value. So if you look at it, you could get rich metrics and visualization capabilities from our product, which can be very helpful for the partner products because they don't have to install an agent and they can get the same capability across bare metal virtual stack as well as containers. So we are integrating with various partners including some CMDB configuration management database products, as well as data analytics or network traffic analytics products. Krishna, do you want to add anything? >> Yeah, so I think it's just not the the analytics products. We're also integrating with VMware. Because right now VMware is a computer orchestrated and we want to be the network policy orchestrator. In the future, we want to integrate with Kubernetes and OpenShift. So we want to add integration so that our platform capability can be easily consumable irrespective of what kind of workload you use or what kind of traffic analytics tool you use or what kind of data link that you use in your enterprise data center. >> Excellent, I think that's a good view forward as to where some of the work is going on the future integration. Krishna and Pirabhu, thank you so much for joining us. Great to catch up. >> Thank you Stu. >> Thanks for having us. >> All right. I'm Stu Miniman. Thank you for watching theCUBE. (gentle music)

>>From the cube studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a cute conversation. Hi, welcome back. I'm Stu middleman. And this is a cube conversation digging in with, talking about what they're doing to help people. Yeah. Really bringing some of the networking ideals to cloud native environment, both know in the cloud, in the data centers program, Krishna penny. He is the vice president of software. Thanks so much for joining us. Thank you so much for talking to me. Alright, so, so Krishna the pin Sandow team, uh, you know, very well known in the industry three, uh, you innovation. Yeah. Especially in the networking world. Give us a little bit about your background specifically, uh, how long you've been part of this team and, uh, you know, but, uh, you know, you and the team, you know? Yeah. >>And Sando. Yup. Um, so, uh, I'm VP of software in Sandow, um, before Penn Sarno, before founding concern, though, I worked in a few startups in CME networks, uh, newer systems and Greenfield networks, all those three startups have been acquired by Cisco. Um, um, my recent role before this, uh, uh, this, this company was a, it was VP of engineering and Cisco, uh, I was responsible for a product called ACA, which is course flagship SDN tonic. Mmm. So I mean, when, why did we find a phone, uh, Ben Sandoz? So when we were looking at the industry, uh, the last, uh, a few years, right? The few trends that are becoming clear. So obviously we have a lot of enterprise background. We were watching, you know, ECA being deployed in the enterprise data centers. One sore point for customers from operational point of view was installing service devices, network appliances, or storage appliances. >>So not only the operational complexity that this device is bringing, it's also, they don't give you the performance and bandwidth, uh, and PPS that you expect, but traffic, especially from East West. So that was one that was one major issue. And also, if you look at where the intelligence is going, has been, this has been the trend it's been going to the edge. The reason for that is the motors or switches or the devices in the middle. They cannot handle the scale. Yeah. I mean, the bandwidths are growing. The scale is growing. The stateful stuff is going in the network and the switches and the appliances not able to handle it. So you need something at the edge close to the application that can handle, uh, uh, this kind of, uh, services and bandwidth. And the third thing is obviously, you know, x86, okay. Even a few years back, you know, every two years, you know, you're getting more transistors. >>I mean, obviously the most lined it. And, uh, we know we know how that, that part is going. So the it's cycles are more valuable and we don't want to use them for this network services Mmm. Including SDN or firewalls or load balancer. So NBME, mutualization so looking at all these trends in the industry, you know, we thought there is a good, uh, good opportunity to do a domain specific processor for IO and build products around it. I mean, that's how we started Ben signed off. Yeah. So, so Krishna, it's always fascinating to watch. If you look at startups, they are often yeah. Okay. The time that they're in and the technologies that are available, you know, sometimes their ideas that, you know, cakes a few times and, you know, maturation of the technology and other times, you know, I'll hear teams and they're like, Oh, well we did this. >>And then, Oh, wow. There was this new innovation came out that I wish I had add that when I did this last time. So we do, a generation. Oh, wow. Talking about, you know, distributed architectures or, you know, well, over a decade spent a long time now, uh, in many ways I feel edge computing is just, you know, the latest discussion of this, but when it comes to, and you know, you've got software, uh, under, under your purview, um, what are some of the things that are available for that might not have been, you know, in your toolkit, you know, five years ago. Yeah. So the growth of open source software has been very helpful for us because we baked scale-out microservices. This controller, like the last time I don't, when we were building that, you know, we had to build our own consensus algorithm. >>We had to build our own dishwasher database for metrics and humans and logs. So right now, uh, we, I mean, we have, because of open source thing, we leverage CD elastic influx in all this open source technologies that you hear, uh, uh, since we want to leverage the Kubernetes ecosystem. No, that helped us a lot at the same time, if you think about it. Right. But even the software, which is not open source, close source thing, I'm maturing. Um, I mean, if you talk about SDN, you know, seven APS bank, it was like, you know, the end versions of doing off SDN, but now the industry standard is an ADPN, um, which is one of the core pieces of what we do we do as Dean solution with DVA. Um, so, you know, it's more of, you know, the industry's coming to a place where, you know, these are the standards and this is open source software that you could leverage and quickly innovate compared to building all of this from scratch, which will be a big effort for us stocked up, uh, to succeed and build it in time for your customer success. >>Yeah. And Krishna, I, you know, you talk about open forum, not only in the software, the hardware standards. Okay. Think about things, the open compute or the proliferation of, you know, GPS and, uh, everything along that, how was that impact? I did. So, I mean, it's a good thing you're talking about. For example, we were, we are looking in the future and OCP card, but I do know it's a good thing that SEP card goes into a HP server. It goes into a Dell software. Um, so pretty much, you know, we, we want to, I mean, see our goal is to enable this platform, uh, that what we built in, you know, all the use cases that customer could think of. Right. So in that way, hardware, standardization is a good thing for the industry. Um, and then same thing, if you go in how we program the AC, you know, we at about standards of this people, programming, it's an industry consortium led by a few people. >>Um, we want to make sure that, you know, we follow the standards for the customer who's coming in, uh, who wants to program it., it's good to have a standards based thing rather than doing something completely proprietary at the same time you're enabling innovations. And then those innovations here to push it back to the open source. That's what we trying to do with before. Yeah. Excellent. I've had some, some real good conversations about before. Um, and, and the way, uh, and Tondo is, is leveraging that, that may be a little bit differently. You know, you talk about standards and open source, oftentimes it's like, well, is there a differentiator there, there are certain parts of the ecosystem that you say, well, kind of been commodified. Mmm. Obviously you're taking a lot of different technologies, putting them together, uh, help, help share the uniqueness. Okay. And Tondo what differentiates, what you're doing from what was available in the market or that I couldn't just cobbled together, uh, you know, a bunch of open source hardware and software together. >>Yeah. I mean, if you look at a technologist, I think the networking that both of us are very familiar with that. If you want to build an SDN solution, or you can take a, well yes. Or you can use exhibit six and, you know, take some much in Silicon and cobble it together. But the problem is you will not get the performance and bandwidth that you're looking for. Okay. So let's say, you know, uh, if you want a high PPS solution or you want a high CPS solution, because the number of connections are going for your IOT use case or Fiji use case, right. If you, uh, to get that with an open source thing, without any assist, uh, from a domain specific processor, your performance will be low. So that is the, I mean, that's once an enterprise in the cloud use case state, as you know, you're trying to pack as many BMCs containers in one set of word, because, you know, you get charged. >>I mean, the customer, uh, the other customers make money based on that. Right? So you want to offload all of those things into a domain specific processor that what we've built, which we call the TSC, which will, um, which we'll, you know, do all the services at pretty much no cost to accept a six. I mean, it's to six, you'll be using zero cycles, a photo doing, you know, features like security groups or VPCs, or VPN, uh, or encryption or storage virtualization. Right. That's where that value comes in. I mean, if you count the TCO model using bunch of x86 codes or in a bunch of arm or AMD codes compared to what we do. Mmm. A TCO model works out great for our customers. I mean, that's why, you know, there's so much interest in a product. Excellent. I'm proud of you. Glad you brought up customers, Christina. >>One of the challenges I have seen over the years with networking is it tends to be, you know, a completely separate language that we speak there, you know, a lot of acronyms and protocols and, uh, you know, not necessarily passable to people outside of the silo of networking. I think back then, you know, SDN, uh, you know, people on the outside would be like, that stands for still does nothing, right? Like networking, uh, you know, mumbo jumbo there for people outside of networking. You know what I think about, you know, if I was going to the C suite of an enterprise customer, um, they don't necessarily care about those networking protocols. They care about the, you know, the business results and the product Liberty. How, how do you help explain what pen Sandow does to those that aren't, you know, steeped in the network, because the way I look at it, right? >>What is customer looking? But yeah, you're writing who doesn't need, what in cap you use customer is looking for is operational simplicity. And then he wants looking for security. They, it, you know, and if you look at it sometimes, you know, both like in orthogonal, if you make it very highly secure, but you make it like and does an operational procedure before you deploy a workload that doesn't work for the customer because in operational complexity increases tremendously. Right? So it, we are coming in, um, is that we want to simplify this for the customer. You know, this is a very simple way to deploy policies. There's a simple way to deploy your networking infrastructure. And in the way we do it is we don't care what your physical network is, uh, in some sense, right? So because we are close to the server, that's a very good advantage. >>We have, we have played the policies before, even the packet leaves the center, right? So in that way, he knows his fully secure environment and we, and you don't want to manage each one individually, we have this, okay, Rockwell PSM, which manages, you know, all this service from a central place. And it's easy to operationalize a fabric, whether you talk about upgrades or you talk about, you know, uh, deploying new services, it's all driven with rest API, and you can have a GUI, so you can do it a single place. And that's where, you know, a customer's value is rather than talking about, as you're talking about end caps or, you know, exactly the route to port. That is not the main thing that, I mean, they wake up every day, they wake up. Have you been thinking about it or do I have a security risk? >>And then how easy for me is to deploy new, uh, in a new services or bring up new data center. Right. Okay. Krishna, you're also spanning with your product, a few different worlds out. Yeah. You know, traditionally yeah. About, you know, an enterprise data center versus a hyperscale public cloud and ed sites, hi comes to mind very different skillset for management, you know, different types of okay. Appointments there. Mmm. You know, I understand right. You were going to, you know, play in all of those environments. So talk a little bit about that, please. How you do that and, you know, you know, where you sit in, in that overall discussion. Yes. So, I mean, a number one rule inside a company is we are driven by customers and obviously not customer success is our success. So, but given said that, right. What we try to do is that we try to build a platform that is kind of, you know, programmable obviously starting from, you know, before that we talked about earlier, but it's also from a software point of view, it's kind of plugable right. >>So when we build a software, for example, at cloud customers, and they use BSC, they use the same set of age KPI's or GSP CRS, TPS that DSC provides their controller. But when we ship the same, uh, platform, what enterprise customers, we built our own controller and we use the same DC APS. So the way we are trying to do is things is fully leverage yeah. In what we do for enterprise customers and cloud customers. Mmm. We don't try to reinvent the wheel. Uh, obviously at the same time, if you look at the highest level constructs from a network perspective, right. Uh, audience, for his perspective, what are you trying to do? You're trying to provide connectivity, but you're trying to avoid isolation and you're trying to provide security. Uh, so all these constructs we encapsulated in APA is a, which, you know, uh, in some, I, some, some mostly like cloud, like APS and those APIs are, are used, but cloud customers and enterprise customers, and the software is built in a way of it. >>Any layer is, can be removed on any layer. It can be hard, right? Because it's not interested. We don't want to be multiple different offers for different customers. Right. Then we will not scale. So the idea when we started the software architecture, is that how we make it pluggable and how will you make the program will that customer says, I don't want this piece of it. You can put them third party piece on it and still integrate, uh, at a, at a common layer with using. Yeah. Yeah. Well, you know, Krishna, you know, I have a little bit of appreciation where some of the hard work, what your team has been doing, you know, a couple of years in stealth, but, you know, really accelerating from, uh, you know, the announcement coming out of stealth, uh, at the end of 2019. Yeah. Just about half a year, your GA with a major OEM of HPE, definitely a lot of work that needs to be done. >>It brings us to, you know, what, what are you most proud about from the work that your team's doing? Uh, you know, we don't need to hear any, you know, major horror stories, but, you know, there always are some of them, you know, not holes or challenges that, uh, you know, often get hidden yeah. Behind the curtain. Okay. I mean, personally, I'm most proud of the team that we've made. Um, so, uh, you know, obviously, you know, uh, our executors have it good track record of disrupting the market multiple times, but I'm most proud of the team because the team is not just worried about that., uh, that, uh, even delegate is senior technologist and they're great leaders, but they're also worried about the customer problem, right? So it's always about, you know, getting the right mix, awfully not execution combined with technology is when you succeed, that is what I'm most proud of. >>You know, we have a team with, and Cletus running all these projects independently, um, and then releasing almost we have at least every week, if you look at all our customers, right. And then, you know, being a small company doing that is a, Hmm, it's pretty challenging in a way. But we did, we came up with methodologists where we fully believe in automation, everything is automated. And whenever we release software, we run through the full set of automation. So then we are confident that customer is getting good quality code. Uh, it's not like, you know, we cooked up something and that they should be ready and they need to upgrade to the software. That's I think that's the key part. If you want to succeed in this day and age, uh, developing the features at the velocity that you would want to develop and still support all these customers at the same time. >>Okay. Well, congratulations on that, Christian. All right. Final question. I have for you give us a little bit of guidance going forward, you know, often when we see a company out and we, you know, to try to say, Oh, well, this is what company does. You've got a very flexible architecture, lot of different types of solutions, what kind of markets or services might we be looking at a firm, uh, you know, download down the road a little ways. So I think we have a long journey. So we have a platform right now. We already, uh, I mean, we have a very baby, we are shipping. Mmm Mmm. The platforms are really shipping in a storage provider. Uh, we are integrating with the premier clouds, public clouds and, you know, enterprise market, you know, we already deployed a distributed firewall. Some of the customers divert is weird firewall. >>So, you know, uh, so if you take this platform, it can be extendable to add in all the services that you see in data centers on clubs, right. But primarily we are driven from a customer perspective and customer priority point of view. Mmm. So BMW will go is even try to add more ed services. We'll try to add more storage features. Mmm. And then we, we are also this initial interest in service provider market. What we can do for Fiji and IOT, uh, because we have the flexible platform. We have the, see, you know, how to apply this platform, this new application, that's where it probably will go into church. All right. Well, Krishna not a penny vice president of software with Ben Tondo. Thank you so much for joining us. Thank you, sir. It was great talking to you. All right. Be sure to check out the cube.net. You can find lots of interviews from Penn Sundo I'm Stu Miniman and thank you. We're watching the cute.

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody watching the Cuban leader live tech coverage. We here covering the M I t CDO conference M I t CEO Day to wrapping up. Bob Parr is here. He's a partner in principle at KPMG, and he's joined by Streetcar Krishna, who is the managing director of data science. Aye, aye. And innovation at KPMG. Gents, welcome to the Cube. Thank >> thank you. Let's start with your >> roles. So, Bob, where do you focus >> my focus? Ah, within KPMG, we've got three main business lines audit tax, an advisory. And so I'm the advisory chief date officer. So I'm more focused on how we use data competitively in the market. More the offense side of our focus. So, you know, how do we make sure that our teams have the data they need to deliver value? Uh, much as possible working concert with the enterprise? CDO uh, who's more focused on our infrastructure, Our standards, security, privacy and those >> you've focused on making KPMG better A >> supposed exactly clients. OK, >> I also have a second hat, and I also serve financial service is si Dios as well. So Okay, so >> get her out of a dual role. I got sales guys in >> streetcar. What was your role? >> Yeah, You know, I focus a lot on data science, artificial intelligence and overall innovation s o my reaction. I actually represent a centre of >> excellence within KPMG that focuses on the I machine learning natural language processing. And I work with Bob's Division to actually advance the data site off the store because all the eye needs data. And without data, there's no algorithms, So we're focusing a lot on How do we use a I to make data Better think about their equality. Think about data lineage. Think about all of the problems that data has. How can we make it better using algorithms? And I focused a lot on that working with Bob, But no, it's it's customers and internal. I mean, you know, I were a horizontal within the form, So we help customers. We help internal, we focus a lot on the market. >> So, Bob, you mentioned used data offensively. So 10 12 years ago, it was data was a liability. You had to get rid of it. Keep it no longer than you had to, because you're gonna get soon. So email archives came in and obviously thinks flipped after the big data. But so what do you What are you seeing in terms of that shift from From the defense data to the offensive? >> Yeah, and it's it's really you know, when you think about it and let me define sort of offense versus defense. Who on the defense side, historically, that's where most of CEOs have played. That's risk regulatory reporting, privacy, um, even litigation support those types of activities today. Uh, and really, until about a year and 1/2 ago, we really saw most CEOs still really anchored in that I run a forum with a number of studios and financial service is, and every year we get them together and asked him the same set of questions. This was the first year where they said that you know what my primary focus now is. Growth. It's bringing efficiency is trying to generate value on the offensive side. It's not like the regulatory work's going away, certainly in the face of some of the pending privacy regulation. But you know, it's It's a sign that the volume of use cases as the investments in their digital transformations are starting to kick out, as well as the volumes of data that are available. The raw material that's available to them in terms of third party data in terms of the the just the general volumes that that exist that are streaming into the organization and the overall literacy in the business units are creating this, this massive demand. And so they're having to >> respond because of getting a handle on the data they're actually finding. Word is, they're categorizing it there, there, >> yeah, organizing that. That is still still a challenge. Um, I think it's better with when you have a very narrow scope of critical data elements going back to the structure data that we're talking it with the regulatory reporting when you start to get into the three offense, the generating value, getting the customer experience, you know, really exploring. You know that side of it. There's there's a ton of new muscle that has to be built new muscle in terms of data quality, new muscle in terms of um, really more scalable operating model. I think that's a big issue right now with Si Dios is, you know, we've got ah, we're used to that limited swath of CDs and they've got Stewardship Network. That's very labor intensive. A lot of manual processes still, um, and and they have some good basic technology, but it's a lot of its rules based. And when you do you think about those how that constraints going to scale when you have all of this demand. You know, when you look at the customer experience analytics that they want to do when you look at, you know, just a I applied to things like operations. The demand on the focus there is is is gonna start to create a fundamental shift >> this week are one of things that I >> have scene, and maybe it's just my small observation space. But I wonder, if you could comment Is that seems like many CBO's air not directly involved in the aye aye initiatives. Clearly, the chief digital officer is involved, but the CDO zehr kind of, you know, in the background still, you see that? >> That's a fantastic question, and I think this is where we're seeing some off the cutting it change that is happening in the industry. And when Barbara presenter idea that we can often civilly look at data, this is what it is that studios for a long time have become more reactive in their roles. And that is that is starting to come forefront now. So a lot of institutions were working with are asking What's the next generation Roll off a CDO and why are they in the background and why are they not in the foreground? And this is when you become more often they were proactive with data and the digital officers are obviously focused on, you know, the transformation that has to happen. But the studios are their backbone in order to make the transformation. Really. And if the CDO started, think about their data as an asset did as a product did us a service. The judicial officers are right there because those are the real, you know, like the data data they're living so CDO can really become from my back office to really become a business line. We've >> seen taking the reins in machine learning in machine learning projects and cos you work with. Who >> was driving that? Yeah. Great question. So we are seeing, like, you know, different. I would put them in buckets, right? There is no one mortal fits all. We're seeing different generations within the company's. Some off. The ones were just testing out the market. There's two keeping it in their technology space in their back office. Take idea and, you know, in in forward I d let me call them where they are starting to experiment with this. But you see, the mature organizations on the other end of the spectrum, they are integrating action, learning and a I right into the business line because they want to see ex souls having the technology right by their side so they can lead leverage. Aye, aye. And machine learning spot right for the business right there. And that is where we're seeing know some of the new models. Come on. >> I think the big shift from a CDO perspective is using a i to prep data for a That's that's fundamentally where you know, where the data science was distributed. Some of that data science has to come back and free the integration for equality for data prepping because you've got all this data third party and other from customer streaming into the organization. And you know, the work that you're doing around, um, anomaly detection is it transcends developing the rules, doing the profiling, doing the rules. You know, the very manual, the very labor intensive process you've got to get away from that >> is used in order for this to be scale goes and a I to figure out which out goes to apply t >> clean to prepare the data toe, see what algorithms we can use. So it's basically what we're calling a eye for data rather than just data leading into a I. So it's I mean, you know, you developed a technology for one off our clients and pretty large financial service. They were getting closer, like 1,000,000,000 data points every day. And there was no way manually, you could go through the same quality controls and all of those processes. So we automated it through algorithms, and these algorithms are learning the behavior of data as they flow into the organization, and they're able to proactively tell their problems are starting very much. And this is the new face that we see in in the industry, you cannot scale the traditional data governance using manual processes, we have to go to the next generation where a i natural language processing and think about on structure data, right? I mean, that is, like 90% off. The organization is unstructured data, and we have not talked about data quality. We have not talked about data governance. For a lot of these sources of information, now is the time. Hey, I can do it. >> And I think that raised a great question. If you look at unstructured and a lot of the data sources, as you start to take more of an offensive stance will be unstructured. And the data quality, what it means to apply data quality isn't the the profiling and the rules generation the way you would with standard data. So the teams, the skills that CEOs have in their organizations, have to change. You have to start to, and, you know, it's a great example where, you know, you guys were ingesting documents and there was handwriting all over the documents, you know, and >> yeah, you know, you're a great example, Bob. Like you no way would ask the client, like, you know, is this document gonna scanned into the system so my algorithm can run and they're like, Yeah, everything is good. I mean, the deal is there, but when you then start scanning it, you realize there's handwriting and the information is in the handwriting. So all the algorithms breakdown now >> tribal knowledge striving Exactly. >> Exactly. So that's what we're seeing. You know, if I if we talk about the digital transformation in data in the city organization, it is this idea dart. Nothing is left unseen. Some algorithm or some technology, has seen everything that is coming into. The organization has has has a para 500. So you can tell you where the problems are. And this is what algorithms do. This scale beautifully. >> So the data quality approaches are evolving, sort of changing. So rather than heavy, heavy emphasis on masking or duplication and things like that, you would traditionally think of participating the difficult not that that goes away. But it's got to evolve to use machine >> intelligence. Exactly what kind of >> skill sets people need thio achieve that Is it Is it the same people or do we need to retrain them or bring in new skills. >> Yeah, great question. And I can talk from the inspector off. Where is disrupting every industry now that we know, right? But we knew when you look at what skills are >> required, all of the eye, including natural language processing, machine learning, still require human in the loop. And >> that is the training that goes in there. And who do you who are the >> people who have that knowledge? It is the business analyst. It's the data analyst who are the knowledge betters the C suite and the studios. They are able to make decisions. But the day today is still with the data analyst. >> Those s Emmys. Those sm >> means So we have to obscure them to really start >> interacting with these new technologies where they are the leaders, rather than just waiting for answers to come through. And >> when that happens now being as a data scientist, my job is easy because they're Siamese, are there? I deploy the technology. They're semi's trained algorithms on a regular basis. Then it is a fully fungible model which is evolving with the business. And no longer am I spending time re architect ing my rules. And like my, you know, what are the masking capabilities I need to have? It is evolving us. >> Does that change the >> number one problem that you hear from data scientists, which is the 80% of the time >> spent on wrangling cleaning data 10 15 20% run into sm. He's being concerned that they're gonna be replaced by the machine. Their training. >> I actually see them being really enabled now where they're spending 80% of the time doing boring job off, looking at data. Now they're spending 90% of their time looking at the elements future creative in which requires human intelligence to say, Hey, this is different because off X, >> y and Z so let's let's go out. It sounds like a lot of what machine learning is being used for now in your domain is clean things up its plumbing. It's basic foundation work. So go out. Three years after all that work has been done and the data is clean. Where are your clients talking about going next with machine learning? Bob, did you want? >> I mean, it's a whole. It varies by by industry, obviously, but, um but it covers the gamut from, you know, and it's generally tied to what's driving their strategies. So if you look at a financial service is organization as an example today, you're gonna have, you know, really a I driving a lot of the behind the scenes on the customer experience. It's, you know, today with your credit card company. It's behind the scenes doing fraud detection. You know, that's that's going to continue. So it's take the critical functions that were more data. It makes better models that, you know, that that's just going to explode. And I think they're really you can look across all the functions, from finance to to marketing to operations. I mean, it's it's gonna be pervasive across, you know all of that. >> So if I may, I don't top award. While Bob was saying, I think what's gonna what What our clients are asking is, how can I exhilarate the decision making? Because at the end of the day on Lee, all our leaders are focused on making decisions, and all of this data science is leading up to their decision, and today you see like you know what you brought up, like 80% of the time is wasted in cleaning the data. So only 20% time was spent in riel experimentation and analytics. So your decision making time was reduced to 20% off the effort that I put in the pipeline. What if now I can make it 80% of the time? They're I put in the pipeline, better decisions are gonna come on the train. So when I go into a meeting and I'm saying like, Hey, can you show me what happened in this particular region or in this particular part of the country? Previously, it would have been like, Oh, can you come back in two weeks? I will have the data ready, and I will tell you the answer. But in two weeks, the business has ran away and the CDO know or the C Street doesn't require the same answer. But where we're headed as as the data quality improves, you can get to really time questions and decisions. >> So decision, sport, business, intelligence. Well, we're getting better. Isn't interesting to me. Six months to build a cube, we'd still still not good enough. Moving too fast. As the saying goes, data is plentiful. Insights aren't Yes, you know, in your view, well, machine intelligence. Finally, close that gap. Get us closer to real time decision >> making. It will eventually. But there's there's so much that we need to. Our industry needs to understand first, and it really ingrained. And, you know, today there is still a fundamental trust issues with a I you know, it's we've done a lot of work >> watch Black box or a part of >> it. Part of it. I think you know, the research we've done. And some of this is nine countries, 2400 senior executives. And we asked some, ah, a lot of questions around their data and trusted analytics, and 92% of them came back with. They have some fundamental trust issues with their data and their analytics and and they feel like there's reputational risk material reputational risk. This isn't getting one little number wrong on one of the >> reports about some more of an >> issue, you know, we also do a CEO study, and we've done this many years in a row going back to 2017. We started asked them okay, making a lot of companies their data driven right. When it comes to >> what they say they're doing well, They say they're day driven. That's the >> point. At the end of the day, they making strategic decisions where you have an insight that's not intuitive. Do you trust your gut? Go with the analytics back then. You know, 67% said they go with their gut, So okay, this is 2017. This industry's moving quickly. There's tons and tons of investment. Look at it. 2018 go down. No, went up 78%. So it's not aware this issue there is something We're fundamentally wrong and you hit it on. It's a part of its black box, and part of it's the date equality and part of its bias. And there's there's all of these things flowing around it. And so when we dug into that, we said, Well, okay, if that exists, how are we going to help organizations get their arms around this issue and start digging into that that trust issue and really it's the front part is, is exactly what we're talking about in terms of data quality, both structured more traditional approaches and unstructured, using the handwriting example in those types of techniques. But then you get into the models themselves, and it's, you know, the critical thing she had to worry about is, you know, lineage. So from an integrity perspective, where's the data coming from? Whether the sources for the change controls on some of that, they need to look at explain ability, gain at the black box part where you can you tell me the inferences decisions are those documented. And this is important for this me, the human in the loop to get confidence in the algorithm as well as you know, that executive group. So they understand there's a structure set of processes around >> Moneyball. Problem is actually pretty confined. It's pretty straightforward. Dono 32 teams are throwing minor leagues, but the data models pretty consistent through the problem with organizations is I didn't know data model is consistent with the organization you mentioned, Risk Bob. The >> other problem is organizational inertia. If they don't trust it, what is it? What is a P and l manage to do when he or she wants to preserve? Yeah, you know, their exit position. They attacked the data. You know, I don't believe that well, which which is >> a fundamental point, which is culture. Yes. I mean, you can you can have all the data, science and all the governance that you want. But if you don't work culture in parallel with all this, it's it's not gonna stick. And and that's, I think the lot of the leading organisations, they're starting to really dig into this. We hear a lot of it literacy. We hear a lot about, you know, top down support. What does that really mean? It means, you know, senior executives are placing bats around and linking demonstrably linking the data and the role of data days an asset into their strategies and then messaging it out and being specific around the types of investments that are going to reinforce that business strategy. So that's absolutely critical. And then literacy absolutely fundamental is well, because it's not just the executives and the data scientists that have to get this. It's the guy in ops that you're trying to get you. They need to understand, you know, not only tools, but it's less about the tools. But it's the techniques, so it's not. The approach is being used, are more transparent and and that you know they're starting to also understand, you know, the issues of privacy and data usage rights. That's that's also something that we can't leave it the curb. With all this >> innovation, it's also believing that there's an imperative. I mean, there's a lot of for all the talk about digital transformation hear it everywhere. Everybody's trying to get digital, right? But there's still a lot of complacency in the organization in the lines of business in operation to save. We're actually doing really well. You know, we're in financial service is health care really hasn't been disrupted. This is Oh, it's coming, it's coming. But there's still a lot of I'll be retired by then or hanging. Actually, it's >> also it's also the fact that, you know, like in the previous generation, like, you know, if I had to go to a shopping, I would go into a shop and if I wanted by an insurance product, I would call my insurance agent. But today the New world, it's just a top off my screen. I have to go from Amazon, so some other some other app, and this is really this is what is happening to all of our kind. Previously that they start their customers, pocketed them in different experience. Buckets. It's not anymore that's real in front of them. So if you don't get into their digital transformation, a customer is not going to discount you by saying, Oh, you're not Amazon. So I'm not going to expect that you're still on my phone and you're only two types of here, so you have to become really digital >> little surprises that you said you see the next. The next stage is being decision support rather than customer experience, because we hear that for CEOs, customer experience is top of mind right now. >> No natural profile. There are two differences, right? One is external facing is absolutely the customer internal facing. It's absolutely the decision making, because that's how they're separating. The internal were, says the external, and you know most of the meetings that we goto Customer insight is the first place where analytics is starting where data is being cleaned up. Their questions are being asked about. Can I master my customer records? Can I do a good master off my vendor list? That is where they start. But all of that leads to good decision making to support the customers. So it's like that external towards internal view well, back >> to the offense versus defense and the shift. I mean, it absolutely is on the offense side. So it is with the customer, and that's a more directly to the business strategy. So it's get That's the area that's getting the money, the support and people feel like it's they're making an impact with it there. When it's it's down here in some admin area, it's below the water line, and, you know, even though it's important and it flows up here, it doesn't get the VIN visibility. So >> that's great conversation. You coming on? You got to leave it there. Thank you for watching right back with our next guest, Dave Lot. Paul Gillen from M I t CDO I Q Right back. You're watching the Cube

>> Announcer: Live from Boston, Massachusetts. It's theCUBE, covering Red Hat Summit 2019. Brought to you by Red Hat. >> And welcome back to Boston. Here on theCUBE we continue our coverage of Red Hat Summit 2019. We just had Jim Whitehurst on, President and CEO, along with Stu Miniman, I'm John Walls. And now, we turn to the IBM side of the equation. Arvind Krishna is with us, the SVP of Cloud and Cognitive Software at IBM. Arvind, good to see you this morning. >> My pleasure to be here, what a great show. >> Yeah, absolutely, it has been. I was telling Jim he couldn't have a better week, right? Monday had good news, Tuesday great kick off, today again following through great key notes. We were talking briefly, a year ago you were with us on theCUBE and talking about IBM and its forward plans, so on and so forth. What a difference a year makes, right? (laughs) >> We couldn't predict that you'd be in the position that you are in now, so just if you can summarize the last year and maybe the last six months for you. >> Sure, and I think it's more building on what I talked to you about a year ago, I remember last May, May of 2018, in San Francisco. So I was exposing very heavily, look the world's going to move towards containers, the world has already embraced Linux, this is the time to have a new architecture that enables hybrid, much along the lines that Jim and all of the clients as well as Ginni and Satya were talking about on stage yesterday. So you put all that together and you say that is what we mentioned last year and we were clear, that is where the world is gonna go. Now you step forward a few months from there into October of 2018 and on the 29th October we announced that IBM intends to acquire Red Hat, so then you say wow, we put actually our money where our mouth was. We were talking about the strategy, we were talking about Linux containers, OpenShift, the partnership we announced last May was IBM software products together with OpenShift. We already believed in that. But now this allows us coming together, it's more like a marriage than sort of loose partners passing each other in the middle of the night. >> Right. >> And that then goes forward, you mention the news on Monday so for our viewers that don't know it, that's the news that the United States Department of Justice approved merger with no conditions. So now we've got to wait on a few other jurisdictions and then hopefully we can get together really soon. >> John: Right, right. >> So, I think back to looking at IBM over my career. I think the first time I heard the word coopetition it was related to IBM because IBM, big ego system, lots of innovation over its long history but as we know the bigger you get, the more chance that your partners are also going to overlap with you. Seeing Ginni up on stage and a little bit later seeing Satya up on stage is really interesting. You look at the public, multicloud environment, everybody doesn't need to work together, you talk to your customers, and I'm sure you find today it's not the future is hybrid and multicloud, that's where they are today even if they're trying to get their arms around all of it. So I'd love to hear your, with the mega trend of Cloud, what you're seeing that competitive but partnering dynamic. >> Look, I want to step back to just give it a little bit of context. So when you talk about companies, let's go back to the beginning of computing, of PC. The PC came from IBM operating system, DOS came from Microsoft. Then you had Windows setting up the IBM PC. So that's coopetition or is that pure partnership? Right, I mean you can take your pick of those words. Our value has always been that we, IBM, come to clients and we try to service problems that actually help them in their business outcomes. Then whoever they have inside their IT shops, that they depend upon, has to be a part of that answer. You cannot say oh, so and so is bad, they're out. So it always had to be coopetition from the lengths that we came to with our clients. We always build originally computers, other people's software are on those computers, other people provided services around it. As we went into certain software space, ISVs and so on came together. So now that you come to the world of Cloud, we hold a very fundamental belief and I think we heard a number of the clients talk about this. They are going to be on multiple public Clouds. If they are going to be on multiple public Clouds, they are also going to have traditional IT and they are also going to have private Clouds. That's the world to live in if I look at it from the viewpoint of that infrastructure. To now come to your direct question, so if that's the world they're going to live in hopefully one of those public Clouds is ours but the others are from other people. The private Cloud, we believe the standard for that should be OpenShift and should be containers. So as we go down that path, then you say if you want to take that environment and also run it on the other publics. That's good for the client, that's good for the publics, that's good for us. It's really a win, win, win. And so I think the ability to go do this and to make that play out, it really goes back to my thesis from more than a year ago where we talk about this is a new set of standards and a new set of technical protocols emerging. >> I want you to take us inside the conversations you're having with CIOs when you talk about Cloud because when Cloud first came out, it was well, the sins of IT is this heterogeneous mess and it's complex and expensive. Cloud's going to be simple, homogeneous and cheap. I look at Cloud of 2019 and I don't think I would use any of those adjectives to define what most people have for Cloud. Where are they today? Where do we need to go as an industry? >> Glass house computing, all centralized, all homogeneous, not all at heterogeneous. Oops, 15 flavors of Unix, all different, none of them really talk to each other. Oops let's go to desktop computing, we begin with a pure architecture, maybe Novell which doesn't exist, maybe it does, I don't even know. Oops, back to this complete sprawl of client server. Okay let's go to Cloud back to centralized glass house. >> You're making me dizzy. >> Oops, let's go to-- (laughing) >> Let's go to lots of public, lots of SaaS, lots of private, back to this thing. So, in each of these a different answer came on how to unite them. I think when we look at that Unix and client server sprawl, I think TCP/IP and the internet came together so that you could have all these islands talk to each other and be able to communicate. All right, great, we've got 20 years of victory on that. Now you're getting these things, how do you begin to workload across because that becomes the next level of values. Not enough to communicate. Can I really take a workload? A workload is not just a VM or just one container, it's a collection of these things integrated together in a pretty tight and complex way. And can we take it from one place and move it to the other? Because that goes to the write once, run anywhere mantra which by the way also we come to about every 20 years. I think that's the magic of this moment and if we succeed in making that happen, which I have complete conviction we will, especially together, then I think we give a huge value back and we give freedom to every CTO and every CIO. >> You paint this really interesting whoops picture, I love that, it's really a back and forth, right, we're swinging and almost there's a cyclical nature to this is what you're I think implying. What's to say in your mind that this isn't just another whoops as opposed to this being a permanent shift in the paradigm? >> I think it's, the reason I think that it's going to be cyclical is we tend to, you know whether you go to construction and real estate, you talk about capacity and factories. You see an opportunity and people tend to go one way. The only way to correct culture if you're sitting in one place is to sort of over-correct the other way, now you're over-corrected. Now you have to come back. And always when you over-correct one way, then suddenly all those other benefits you've lost, so then you've got to come back to get those benefits. After about 10 years, probably, you can debate 10 or 15, you're done. You've exploited all those benefits, now you need to go get those benefits. Because the technologies have changed, it's not just that you're going back to what was. We're going very conceptually from centralized to distributed, to centralized to distributed. And by the way, another one that's getting out from pure centralized is also Edge. Edge in effect is another distributed, so you put those together and you say I went there, but then I lost all this stuff, now I need to get back to that stuff. If you've got too much there, you'll say, no, no, no, I need to get some of this back. So it's going to go that way I think for every, if you look at it, the big arcs are back, the pendulum, what do you call it, the pendulum swing, is I think about 20 years it looks like, right? 1960, centralized, 1980, PC, 2000, you could say was the peak of the internet. Hey, 2020, we're in Cloud. So looks like about 20 years, looks like. >> All right, so, I like what you were saying when you talk about that multicloud environment, the application is really central there. IBM, of course, has a strong history, not just in middleware but in applications. What do you think will differentiate this kind of next wave of multicloud, how will the leaders emerge? >> Right, so if you look at it today, you run infrastructure. I think OpenShift has done a great job of how you help run their infrastructure. The value in our eyes in putting the services on top, both coming from open source as well as other companies that are running like an integrated package. This is all about taking the cost out of how do you deploy and develop. And if we can take the cost out of that, you're not talking about that five to 10 X as we heard a couple of the clients up on stage yesterday with Jim talk about. If we give that to everybody, you can sort of say that 70% which goes into managing your current and only 30% on innovation. Can you shift that paradigm completely? That's the big business outcome that you get. As you begin to deliver these towers of function on top of the base. You need to start at base, without one base, you don't know how to say, I can't deal with these towers of function on thirty different things underneath. That engineering answer is a terrible one. >> In terms of the infrastructure market, things keep changing, right? Consolidating, EMC doing what they're, you know what happened there. How do you see your play in that market? First off, how do you see infrastructure evolving? And then how do you see your play in that going forward? >> Infrastructure has always been big, in the end all the stuff you talk about has to run on infrastructure. I'd say the consumption model of how you get infrastructure is changing. So it used to be that many years ago, people bought all their own infrastructures. They bought boxes, they put in boxes, they did all the integration. And what came from the vendor was just a box. Then you went to, all right you can get it as a managed service or you can get it in Cloud which is also a pay by the drink but you can now turn it up and down also. So it's not a either or, people want all of these models. And so our role in infrastructure, certain things we will provide. When it comes to running really high mission critical workloads, think mainframe, think big Unix, think storage, of that ilk; we'll keep providing that. We believe there's a lot of value in that. We see the value, our clients appreciate that value. That workload turns up, but it's the mission critical part of the workload. Then in turn we also provide the more commodity infrastructure but as a service. We supply a large amount of it to our clients. It comes sometimes wrapped in a managed service, it sometimes comes wrapped as a Cloud. And we will also consume infrastructure from other Cloud providers because if people are providing base computer, network and storage, there is no reason to presume that our capabilities wouldn't run on top. If I go back to just February, we announced that Watson will now run. We said we used the moniker Watson Anywhere to make the assertion that we will run Watson anywhere that we can run the correct containerized infrastructure. >> So, Arvind, what's the single most pressing issue that you hear from organizations with respect to their technology strategy and how's IBM helping there? >> I think modernizing applications is the biggest one. So people have, typically a large enterprise will have anywhere from 3,000 to 15,000 applications. That's what runs the enterprise. We talk about everyone's becoming a software company, right, I mean that was one of the quotes and everybody is becoming a tech company that was I think what one of the clients said, hey, we think you're a bank, you're actually a tech company. What that says is that you're capturing the essence of all the business processes. You're capturing the essence of the experiences. The essence of what regulators need, the essence of how you maintain customer and customer of our clients, trust, back to them. It's maintained through this collection of applications. Now if you say I want to go change, I want to become even more client centric, I want to insert AI into the middle of my business process, I want to become more digital. All of that is modernizing applications. The big pinpoint they all have is how do I modernize them? What becomes that fabric in which I modernize? How do I know I'm not locked into yet another spaghetti mess if I go down this path? Because we've seen that movie also. So they're interested in, hey, I want to be clean at the end of this. I want freedom to be able to move it. And that is why I'm so passionate about, the fabric is based on open source, the fabric's got to be based on open standards. If you go there, there is no lock-in, and it's not a spaghetti mess, it is actually clean. Much cleaner than any other option that we can dream of is going to be. And so if we go down this path, now you can open yourself up to a much faster velocity of how you deliver innovation and value back to the business. >> Okay, so, I'd agree first of all when you talk about modernization, the applications that they have, that's the long pole in the tent. We understand compared to all the other digitization, modernization, this is the toughest challenge here. I'm a little surprised though that I didn't hear the word data because they don't necessarily articulate it but the biggest opportunity that they have has to be tied to data. >> Well to me, when I use the word application here, and you heard me use the word AI, can I insert AI in the context of an application? Now, why is it not being done today? To get the value out of AI, the data that powers the AI is stuck in all the silos, all over the place. So you've got to have, as you do this modernization, it's imperative to put the correct data architecture so that now you can do the governance, so that you can choose to unlock the appropriate parts of the data. It's really important to say the appropriate parts because neither do you want data sort of free floating around the globe, because that is the value of a company at the end of the day. And so that unlocking of that value is a huge part of this. So you're absolutely right to ask me to express it more strongly when I use the word application, I'm inclusive of not just runtime but always of the data that powers that application. >> Arvind, it was again a year ago that we were talking to you out in San Francisco and you made some rather strong thematic predictions that turned out well. I'm not going to put you on the spot here, but I can't wait to see next year. And see how this turns out. >> I can't let him go before, we had the CIO of Delta who we had on our program. >> Oh, right, right. >> In the key note, made a question about licensing, of course Jim Whitehurst said we don't have licensing but what's your answer? >> I'm willing to offer a deal to Samant. So I think that both IBM and Red Hat do a fair amount of air travel. We'll give him a common license if he can just include Red Hat for whatever IBM pays, just include all the Red Hat travel that is needed on Delta. (laughing) You know just so that the business models become clear and we can go have a robust discussion. >> Out of Raleigh that's a good deal. >> For us. >> That's what I'm saying. That is a good deal. All right, the ball is in your court, or on your runway. Whatever the case may be. Arvind, thanks for being with us. >> My pleasure. >> We appreciate it. And we'll let you know if we hear back from Rahul on that good deal. TheCUBE continues live from Boston right after this. (upbeat music)

>> Live from San Francisco. It's the cue covering IBM thing twenty nineteen brought to you by IBM. >> Clever and welcome to the live coverage here. The Cube in San Francisco for IBM. Think twenty nineteen day Volonte where he with Urban Krishna, senior vice president of cloud and cognitive software at IBM. Man in charge of all the cloud products cloud everywhere. Aye, aye. Anywhere are great to see you. Thanks for spending time. Know you're super busy. Thanks for spending time. >> I'm ready to be here right >> now. So we talked at the Red Hat Summit last year. You essentially laid out the vision for micro Services. Coup Burnett is how this always kind of coming together than the redhead acquisition. And now you're seeing big news here at IBM. Think setting the stage here in San Francisco for a I anywhere, which is cognitive kind of all over the clouds, and then really clarity around cloud multi cloud strategy end to end workloads all kind of tied together on premise in the clouds. Super important for IBM. Explain and unpacked that force. What does it mean, >> Right? So I'm going to begin unpacking it from where actually I left off last year. So if I just for ten seconds, last year, we talked a lot about containerized platforms are going to become the future that'll be the fabric on which every enterprise is going to build their IT and their future. OK, we talked about that last year, and I think with the announced acquisition of Red Hat that gets cemented and that'll go further once that closes. Now you take that and now you take it to the next level of value. So take Watson. Watson runs as a containerized set of services. If it's a containerized set of services, it could run on what we call Cloud Private. Cloud Private in turn runs on top of OpenShift. So then you say, wherever OpenShift runs, I can run this entire stack. Where does OpenShift run today? It runs on Amazon. It runs on the IBM cloud and runs on Azure. It runs on your premise. So on the simple simple. I always like things that are simple. So Watson runs on Cloud Private runs and OpenShift runs on all these infrastructures I just mentioned that gives you Watson anywhere. You want it close to your data run it on-prem. You want to run it on Azure, run it there. You want to run it on the IBM cloud you run it there. And hence that's the complete story. >> says it was more important for you to give customers choice >> than it was to keep Watson to yourself. To try to sell >> more cloud. >> I think that every company that survives a long term learns that choice to a customer is really important and forcing customers to do things only one way is jelly in the long term. A bad strategy. So >> from a customer statement, just get the facts right on the hard news. Watson. Anywhere. Now I can run Watson via containers. Asian Open ship Things you mentioned on a ws as sheer Microsoft azure and IBM cloud cloud private. All that >> on on premise >> and on premise, all cohesively enter end. >> Correct in an identical way. Which means even if you do things one place you build up more than one place, you could go deploy a moral in another place gives you that flexibility also. >> So I'm Akash Mercy over This sounds too crazy Is too hard to do that. I've tried all this multi cloud stuff. Got all this stuff. Why is it easier? How do how do you guys make this happen? What's the key secret sauce for pulling that end to end a I anywhere on multiple clouds, on premises and through the workloads. >> Two levels. One. We go to a container infrastructure as that common layer that isolates out what is the bottom infrastructure from everything that runs on top. So going to the common services on a Cuban Eddie's in a container layer that is common across all these environments, does the isolation off the bottom infrastructure? That's hard engineering, but we do that engineering. The second piece is you've taken the Watson set of capabilities and also put them into just three pieces. What's in studio? What's an ML from water machine learning and what's an open scale? And there you have the complete set that you go need to run everywhere. So we have done that engineering as well. >> Congratulations. Get the cloud anywhere. I mean, it's cloud. It's essentially everything's every anywhere. Now you got data everywhere you got cloud everywhere. Cloud operations. Where's the multi cloud and hybrid fit in? Because now, if I could do a I anywhere via container ization, shouldn't I built? Run any workload on premise and in multiple clouds. >> So we fundamentally believe that when I was here last time, we talked about the container fabrics. And I do believe that we need to get to the point where these can run anywhere. So you take the container fabric and you can go run that anywhere, right? So so that's one piece of it, the next part of is but I now need to integrate. So I now need to bring in all my pieces. How I integrate this application with another? It's the old problem of integration back again. So whether you want to use MQ or you want to use Kafka or you want to use one of these technologies? How do we get them to couple one work flow to another work flow? How do I get them to be secure? How do I get them to be resilient in the presence of crashes in the presence of latency and all that? So that's another big piece of announcements that we're making. You can take that complete set off integration technologies, and those can run anywhere on any cloud. Again, using the same partner describes. I'm not going to go into that again. And on premise. So you can knit all of those together. >> How can you talk about the rationale for the Red Hat acquisition? Specifically in the context of developers, IBM over the years has made you know many efforts took to court developers. Now, with the redhead acquisition, it's eight million developers and talk about specifically the importance of developers and how that's changed >> your strategy or enhance your >> strategy. I'm an enhancement. It's not really a change. I think we all acknowledge developers have always been important and will remain important. I mean, IBM has done a great job, I think, over the last twenty years and both helping create the whole developer ecosystem, for example, around Job. We were a very big piece of that, not the only participant in there. There were others, but we were a big piece of that. So you not take red hat on Lenox and Open shit and Open source and J. Boss and all of these technologies. There's a big ecosystem of developers. You mentioned eight million number. But why did that set of people come along? They come along because they get a lot of value from developing on top of something that in turn has so many other people on top. I think there's half a million pieces of software which use redhead as the primary infrastructure on which they develop. So it's the network effect really. Is that value andan Africa can only come from you, keep it open, You keep it running on the widest possible base, and then they get the value that if they develop on that digger access to that and US base on which Red Hat Franz >> are, we have >> evidence that >> totally makes sense. But I want to get one dig deeper that we cover a lot of developer, the business side of developers. Not so much, no ins and outs, so developer tools and stuff. There's a lot of stack overflow. Variety of sources do that, So developers want to things they want to be in the right wave. You laying out a great platform for that, then this monetization Amazon has seen massive growth on their partner network. You guys haven't ecosystem. You mentioned that. How does this anywhere philosophy impact ecosystem because they want to party with IBM? Where's the white spaces? What's the opportunity for partners? How should they evolve with IBM? What's your What's your direction on that? >> Okay, so two kinds of partners one there's a set of partners will bring a huge set of value to their clients because they actually provide the domain knowledge. The application specify acknowledged the management expertise, the operational expertise, printable technologies, perhaps that we provide. That's what a partner's is always gonna have. Value talked yesterday at a portable conference about what, cognizant? Who's a bigger part. They do. They built a self service application for patients off a medical provider to be able to get remote access to doctors when they couldn't get enough. And that was not life threatening immediately. Well, that's a huge sort of valley that they provide built on top of our technologies and products. A second kind of partner you went on developers is people who do open those packages. I think we've been quite good. We don't tend to cannibalize our partners, unlike some others we can talk about. So for those partners who have that value, we can put our investment in other places. But we could help maybe give access to the enterprise market for those developers, which I think opens up. A lot of you >> guys make the martyr for developers. That's right. I want to ask you a question. You guys are all sleep in all in on Cooper Netease. Red hat made a great bed on Cooper Netease on. Now that you're harvesting that with the requisition, huge growth there containers. Everyone saw containers. That was kind of a no brainer. Technical world developers are. What's the importance of uber Netease? As you see Kou Bernetti starting to shrink the abstraction software overlay. In the end, this new complexity where Cooper needs a running great value. What does that mean? This trend mean for CEOs CTO CSOs as enterprise start to think, you know, cohesive set of services across on Prem multiple clouds. Cooper Nettie seems to be a key point. What is the impact of it? What does it mean? >> I think I'll go to the business. Benefit Secure binaries. In the end is an orchestration. Later takes over management complexity. It takes away the cost of doing operations in a large cluster ofthe physical resource is, I think the value for the CIA level is the following today, on average, seventy percent of the total cost and people are tied up in maintaining what you have. Thirty percent is on new. That's rough rule of Tom Technologies like communities have taken to where we wanted to go and flipped out to thirty seventy. We need to spend only thirty percent maintaining what you have. And he could then go spend seventy percent on doing innovation, which is going to make inclined, happier and your business happier. Your team's had a couple of announcements today. One was hyper protect, and the other is a lot of services to facilitate. Hybrid. Can you talk about those brats up to date on a quick one, so hyper protect means. So where do you put your data in the cloud everybody gets worried about? Well, if it's in the clear, it could get stolen. C Togo to encryption. Typically, encryption is then down with the key. Well, who manages that cake? The hyper protect services are all about that key. Management is comin across. Both are getting hybrid world across both your premise and in the cloud. And nobody in the cloud, not even our deepest system administrator in the cloud, can get access to the key. That's pretty remarkable when you think about it, and so that provide the level of safety and encryption that should give you a lot of reassurance that nobody can get hold of that data that's hyper protect. And then if I go to all of the other services were doing, sometimes I see a lot of help. Someone advice. Look, in the three client meeting I just had every one of them was asking what should keep regarded watching I slightly more nice. What should I write knew? That means a whole lot of advice that you need and how to assess what you have in what should be a correct strategy. Then once you do that, somebody will say will help me move it. Others will say, Help me manage it So all the services to go do that is a big piece of what we're announcing it end and to end in addition to but into end. But also you can cover it up. Not only give me advice, I know I got buying strategy laid out, helping move it on Oprah's do boards for me or help you manage it after I move it except >> armor. When you sit in customer meetings. Big clients write me, and when they say we want to modernize, what does that mean to you? And how do you respond to that? >> Well, some organizes. Normally today it means that you've got to bring cloud technologies. You gotta bring air technologies. You got to bring what is called digital transformation all to bear. It's got to be in the service of either client intimacy, or it's got to be in terms ofthe doing straight through processing, as opposed to the old way of doing all the business processes that you have and then you get into always got to begin with some easy wind. So I always say, Begin with the easy stuff, not begin with the harder stuff. What started the architecture that let you do the hardest off later? It's not throw away, and those are all the discussions that we have, which are always a mixture of this people process technology. That world has not changed. We need to worry about. All >> three are thanks for spending your valuable time coming on the Q. Bree. We appreciate the insight. I know you're super busy. Final question. Take take a minute. To explain this year. Think What's the core theme? What's the most important story people should pay attention to this year and IBM think in San Francisco? >> I think this two things and the borders. That is the evolution that is giving greater business value for using the word that is Chapter two off the cloud journey. And it's Chapter two off a cognitive enterprise. Chapter two means that you're not getting into solving really mission critical workloads, and that's what is happening there. And that's enabled through the mixture of what we're calling hybrid on multi cloud strategies and then the cognitive enterprises all around. How can you bring air to power every workflow? It's not a little shiny Tonda. Besides, it's in the very heart off every confirmation. >> The word of the day. Here's anywhere cloud anywhere, data anywhere. Aye, aye, anywhere that's a cube were everywhere and anywhere we could go to get the signal from the noise. Arvin Krista, senior vice president, cloud and cognitive software's new title man Architect in the Red Hat Acquisition in the cloud Multi cloud DNA. Congratulations on your success. Looking forward to following your journey. Thanks for coming on, thanks Thanks. Safe. Okay. More live coverage after this short break state with the cube dot net is where you find the videos were in San Francisco. Live here in Mosconi, North and south, bringing the IBM think twenty nineteen. Stay with us.