>> 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.

>>C G technology is focused on data where we have long believed that data is in our DNA. We help maximize humanity's potential by delivering world class, precision engineered data solutions developed through sustainable and profitable partnerships. Included in our offerings are hard disk drives. As I'm sure many of you know, ah, hard drive consists of a slider also known as a drive head or transducer attached to a head gimbal assembly. I had stack assembly made up of multiple head gimbal assemblies and a drive enclosure with one or more platters, or just that the head stacked assembles into. And while the concept hasn't changed, hard drive technology has progressed well beyond the initial five megabytes, 500 quarter inch drives that Seagate first produced. And, I think 1983. We have just announced in 18 terabytes 3.5 inch drive with nine flatters on a single head stack assembly with dual head stack assemblies this calendar year, the complexity of these drives further than need to incorporate Edge analytics at operation sites, so G Edward stemming established the concept of continual improvement and everything that we do, especially in product development and operations and at the end of World War Two, he embarked on a mission with support from the US government to help Japan recover from its four time losses. He established the concept of continual improvement and statistical process control to the leaders of prominent organizations within Japan. And because of this, he was honored by the Japanese emperor with the second order of the sacred treasure for his teachings, the only non Japanese to receive this honor in hundreds of years. Japan's quality control is now world famous, as many of you may know, and based on my own experience and product development, it is clear that they made a major impact on Japan's recovery after the war at Sea Gate. The work that we've been doing and adopting new technologies has been our mantra at continual improvement. As part of this effort, we embarked on the adoption of new technologies in our global operations, which includes establishing machine learning and artificial intelligence at the edge and in doing so, continue to adopt our technical capabilities within data science and data engineering. >>So I'm a principal engineer and member of the Operations and Technology Advanced Analytics Group. We are a service organization for those organizations who need to make sense of the data that they have and in doing so, perhaps introduce a different way to create an analyzed new data. Making sense of the data that organizations have is a key aspect of the work that data scientist and engineers do. So I'm a project manager for an initiative adopting artificial intelligence methodologies for C Gate manufacturing, which is the reason why I'm talking to you today. I thought I'd start by first talking about what we do at Sea Gate and follow that with a brief on artificial intelligence and its role in manufacturing. And I'd like them to discuss how AI and machine Learning is being used at Sea Gate in developing Edge analytics, where Dr Enterprise and Cooper Netease automates deployment, scaling and management of container raised applications. So finally, I like to discuss where we are headed with this initiative and where Mirant is has a major role in case some of you are not conversant in machine learning, artificial intelligence and difference outside some definitions. To cite one source, machine learning is the scientific study of algorithms and statistical bottles without computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference Instead, thus, being seen as a subset of narrow artificial intelligence were analytics and decision making take place. The intent of machine learning is to use basic algorithms to perform different functions, such as classify images to type classified emails into spam and not spam, and predict weather. The idea and this is where the concept of narrow artificial intelligence comes in, is to make decisions of a preset type basically let a machine learn from itself. These types of machine learning includes supervised learning, unsupervised learning and reinforcement learning and in supervised learning. The system learns from previous examples that are provided, such as images of dogs that are labeled by type in unsupervised learning. The algorithms are left to themselves to find answers. For example, a Siris of images of dogs can be used to group them into categories by association that's color, length of coat, length of snout and so on. So in the last slide, I mentioned narrow a I a few times, and to explain it is common to describe in terms of two categories general and narrow or weak. So Many of us were first exposed to General Ai in popular science fiction movies like 2000 and One, A Space Odyssey and Terminator General Ai is a I that can successfully perform any intellectual task that a human can. And if you ask you Lawn Musk or Stephen Hawking, this is how they view the future with General Ai. If we're not careful on how it is implemented, so most of us hope that is more like this is friendly and helpful. Um, like Wally. The reality is that machines today are not only capable of weak or narrow, a I AI that is focused on a narrow, specific task like understanding, speech or finding objects and images. Alexa and Google Home are becoming very popular, and they can be found in many homes. Their narrow task is to recognize human speech and answer limited questions or perform simple tasks like raising the temperature in your home or ordering a pizza as long as you have already defined the order. Narrow. AI is also very useful for recognizing objects in images and even counting people as they go in and out of stores. As you can see in this example, so artificial intelligence supplies, machine learning analytics inference and other techniques which can be used to solve actual problems. The two examples here particle detection, an image anomaly detection have the potential to adopt edge analytics during the manufacturing process. Ah, common problem in clean rooms is spikes in particle count from particle detectors. With this application, we can provide context to particle events by monitoring the area around the machine and detecting when foreign objects like gloves enter areas where they should not. Image Anomaly detection historically has been accomplished at sea gate by operators in clean rooms, viewing each image one at a time for anomalies, creating models of various anomalies through machine learning. Methodologies can be used to run comparative analyses in a production environment where outliers can be detected through influence in an automated real Time analytics scenario. So anomaly detection is also frequently used in machine learning to find patterns or unusual events in our data. How do you know what you don't know? It's really what you ask, and the first step in anomaly detection is to use an algorithm to find patterns or relationships in your data. In this case, we're looking at hundreds of variables and finding relationships between them. We can then look at a subset of variables and determine how they are behaving in relation to each other. We use this baseline to define normal behavior and generate a model of it. In this case, we're building a model with three variables. We can then run this model against new data. Observations that do not fit in the model are defined as anomalies, and anomalies can be good or bad. It takes a subject matter expert to determine how to classify the anomalies on classify classification could be scrapped or okay to use. For example, the subject matter expert is assisting the machine to learn the rules. We then update the model with the classifications anomalies and start running again, and we can see that there are few that generate these models. Now. Secret factories generate hundreds of thousands of images every day. Many of these require human toe, look at them and make a decision. This is dull and steak prone work that is ideal for artificial intelligence. The initiative that I am project managing is intended to offer a solution that matches the continual increased complexity of the products we manufacture and that minimizes the need for manual inspection. The Edge Rx Smart manufacturing reference architecture er, is the initiative both how meat and I are working on and sorry to say that Hamid isn't here today. But as I said, you may have guessed. Our goal is to introduce early defect detection in every stage of our manufacturing process through a machine learning and real time analytics through inference. And in doing so, we will improve overall product quality, enjoy higher yields with lesser defects and produce higher Ma Jin's. Because this was entirely new. We established partnerships with H B within video and with Docker and Amaranthus two years ago to develop the capability that we now have as we deploy edge Rx to our operation sites in four continents from a hardware. Since H P. E. And in video has been an able partner in helping us develop an architecture that we have standardized on and on the software stack side doctor has been instrumental in helping us manage a very complex project with a steep learning curve for all concerned. To further clarify efforts to enable more a i N M l in factories. Theobald active was to determine an economical edge Compute that would access the latest AI NML technology using a standardized platform across all factories. This objective included providing an upgrade path that scales while minimizing disruption to existing factory systems and burden on factory information systems. Resource is the two parts to the compute solution are shown in the diagram, and the gateway device connects to see gates, existing factory information systems, architecture ER and does inference calculations. The second part is a training device for creating and updating models. All factories will need the Gateway device and the Compute Cluster on site, and to this day it remains to be seen if the training devices needed in other locations. But we do know that one devices capable of supporting multiple factories simultaneously there are also options for training on cloud based Resource is the stream storing appliance consists of a kubernetes cluster with GPU and CPU worker notes, as well as master notes and docker trusted registries. The GPU nodes are hardware based using H B E l 4000 edge lines, the balance our virtual machines and for machine learning. We've standardized on both the H B E. Apollo 6500 and the NVIDIA G X one, each with eight in video V 100 GP use. And, incidentally, the same technology enables augmented and virtual reality. Hardware is only one part of the equation. Our software stack consists of Docker Enterprise and Cooper Netease. As I mentioned previously, we've deployed these clusters at all of our operations sites with specific use. Case is planned for each site. Moran Tous has had a major impact on our ability to develop this capability by offering a stable platform in universal control plane that provides us, with the necessary metrics to determine the health of the Kubernetes cluster and the use of Dr Trusted Registry to maintain a secure repository for containers. And they have been an exceptional partner in our efforts to deploy clusters at multiple sites. At this point in our deployment efforts, we are on prem, but we are exploring cloud service options that include Miranda's next generation Docker enterprise offering that includes stack light in conjunction with multi cluster management. And to me, the concept of federation of multi cluster management is a requirement in our case because of the global nature of our business where our operation sites are on four continents. So Stack Light provides the hook of each cluster that banks multi cluster management and effective solution. Open source has been a major part of Project Athena, and there has been a debate about using Dr CE versus Dr Enterprise. And that decision was actually easy, given the advantages that Dr Enterprise would offer, especially during a nearly phase of development. Cooper Netease was a natural addition to the software stack and has been widely accepted. But we have also been a work to adopt such open source as rabbit and to messaging tensorflow and tensor rt, to name three good lab for developments and a number of others. As you see here, is well, and most of our programming programming has been in python. The results of our efforts so far have been excellent. We are seeing a six month return on investment from just one of seven clusters where the hardware and software cost approached close to $1 million. The performance on this cluster is now over three million images processed per day for their adoption has been growing, but the biggest challenge we've seen has been handling a steep learning curve. Installing and maintaining complex Cooper needs clusters in data centers that are not used to managing the unique aspect of clusters like this. And because of this, we have been considering adopting a control plane in the cloud with Kubernetes as the service supported by Miranda's. Even without considering, Kubernetes is a service. The concept of federation or multi cluster management has to be on her road map, especially considering the global nature of our company. Thank you.