>> Voiceover: Live from New York it's theCUBE, covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. (upbeat techno music) >> Welcome back to New York City as theCUBE continues our coverage here at IBM's Machine Learning Everywhere: Build Your Ladder to AI. Along with Dave Vellante, I'm John Walls. We're now joined by Vitaly Tsivan who is Executive Vice President at AMC Networks. And Vitaly, thanks for joining us here this morning. >> Thank you. >> I don't know how this interview is going to go, frankly. Because we've got a die-hard Yankee fan in our guest, and a Red Sox fans who bleeds Red Sox Nation. Can you guys get along for about 15 minutes? >> Dave: Maybe about 15. >> I'm glad there's a bit of space between us. >> Dave: It's given us the off-season and the Yankees have done so well. I'll be humble. Okay? (John laughs) We'll wait and see. >> All right. Just in case, I'm ready to jump in if we have to separate here. But it is good to have you here with us this morning. Thanks for making the time. First off, talk about AMC Networks a little bit. So, five U.S. networks. You said multiple international networks and great presence there. But you've had to make this transition to becoming a data company, in essence. You have content and you're making this merger in the data. How has that gone for you? And how have you done that? >> First of all, you make me happy when you say that AMC Networks have made a transition to be a data company. So, we haven't. We are using data to help our primary business, which is obviously broadcasting our content to our viewers. But yes, we use data to help to tune our business, to follow the lead that viewers are giving us. As you can imagine, in the last so many years, viewers have actually dictating how they want to watch. Whether it's streaming video rather than just turning their satellite boxes or TV boxes on, and pretty much dictating what content they want to watch. So, we have to follow, we have to adjust and be at the cutting edge all for our business. And this is where data come into play. >> How did you get there? You must have done a lot of testing, right? I mean, I remember when binge watching didn't even exist, and then all of a sudden now everybody drops 10 episodes at once. Was that a lot of A-B testing? Just analyzing data? How does a company like yours come to that realization? Or is it just, wow, the competition is doing it, we should too. Explain how -- >> Vitaly: Interesting. So, when I speak to executives, I always tell them that business intelligence and data analytics for any company is almost like an iceberg. So, you can actually see the top of it, and you enjoy it very much but there's so much underwater. So, that's what you're referring to which is that in order to be able to deliver that premium thing that's the tip of the iceberg is that we have to have state of the art data management platforms. We have to curate our own first by data. We have to acquire meaningful third party data. We have to mingle it all together. We have to employ optimization predictive algorithms on top of that. We have to employ statistics, and arm business with data-driven decisions. And then it all comes to fruition. >> Now, your company's been around for awhile. You've got an application -- You're a developer. You're an application development executive. So, you've sort of made your personal journey. I'm curious as to how the company made its journey. How did you close that gap between the data platforms that we all know, the Googles, the Facebooks, etc., which data is the central part of their organization, to where you used to be? Which probably was building, looking back doing a lot of business intelligence, decision support, and a lot of sort of asynchronous activities. How did you get from there to where you are today? >> Makes sense. So, I've been with AMC Networks for four years. Prior to that I'd been with Disney, ABC, ESPN four, six years, doing roughly the same thing. So, number one, we're utilizing ever rapidly changing technologies to get us to the right place. Number two is during those four years with AMC, we've employed various tactics. Some of them are called data democratization. So, that's actually not only get the right data sources not only process them correctly, but actually arm everyone in the company with immediate, easy access to this data. Because the entire business, data business, is all about insights. So, the insights -- And if you think of the business, if you for a minute separate business and business intelligence, then business doesn't want to know too much about business intelligence. What they want insights on a silver plate that will tell them what to do next. Now, that's the hardest thing, you can imagine, right? And so the search and drive for those insights has to come from every business person in the organization. Now, obviously, you don't expect them to build their own statistical algorithms and see the results in employee and machine learning. But if you arm them with that data at the tip of their fingers, they'll make many better decisions on a daily basis which means that they're actually coming up with their own small insights. So, there are small insights, big insights, and they're all extremely valuable. >> A big part of that is cultural as well, that mindset. Many companies that I work with, they're data is very siloed. I don't know if that was the case with your firm, maybe less prior to your joining. I'd be curious as to how you've achieved that cultural mindset shift. Cause a lot of times, people try to keep their own data. They don't want to share it. They want to keep it in a silo, gain political power. How did you address that? >> Vitaly: Absolutely. One of my conversations with the president, we were discussing the fact that if we were to go make recordings of how people talk about data in their organization today and go back in time and show them what they will be doing three years from now, they would be shocked. They wouldn't believe that. So, absolutely. So, culturally, educationally, bringing everyone into the place where they can understand data. They can take advantage of the data. It's an undertaking. But we are successful in doing that. >> Help me out here. Maybe I just have never acquired a little translation here, or simplification. So, you think about AMC. You've got programming. You've got your line up. I come on, I click, I go, I watch a movie and I enjoy it or watch my program, whatever. So, now in this new world of viewer habits changing, my behaviors are changing. What have you done? What have you looked for in terms of data and telling you about me that has now allowed you to modify your business and adapt to that. So, I mean, health data shouldn't drive that on a day to day basis in terms of how I access your programming. >> So, good example to that would be something we called TV everywhere. So, you said it yourself, obviously users or viewers are used to watching television as when the shows were provided via television. So, with new technologies, with streaming opportunities, today, they want to watch when they want to watch, and what they want to watch. So, one of the ways we accommodate them with that is that we don't just television, so we are on every available platform today and we are allowing viewers to watch our content on demand, digitally, when they want to watch it. So, that is one of the ways how we are reacting to it. And so, that puts us in the position as one of the B to C type of businesses, where we're now speaking directly to our consumers not via just the television. So, we're broadcasting, their watching which means that we understand how they watch and we try to react accordingly to that. Which is something that Netflix is bragging about is that they know the patterns, they actually kind of promote their business so we on that business too. >> Can you describe your innovation formula, if you will? How do you go about innovating? Obviously, there's data, there's technology. Presumably, there's infrastructure that scales. You have to be able to scale and have massive speed and infrastructure that heals itself. All those other things. But what's your innovation formula? How would you describe it? So, informally simple. It starts with business. I'm fortunate that business has desire to innovate. So, formulating goals is something that drives us to respond to it. So, we don't just walk around the thing, and look around and say, "Let's innovate." So, we follow the business goals with innovation. A good example is when we promote our shows. So, the major portion of our marketing campaigns falls on our own air. So, we promote our shows to our AMC viewers or WE tv viewers. When we do that, we try to optimize our campaigns to the highest level possible, to get the most out of ROI out of that. And so, we've succeeded and we managed today to get about 30% ROI on that and either just do better with our promotional campaigns or reallocate that time for other businesses. >> You were saying that after the first question, or during responding to the first question, about you saying we're really not ... We're a content company still. And we have incorporated data, but you really aren't, Dave and I have talked about this a lot, everybody's a data company now, in a way. Because you have to be. Cause you've got this hugely competitive landscape that you're operating in, right? In terms of getting more odd calls. >> That's right. >> So, it's got to be no longer just a part of what you do or a section of what you do. It's got to be embedded in what you do. Does it not? Oh, it absolutely is. I still think that it's a bit premature to call AMC Networks a data company. But to a degree, every company today is a data company. And with the culture change over the years, if I used to solicit requests and go about implementing them, today it's more of a prioritization of work because every department in the company got educated to the degree that they all want to get better. And they all want those insights from the data. They want their parts of the business to be improved. And we're venturing into new businesses. And it's quite a bit in demand. >> So, is it your aspiration to become a data company? Or is it more data-driven sort of TV network? How would you sort of view that? >> I'd like to say data-driven TV network. Of course. >> Dave: Okay. >> It's more in tune with reality. >> And so, talk about aligning with the business goals. That's kind of your starting point. You were talking earlier about a gut feel. We were joking about baseball. Moneyball for business. So, you're a data person. The data doesn't lie, etc. But insights sometimes are hard. They don't just pop out. Is that true? Do you see that changing as the time to insight, from insight to decision going to compress? What do you see there? >> The search for insights will never stop. And the more dense we are in that journey the better we are going to be as a company. The data business is so much depends on technologies. So, that when technologies matures, and we manage to employ them in a timely basis, so we simply get better from that. So, good example is machine learning. There are a ton of optimizations, optimization algorithms, forecasting algorithms that we put in place. So, for awhile it was a pinnacle of our deliveries. Now, with machine learning maturing today. We are able or trying to be in tune with the audience that is changing their behavior. So, the patterns that we would be looking for manually in the past, machine is now looking for those patterns. So, that's the perfect example for our strength to catch up with the reality. What I'm hoping for, and that's where the future is, is that one day we won't be just reacting utilizing machine learning to the change in patterns in behavior. We are actually going to be ahead of those patterns and anticipate those changes to come, and react properly. >> I was going to say, yeah, what is the next step? Because you said that you are reacting. >> Vitaly: I was ahead of your question. >> Yeah, you were. (laughter) So, I'm going to go ahead and re-ask it. >> Dave: Data guy. (laughter) >> But you've got to get to that next step of not just anticipating but almost creating, right, in your way. Creating new opportunities, creating news data to develop these insights into almost shaping viewer behavior, right? >> Vitaly: Totally. So, like I said, optimization is one avenue that we pursue and continue to pursue. Forecasting is another. But I'm talking about true predictability. I mean, something goes beyond just to say how our show will do. Even beyond, which show would do better. >> John: Can you do that? Even to the point and say these are the elements that have been successful for this genre and for this size of audience, and therefore as we develop programming, whether it's in script and casting, whatever. I mean, take it all the way down to that micro-level to developing almost these ideals, these optimal programs that are going to be better received by your audience. >> Look, it's not a big secret. Every company that is in the content business is trying to get as many The Walking Deads as they can in their portfolio. Is there a direct path to success? Probably not, otherwise everyone would have been-- >> John: Over do it. >> Yeah, would be doing that. But yeah, so those are the most critical and difficult insights to get ahold of and we're working toward that. >> Are you finding that your predictive capabilities are getting meaningfully better? Maybe you could talk about that a little bit in terms of predicting those types of successes. Or is it still a lot of trial and error? >> I'd like to say they are meaningfully better. (laughter) Look, we do, there are obviously interesting findings. There are sometimes setbacks and we learn from it, and we move forward. >> Okay, as good as the weather or better? Or worse? (laughs) >> Depends on the morning and the season. (laughter) >> Vitaly, how have your success or have your success measurements changed as we enter this world of digital and machine learning and artificial intelligence? And if so, how? >> Well, they become more and more challenging and complex. Like, I gave an example for data democratization. It was such an interesting and telling company-wide initiative. And at the time, it felt as a true achievement when everybody get access to their data on their desktops and laptops. When we look back now a few years, it was a walk in the park to achieve. So, the more complex data and objectives we set in front of ourselves, the more educated people in the company become, the more challenging it is to deliver and take the next step. And we strive to do that. >> I wonder if I can ask you a question from a developers perspective. You obviously understand the developer mindset. We were talking to Dennis earlier. He's like, "Yeah, you know, it's really the data scientists that are loving the data, taking a bath in it. The data engineers and so forth." And I was kind of pushing on that saying, "Well, but eventually the developers have to be data-oriented. Data is the new development kit. What's your take? I mean, granted the 10 million Java developers most of them are not focused on the data per se. Will that change? Is that changing? >> So, first of all, I want separate the classical IT that you just referred to, which are developers. Because this discipline has been well established whether it's Waterfall or Agile. So, every company has those departments and they serve companies well. Business intelligence is a different animal. So, most of the work, if not all of the work we do is more of an R&D type of work. It is impossible to say, in three months I'll arrive with the model that will transform this business. So, we're driving there. That's the major distinction between the two. Is it the right path for some of the data-oriented developers to move on from, let's say, IT disciplines and into BI disciplines? I would highly encourage that because the job is so much more challenging, so interesting. There's very little routine as we said. It's actually challenge, challenge, and challenge. And, you know, you look at the news the way I do, and you see that data scientists becomes the number one desired job in America. I hope that there will be more and more people in that space because as every other department was struggling to find good people, right people for the space, and even within that space, you have as you mentioned, data engineers. You have data scientists or statisticians. And now it's maturing to the point that you have people who are above and beyond that. Those who actually can envision models not to execute on them. >> Are you investigating blockchain and playing around with that at all? Is there an application in your business? >> It hasn't matured fully yet in our hands but we're looking into it. >> And the reason I ask is that there seems to me that blockchain developers are data-oriented. And those two worlds, in my view, are coming together. But it's earlier days. >> Look, I mean, we are in R&D space. And like I said, we don't know exactly, we can't fully commit to a delivery. But it's always a balance between being practical and dreaming. So, if I were to say, you know, let me jump into a blockchain right now and be ahead of the game. Maybe. But then my commitments are going to be sort of farther ahead and I'm trying to be pragmatic. >> Before we let you go, I got to give you 30 seconds on your Yankees. How do you feel about the season coming up? >> As for with every season, I'm super-excited. And I can't wait until the season starts. >> We're always excited when pitchers and catchers show up. >> That's right. (laughter) >> If I were a Yankee fan, I'd be excited too. I must admit. >> Nobody's lost a game. >> That's right. >> Vitaly, thank you for being with us here. We appreciate it. And continued success at AMC Networks. Thank you for having me. >> Back with more on theCUBE right after this. (upbeat techno music)

>>Welcome to this cube conversation that is a part of the AWS startup showcase. I'm lisa martin I've got with me now the ceo of amazon john Vitaly john welcome to the cube >>lisa. It's a pleasure to be here. Nice to see you. >>Likewise give our audience in a real liaison and what it is that you guys do specifically in pharma and life sciences. >>Well, you can find that in our, the name of the company is on uh, we think of us as leading uh, customers to the horizon of AI and pharmaceutical, biological manufacturing. And uh, we're all about helping our customers take The step into Pharma 40 and really realized the value of leveraging, machine learning and artificial intelligence in the manufacturing process so they can get higher yields and predictability and ultimately better outcomes for their patients. >>Is your technology built on AWS? >>Absolutely. From the ground up. We leverage, yeah, we leveraged as much as we can from AWS innovation and, you know, a few years ago, when our founders envisioned the future of manufacturing in this industry and where it needs to go first thought was go with a leader to build the solutions and of course A W. S. Is by far the largest provider of this type of technology. And we're happy to say that we're helping and partnering with A W. S. Two to advance the science of artificial intelligence in life sciences. And uh it's just a natural fit for us to continue to leverage the platform on behalf of our customers. >>I like that. The Ai horizon. Excellent. So talk to me a little bit about, you know, the last year has been presented many challenges and also opportunities for people in every industry. I'm just wondering what are some of the changes that we've seen? Farm and life sciences companies have become household names for example, but talk to me about some of the the key initiatives in smart manufacturing and what pharma companies require. >>Well sure, you know farmer companies and biotech companies like look into the lessons from other industries where ai has been widely adopted. If you look at uh manufacturing and other industries has been widely adopted for a number of years. Tesla is a great example of how to use A. I. And robotics and and data science uh to advance uh the efficiency of manufacturing globally. Uh that's exactly what we're trying to achieve here in in life sciences. So um you know, a lot of the leading innovators in this space have been working in their labs with data science teams to you know find new ways to collect data uh to cleanse that data, make it data that's useful across the enterprise. Um but they haven't really tackled, you know, continuous processing in manufacturing yet. There are a number of leaders that are mapping out strategies and they've begun to go down this path. Um But most are really looking at how first to bring the data together in a way that it could be democratized and anonymous in some cases and used across the enterprise. Uh There's a model that we've adopted in terms of our product strategy and how we engage customers and that's the uh the the pharmaceutical maturity model which was developed by the bio forum. This maturity models is a great way for companies and vendors alike innovators to look at how to help Advance their capabilities from one level to the next. And so we help customers understand where they are in that journey and we look for the areas where they can get traction more quickly. They can see value sooner and therefore the adoption would would be accelerating across across their their sites. And in different ways of use. >>Is that maturity model? That farm of maturity model? Is it is it built on or based on digital transformation? >>Absolutely. It's all about digital transformation. And so the model really begins with pre digital and you'd be amazed to find I think the the amount of Excel spreadsheets that are still used in manufacturing today and that would be what we would consider to be pretty much pre digital because that data is not accessible. It's only used by the operator or the user. So it's really about getting from that level to uh breaking down data silos and bringing that data together and harmonizing the data and making it useful. The next level would be about the connected plant actually connecting machines and data lakes um to begin to get more value and find find more ways to improve the processes. And then you move up to using advanced analytics and AI and then ultimately have an enterprise wide adaptive manufacturing capabilities, which is really the ultimate vision, ultimate goal. Every manufacturer has. >>One of the things that we've been talking about for the last 14 plus months or so is really the acceleration in cloud adoption, digital transformation as really a survival mechanism that many industries undertook. And we saw all of us go remote or many of us and be dependent on cloud based collaboration tools. For example, I'm curious in the pharmaceutical industry again, as I said, you know, we we know that the big three and for household names that many of us have been following for the last 14 months or so. What have you seen in terms of acceleration? Informal companies going all right, we need to figure out where we are in this maturity model. We need to be able to accelerate, you know, drug discovery, be able to get access to data. Has that accelerated in the Covid era? >>Covid has been the great catalyst of all time for this industry. Ah and I think it was a wake up call for a lot of, a lot of people in the industry to recognize that uh, just because we have the highest quality standards and we have highest level of compliance requirements and um, we ultimately all think about efficacy and patient safety as our goal to achieve the highest levels of quality. Everyone agrees with that. What the realization was is that we do not have the capacity in any, any geography or with any company, um, to meet the demands that we're seeing today demands to get product to market the demand to get the supply chain right and make it work for manufacturing. The, uh, the uh, The opportunity to partner to get there was, you know, you can see that by the way companies came together to partner for COVID-19 vaccine manufacturing production. And so, um, it was a wake up call that it's time to get over the kind of cultural barriers, risk aversion and really come together to coalesce around a a smart manufacturing strategy that has to be combined with a G XP or good manufacturing compliance standards. And that has to be designed in to the technology and manufacturing processes Together. That's Pharma 4.0, >>got it. Thank you. Let's dig in more to that GSP compliance. And you guys, we talk about that in different industries. The X being, you know, X for X type of industry, talk to me about the compliance regulations and your G XP AI platform and how you guys built on top of amazon, help customers evolve their maturity and facilitate complaints. >>Absolutely. So as I alluded to earlier, one of the biggest challenges is just getting the data together in a place that you can actually manage it. And because there's so many legacy systems and on predominantly on prem technologies and use today, cloud is starting to gain a lot more traction, but it's been limited to uh kind of tier two and tier three data. Uh so now we're seeing uh you know, the recognition that uh just having a data link isn't enough. And so uh we have to overcome, you know, the biggest barrier is really a version to change and change management is really a huge part of any customer being successful. And I think with a W S and us, we were working together to help customers customers understand the type of change management that's required. It's not enough to say, well, we're going to apply the old techniques and processes and use new technology. It just doesn't work that way. If you're adding people uh, and scaling up people just to do validation, worked on a brand new platform, like AWS offers, like we offer on top of AWS, you just won't get three return on investment, you won't get the outcomes and results you're targeting. Uh you have to really have a full strategy in place. Um but you can, and start in small ways, you can start to get traction with use cases that might not have the a huge impact that you're looking for, but it's a way to get started. And uh, the AWS platform is, you know, a great way to look at um, a strategy to scale manufacturing not just in one site but across multiple sites because it's really a data management strategy uh for us using US components uh to build our data collection technology was the starting point. So how do you bring this day together and make it easy and with low overhead and begin to use Ai at the point of collection? So we built our technology with AWS components to do that it's called we call them be data feeders and those are agents that go out and collect that data and bring it together. We also because of the way at AWS innovated around data management we can use a multitude of components to continue to build capabilities on top of what we have today. So we're excited to partner to follow the AWS Roadmap but also continue to add value to what A. W. S. Does today for customers. >>Right? Seems very symbiotic but also your gives you the platform gives you the agility and flexibility that you need to turn things on a dime. I like how you said Covid was a catalyst. I've been saying that for a year now there are things that it has catalyzed for the good and one of those that we've seen repeatedly is that the need for real time data access in many industries like life sciences and pharma is no longer a nice to have but it's incredibly challenging to get real time access to high quality data. Be able to run analytics on that you know, identify where the supply chain in the manufacturing process. For example things can be optimized. Give me an example or some examples of some of the use cases that you guys are working with customers on. I imagine things like that to process optimization, anomaly detection. But what are some of those key use cases in which you really excel? >>Well, it all starts with with what we can do around predictions. There's a lot of data science work being done today, understand variability and how to reduce deviations and how to get more um of predictions to know what is expected to happen. Uh But a lot of that doesn't get applied to the processes. It's not applied as a change the process because that requires revalidation of that entire process. Our platform brings huge value to customers and partners because we do the qualification and validation on the platform in real time. And so that eliminates the needs to go back out and deploy people and uh track and re document uh and re validate what's going on in the process. So that that just takes a huge uh responsibility in some cases liabilities off off of the operators and uh the folks analyzing the data. So that's that's really to get to real time. You have to think carefully about how to apply apply ai because a I was developed in a scientific way but you also have to apply it in a scientific way to to these critical processes in manufacturing. And so that's that's only done uh on a platform, you can't do it on a kind of a stand alone basis. You have to leverage a platform because you're analysing changes to the data and to the code being used to collect and analyze the data that all has to be documented. And that's that's done by our capabilities are using to audit or create audit trails uh to any changes that are happening in the process. And so that's a critical critical process monitoring capability. That is almost impossible to do manually. Uh Some some would say it's impossible to do manually. Uh so uh the the ability to to qualify algorithms to validate in real time enables real time manufacturing and there's a F. D A. Uh I would I would say mandate but guidance called continuous process verification cPV that they will be coming out with additional guidance on that this year. That's really there to uh tell tell manufacturers that they should be getting to real time capabilities. They should be driving their investments and and types of deployments to get to real time manufacturing. That's the only way you can predict deviations and predict anomalies and deal with them in the process and track it. >>So give me give me a snapshot of a customer or two that you've worked with in the last year as they were rapidly evolving and adjusting to the changes going on. How did you help some of these customers extract more value from their pharma manufacturing processes, understand what it is that they need to do to embrace A. I. And get to that real time. >>Absolutely. So, you know, most of our customers are facing the challenge and dilemma that just adding more people and more resources and even upgrading existing technologies or adding more data scientist has a limit. They've reached the limit of improvement that they can make to these processes in the output in manufacturing. So the next natural step would be to say, okay, what science can I apply here and what technology is available To really get to that next one or two improvement in the processes. And it's really critical to look at um you know, not just one use case, but how can I address multiple problems using the same technology? So bringing multi variant uh multi variable excuse me. Um analysis capabilities um is is something that's done in every other industry um but it has not been applied here in terms of changing how manufacturing works today. We can do that, we can we can do multi variable analysis in real time, we can predict what will happen. We can actually alert the operator to make changes to the process based on uh a number of predictions of what will happen in a batch or series of matches in manufacturing. We also bring unstructured data into those calculations that wasn't possible before cloud technology came along and before a I was deployed. Um So now we can look at environmental inputs, we can look at um upstream data that can be used for improving um you know, the yield on batches. So the you know, the main um focus today is you know, how do I get, reduce my risk around asset management? How can I improve visibility into the supply chain? How can I reduce deviations in these processes? How can I get more yield? How can I optimize the yield uh in any given batch uh to improve uh you know, the entire process but also reduce costs in each step of the way. Uh So uh the good news is that when you apply our technology and our know how uh there's an immediate positive impact. There's a customer, we're working with very large customer where we walked in and they said we have this problem, we've reached a certain level of optimization and yield. We can't seem to get it to go any higher. and within six weeks we had a solution in place and we are saving them tens of millions of dollars in material loss just in that once one step in the process that's worth hundreds of millions of dollars in terms of finished product. Uh and if you apply that across multiple lines and across multiple manufacturing sites for that customer, we're talking hundreds of millions of dollars of savings, um >>significant impact, significant business impact that your customers I saw on the website, you know, R. O. I. And was at six when I get this right. I had it here somewhere um quite quickly. But the key thing there is that these organizations actually are really moving their business forward. You just gave some great examples of how you can do that. And just kind of a phase one of the project. Let me ask you this in in a post Covid world, assuming we'll get there hopefully soon. Where is in your opinion? Um Ai and ml for pharma companies, is it going to be something that is is for those that adopt it and adopt all the change management needed to do that? Is it going to be kind of the factor in deciding the winners and the losers of tomorrow? Okay, >>well, I don't want to lay down predictions like that, but I would, what I would say is uh all of thought leaders out there have have openly shared and privately shared that this is exactly where the industry has to go to meet the demands. Not just of ramping up COVID-19 vaccine production on a global basis, which we have to do. It's also dealing with how do we how do we uh scale up for personalized medicine, which requires small, small batch manufacturing? How do we turn over lines of manufacturing more efficiently to get more drugs to market more different types of drugs to market, how to contract manufacturers deal with all these pressures, um, and still serve their customers and innovate. Uh, there's also the rise of generics there, you know, that's bringing on cost pressures for big pharma particularly. And so these are all moving the industry in the right direction to respond to these on an individual basis. Would would definitely require the use of Ai and Ml But when you bring it all together, there's a huge huge of push for finding and finding breakthroughs to increase capacity and quality at the same time. >>Yeah, tremendous opportunity. My last question for you, john is a bit more on the personal side. I know you're a serial entrepreneur. What drew you to a zon when you have the opportunity? I can only imagine based on some of the things that you've said. But what was it that you said? This is my next great >>opportunity. That's a great question because I asked myself that question, uh so having been in the industry for for a long time, having been with very innovative companies my whole career, uh I knew that uh manufacturing had fallen behind even further in terms of innovating using the latest cloud technologies and ai in particular, I knew that from running another company uh that focused on the use of predictive analytics. And so uh given all the vectors coming together, the market pressure that's happening on the technology, absolutely. Being a maturity level that we could we could make these things a reality for customers in the size of the challenge. And market opportunity was just overwhelming. It was it was enough to make me jump in with both feet. So I'm very happy uh to be leading such a great team and amazing, amazing talent at amazon and super excited about our partnership with a W. S and where that's going and solving very, very complex and very critical, uh, challenges that our customers are facing together as partners. >>Absolutely. Well, john, thank you for joining me today and talking to us about who is on is what you're doing, particularly in pharma and life sciences, smart manufacturing and what you're enabling in a covid catalysis sort of way. We appreciate you joining us here today. >>This has been a pleasure. Thanks for having me. >>Likewise for john Vitaly, I'm lisa martin, you're watching the cube.