Chris Degnan, Snowflake & Chris Grusz, Amazon Web Services | Snowflake Summit 2022
(upbeat techno music) >> Hey everyone, and welcome back to theCUBE's coverage of Snowflake Summit '22 live from Caesar's Forum in beautiful, warm, and sunny Las Vegas. I'm Lisa Martin. I got the Chris and Chris show, next. Bear with me. Chris Degnan joins us again. One of our alumni, the Chief Revenue Officer at Snowflake. Good to have you back, Chris. >> Thank you for having us. >> Lisa: Chris Grusz also joins us. Director of Business Development AWS Marketplace and Service Catalog at AWS. Chris and Chris, welcome. >> Thank you. >> Thank you. >> Thank you. Good to be back in person. >> Isn't it great. >> Chris G: It's so much better. >> Chris D: Yeah. >> Nothing like it. So let's talk. There's been so much momentum, Chris D, at Snowflake the last few years. I mean the momentum at this show since we launched yesterday, I know you guys launched the day before with partners, has been amazing. A lot of change, and it's like this for Snowflake. Talk to us about AWS working together with Snowflake and some of the benefits in it from your customer. And then Chris G, I'll go to you for the same question. >> Chris G: Yep. >> You know, first of all, it's awesome. Like, I just, you know, it's been three years since I've had a Snowflake Summit in person, and it's crazy to see the growth that we've seen. You know, I can't, our first cloud that we ever launched on top of was, was AWS, and AWS is our largest cloud, you know, in in terms of revenue today. And they've been, they just kind of know how to do it right. And they've been a wonderful partner all along. There's been challenges, and we've kind of leaned in together and figured out ways to work together, you know, and to solve those challenges. So, been a wonderful partnership. >> And talk about it, Chris G, from your perspective obviously from a coopetition perspective. >> Yep. >> AWS has databases, cloud data forms. >> Chris G: Yeah. >> Talk to us about it. What was the impetus for the partnership with Snowflake from AWS's standpoint? >> Yeah, well first and foremost, they're building on top of AWS. And so that, by default, makes them a great partner. And it's interesting, Chris and I have been working together for, gosh, seven years now? And the relationship's come a really long way. You know, when we first started off, we were trying to sort out how we were going to work together, when we were competing, and when we're working together. And, you know, you fast forward to today, and it's just such a good relationship. Because both companies work backwards from customers. And so that's, you know, kind of in both of our DNA. And so if the customer makes that selection, we're going to support them, even from an AWS perspective. When they're going with Snowflake, that's still a really good thing for AWS, 'cause there's a lot of associated services that Snowflake either integrates to, or we're integrating to them. And so, it's really kind of contributed to how we can really work together in a co-sell motion. >> Talk to us, talk about that. The joint GOTO market and the co-selling motion from Snowflake's perspective, how do customers get engaged? >> Well, I think, you know, typically we, where we are really good at co-selling together is we identify on premise systems. So whether it's, you know, some Legacy UDP system, some Legacy database solution, and they want to move to the cloud? You know, Amazon is all in on getting everyone to the cloud. And I think that's their approach they've taken with us is saying we're really good at accelerating that adoption and moving all these, you know, massive workloads into the cloud. And then to Chris's point, you know, we've integrated so nicely into things like SageMaker and other tool sets. And we, we even have exciting scenarios where they've allowed us to use, you know, some of their Amazon.com retail data sets that we actually use in data sharing via the partnership. So we continue to find unique ways to partner with our great friends at Amazon. >> Sounds like a very deep partnership. >> Chris D: Yeah. Absolutely. >> Chris G: Oh, absolutely, yeah. We're integrating into Snowflake, and they're integrating to AWS. And so it just provides a great combined experience for our customers. And again, that's kind of what we're both looking forward from both of our organizations. >> That customer centricity is, >> Yeah. >> is I think the center of the flywheel that is both that both of you, your companies have. Chris D, talk about the the industry's solutions, specific, industry-specific solutions that Snowflake and AWS have. I know we talked yesterday about the pivot from a sales perspective >> Chris D: Yes. >> That snowflake made in recent months. Talk to us about the industries that you are help, really targeting with AWS to help customers solve problems. >> Yeah. I think there's, you know, we're focused on a number of industries. I think, you know, some of the examples, like I said, I gave you the example of we're using data sharing to help the retail space. And I think it's a really good partnership. Because some of the, some companies view Amazon as a competitor in the retail space, and I think we kind of soften that blow. And we actually leverage some of the Amazon.com data sets. And this is where the partnership's been really strong. In the healthcare space, in the life sciences space, we have customers like Anthem, where we're really focused on helping actually Anthem solve real business problems. Not necessarily like technical problems. It's like, oh no, they want to get, you know, figure out how they can get the whole customer and take care of their whole customer, and get them using the Anthem platform more effectively. So there's a really great, wonderful partnership there. >> We've heard a lot in the last day and a half on theCUBE from a lot of retail customers and partners. There seems to be a lot of growth in that. So there's so much change in the retail market. I was just talking with Click and Snowflake about Urban Outfitters, as an example. And you think of how what these companies are doing together and obviously AWS and Snowflake, helping companies not just pivot during the pandemic, but really survive. I mean, in the beginning with, you know, retail that didn't have a digital presence, what were they going to do? And then the supply chain issues. So it really seems to be what Snowflake and its partner Ecosystem is doing, is helping companies now, obviously, thrive. But it was really kind of like a no-go sort of situation for a lot of industries. >> Yeah, and I think the neat part of, you know, both the combined, you know, Snowflake and AWS solution is in, a good example is DoorDash, you know. They had hyper growth, and they could not have handled, especially during COVID, as we all know. We all used DoorDash, right? We were just talking about it. Chipotle, like, you know, like (laughter) and I think they were able to really take advantage of our hyper elastic platforms, both on the Amazon side and the Snowflake side to scale their business and meet the high demand that they were seeing. And that's kind of some of the great examples of where we've enabled customer growth to really accelerate. >> Yeah. Yeah, right. And I'd add to that, you know, while we saw good growth for those types of companies, a lot of your traditional companies saw a ton of benefit as well. Like another good example, and it's been talked about here at the show, is Western Union, right? So they're a company that's been around for a long time. They do cross border payments and cross currency, you know, exchanges, and, you know, like a lot of companies that have been around for a while, they have data all over the place. And so they started to look at that, and that became an inhibitor to their growth. 'Cause they couldn't get a full view of what was actually going on. And so they did a lengthy evaluation, and they ended up going with Snowflake. And, it was great, 'cause it provided a lot of immediate benefits, so first of all, they were able to take all those disparate systems and pull that into Snowflake. So they finally had a single source of the truth, which was lacking before that. So that was one of the big benefits. The second benefit, and Chris has mentioned this a couple times, is the fact that they could use data sharing. And so now they could pull in third data. And now that they had a holistic view of their entire data set, they could pull in that third party data, and now they could get insights that they never could get before. And so that was another large benefit. And then the third part, and this is where the relationship between AWS and Snowflake is great, is they could then use Amazon SageMaker. So one of the decisions that Western Union made a long time ago is they use R for their data science platform, and SageMaker supports R. And so it really allowed them to dovetail the skill sets that they had around data science into SageMaker. They could now look across all of Snowflake. And so that was just a really good benefit. And so it drove the cost down for Western Union which was a big benefit, but the even bigger benefit is they were now able to start to package and promote different solutions to their customers. So they were effectively able to monetize all the data that they were now getting and the information they were getting out of Snowflake. And then of course, once it was in there, they could also use things like Tableau or ThoughtSpot, both of which available in AWS Marketplace. And it allowed them to get all kinds of visualization of data that they never got in the past. >> The monetization piece is, is interesting. It's so challenging for organizations, one, to get that single source view, to be able to have a customer 360, but to also then be able to monetize data. When you're in customer conversations, how do you help customers on that journey, start? Because the, their competitors are clearly right behind them, ready to take first place spot. How do you help customers go, all right this is what we're going to do to help you on this journey with AWS to monetize your data? >> I think, you know, it's everything from, you know, looking at removing the silos of data. So one of the challenges they've had is they have these Legacy systems, and a lot of times they don't want to just take the Legacy systems and throw them into the cloud. They want to say, we need a holistic view of our customer, 360 view of our customer data. And then they're saying, hey, how can we actually monetize that data? That's where we do everything from, you know, Snowflake has the data marketplace where we list it in the data marketplace. We help them monetize it there. And we use some of the data sets from Amazon to help them do that. We use the technologies like Chris said with SageMaker and other tool sets to help them realize the value of their data in a real, meaningful way. >> So this sounds like a very strategic and technical partnership. >> Yeah, well, >> On both sides. >> It's technical and it's GOTO market. So if you take a look at, you know, Snowflake where they've built over 20 integrations now to different AWS services. So if you're using S3 for object storage, you can use Snowflake on top of that. If you want to load up Snowflake with Glue which is our ETL tool, you can do that. If you want to use QuickSite to do your data visualization on top of Snowflake, you can do that. So they've built integration to all of our services. And then we've built integrations like SageMaker back into Snowflake, and so that supports all kinds of specific customer use cases. So if you think of people that are doing any kind of cloud data platform workload, stuff like data engineering, data warehousing, data lakes, it could be even data applications, cyber security, unistore type things, Snowflake does an excellent job of helping our customers get into those types of environments. And so that's why we support the relationship with a variety of, you know, credit programs. We have a lot of co-sell motions on top of these technical integrations because we want to make sure that we not only have the right technical platform, but we've got the right GOTO market motion. And that's super important. >> Yeah, and I would add to that is like, you know one of the things that customers do is they make these large commitments to Amazon. And one of the best things that Amazon did was allow those customers to draw down Snowflake via the AWS Marketplace. So it's been wonderful to his point around the GOTO market, that was a huge issue for us. And, and again, this is where Amazon was innovative on identifying the ways to help make the customer have a better experience >> Chris G: Yeah. >> Chris D: and put the customer first. And this has been, you know, wonderful partnership there. >> Yeah. It really has. It's been a great, it's been really good. >> Well, and the customers are here. Like we said, >> Yep. >> Yes. Yes they are. >> we're north of 10,000 folks total, and customers are just chomping at the bit. There's been so much growth in the last three years from the last time, I think I heard the 2019 Snowflake Summit had about 1500 people. And here we are at 10,000 plus now, and standing-room-only keynote, the very big queue to get in, people turned away, pushed back to an overflow area to be able to see that, and that was yesterday. I didn't even get a chance to see what it was like today, but I imagine it was probably the same. Talk about the, when you're in customer conversations, where do you bring, from a GTM perspective, Where do you bring Snowflake into the conversation? >> Yeah >> Obviously, there's Redshift there, what does that look like? I imagine it follows the customer's needs, challenges. >> Exactly. >> Compelling events. >> Yeah. We're always going to work backwards from the customer need, and so that is the starting point for kindling both organizations. And so we're going to, you know, look at what they need. And from an AWS perspective, you know, if they're going with Snowflake, that's a very good thing. Right? 'Cause one of the things that we want to support is a selection experience to our AWS customers and make sure that no matter what they're doing, they're getting a very good, supported experience. And so we're always going to work backwards from the customer. And then once they make that technology decision, then we're going to support them, as I mentioned, with a whole bunch of co-sell resources. We have technical resources in the field. We have credit programs and in, you know, and, of course, we're going to market in a variety of different verticals as well with Snowflake. If you take a look at all the industry clouds that Snowflake has spun up, financial services and healthcare, and media entertainment, you know, those are all very specific use cases that are very valuable to an AWS customer. And AWS is going more and more to market on a vertical approach, and so Snowflake really just fits right in with our overall strategy. >> Right. Sounds like very tight alignment there. That mission alignment that Frank talked about yesterday. I know he was talking about that with respect to customers, but it sounds like there's a mission alignment between AWS and Snowflake. >> Mission alignment, yeah. >> I live that every week. (laughter) >> Sorry if I brought up a pain point. >> Yeah. Little bit. No. >> Guys, what's, in terms of use cases, obviously we've been here for a couple days. I'm sure you've had tremendous feedback, >> Chris G: Yeah. >> from, from customers, from partners, from the ecosystem. What's next, what can we expect to hear next? Maybe give us a preview of re:Invent in the few months. >> Preview of re:Invent. Yeah. No, well, one of the things we really want to start doing is just, you know, making the use case of, of launching Snowflake on AWS a lot easier. So what can we do to streamline those types of experiences? 'Cause a lot of times we'll find that customers, once they buy a third party solution like Snowflake, they have to then go through a whole series of configuration steps, and what can we do to streamline that? And so we're going to continue to work on that front. One of the other places that we've been exploring with Snowflake is how we work with channel partners. And, you know, when we first launched Marketplace it was really more of an app store model that was ISVs on one side and channel partners on the other, and there wasn't really a good fit for channel partners. And so four years ago we retrofitted the platform and have opened it up to resellers like an SHI or SIs like Salam or Deloitte who are top, two top SIs for Snowflake. And now they can use Marketplace to resell those technologies and also sell their services on top of that. So Snowflake's got a big, you know, practice with Salam, as I mentioned. You know, Salam can now sell through Marketplace and they can actually sell that statement of work and put that on the AWS bill all by virtue of using Marketplace, that automation platform. >> Ease of use for customers, ease of use for partners as well. >> Yes. >> And that ease of use is it's no joke. It's, it's not just a marketing term. It's measurable and it's about time-to-value, time-to-market, getting customers ahead of their competition so that they can be successful. Guys, thanks for joining me on theCUBE today. Talking about AWS and >> Nice to be back. Nice to be back in person. >> Isn't it nice to be back. It's great to be actually sitting across from another human. >> Exactly. >> Thank you so much for your insights, what you shared about the partnership and where it's going. We appreciate it. >> Thank you. >> Cool. Thank you. >> Thank you. >> All right guys. For Chris and Chris, I'm Lisa Martin, here watching theCUBE live from Las Vegas. I'll be back with my next guest momentarily, so stick around. (Upbeat techno music)
SUMMARY :
One of our alumni, the Chief Chris and Chris, welcome. Good to be back in person. and some of the benefits and it's crazy to see the And talk about it, Chris AWS has databases, Talk to us about it. And so that's, you know, and the co-selling motion And then to Chris's point, you know, and they're integrating to AWS. of the flywheel that is both that you are help, really targeting I think, you know, some of the examples, So it really seems to be what Snowflake and the Snowflake side And so they started to look at that, this is what we're going to do to help you I think, you know, and technical partnership. at, you know, Snowflake And one of the best And this has been, you know, It's been a great, it's been really good. Well, and the customers in the last three years I imagine it follows the And so we're going to, you That mission alignment that I live that every week. obviously we've been partners, from the ecosystem. and put that on the AWS bill all by virtue Ease of use for so that they can be successful. Nice to be back in person. Isn't it nice to be back. Thank you so much for your For Chris and Chris,
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Kevin Miller, Amazon Web Services
(uplifting music) >> The data lake we see is evolving and ChaosSearch has built some pretty cool tech to enable customers to get more value out of data that's in lakes so that it doesn't become stagnant. Time to dig deeper, dive deeper into the water. We're here with Kevin Miller, who's the vice president and general manager of S3 at Amazon Web Services. We're going to talk about activating S3 for analytics. Kevin, welcome. Good to see you again. >> Yeah, thanks, Dave. It's great to be here again. >> So S3 was the very first service offered by AWS 15 years ago. We covered that out in Seattle. It was a great event you guys had. It has become the most prominent and popular example of object storage in the marketplace. And for years, customers use S3 as simple, cheap data storage, but because there's so much data now stored in S3, customers are looking to do more with the platform. So, Kevin, as we look ahead to reinvent this year, we're super excited about that, what's new? What's got you excited when it comes to the AWS flagship storage offering? >> Yeah, Dave. Well, that's right. And we're definitely looking forward to reinvent. We have some fun things that we're planning to announce there, so stay tuned on those. But, I'd say that one of the things that's most exciting for me as customers do more with their data and look to store more, to capture more of the data that they're generating every day, is our storage class that we had an announce a few years ago. But we actually just announced some improvements to the S3, intelligent-tiering storage class. And this is really our storage class, the only one in the cloud at this point that delivers automatic storage cost savings for customers where the data access patterns change. And that can happen, for example, as customers have some data that they're collecting and then a team spins up and decides to try to do something more with that data, and that data that was very cool and sitting sort of idle is now being actively used. And so with intelligent tiering, we're automatically monitoring data. And then, for customers there's no retrieval costs and no tiering charges. We're automatically moving the data into an access tier that reduces their costs, though, when that that data is not being accessed. So we've announced some improvements to that just a few months ago. And I'll just say, I look forward to some more announcements at reinvent, that will continue to extend what we have in our intelligent-tiering storage class. >> That's cool, Kevin. I mean, you've seen, you know, that technology, that tiering concept had been around, you know. But since back in the mainframe days the problem was it was always inside a box. So you didn't have the scale of the cloud and you didn't have that automation. So, I want to ask you, as the leader of S3, that business, when you meet with customers, Kevin, what do they tell you that they're facing as challenges when they want to do more, get better insights out of all that data that they've moved into S3? >> Well, I think that's just it, Dave. I think that most customers I speak with, of course they have the things that they want to do with their storage costs, you know: reducing storage costs and just making sure they have capacity available. But increasingly I think the real emphasis is around business transformation. What can I do with this data, that's very unique and different that unlike, you know, prior optimizations where it would just reduce the bottom line, they're saying, what can I do that will actually drive my top line more by either, you know, generating new product ideas, allowing for faster closed-loop process for acquiring customers? And so it's really that business transformation and everything around it that I think is really exciting. And for a lot of customers, that's a pretty long journey. And helping them get started on that, including transforming their workforce, and up-skilling, you know, parts of their workforce to be more agile and more oriented around software development, developing new products using software. >> So when I first met the folks at ChaosSearch, Thomas took me through sort of the architecture with Ed as well. They had me at "you don't have to move your data." That was the grabber for me. And there are a number of public customers, Digital River, Blackboard, or Klarna, we're going to get the customer perspective little later on, and others, that use both AWS S3 and ChaosSearch. And they're trying to get more out of their S3 data and execute analytics at scale. So, wonder if you could share with us, Kevin, what types of activities and opportunities do you see for customers like these that are making the move to put their enterprise data in S3, in terms of capabilities and outcomes that they are trying to achieve and are able to achieve beyond using S3 as just a bit bucket? >> Right. Well, Dave, I think you hit the nail on the head when you talk about outcomes. 'Cause that, I think, is key here. Customers want to reduce the time it takes to get to a tangible result that affects their business, that improves their business. And so that's one of the things that excites me about what ChaosSearch is doing here, specifically is that automatic indexing. Being able to take the data, as it is, in their bucket, index it and keep that index fresh and then allow for the customers to innovate on top of that and to try to experiment with a new capability, see why it works and then double down on the things that really do work to drive that business. And so, I just think that that capability reduces the amount of what I might call undifferentiated heavy-lifting the work to just sort of index and organize and catalog data. And instead allow customers to really focus on here's the idea, let's try to get this into production or into a test environment as quickly as possible to see if this can really drive some value for our business. >> Yeah, so you're seeing that sort of value that you've mentioned, the non-differentiated heavy-lifting, moving up the stack, right? It used to just be provisioning and managing the storage. Now it's all the layers above that. And we're going beyond that. So my question to you, Kevin, is, how do you see the evolution of all this data at scale? I'm especially interested as it pertains to data that's, of course, in S3, which is your swim lane. When you talk to customers who want to do more with their data and analytics, and, by the way, even beyond analytics, you know, where it's having conversations now in the community about building data products and creating new value. But how do you respond and how do you see ChaosSearch fitting in to those outcomes? >> Well, I think that's it, Dave. It's about kind of going up the stack and instead of spending time organizing and cataloging data, particularly as the data volumes get much larger. When modern customers and modern data lakes that we're seeing, quickly go from a few petabytes to tens, to hundreds of petabytes or more. And, when you're reaching that kind of scale of data, a single person can't reasonably kind of wrap their head around all that data, you need tools. S3 provides a number of first party tools. And, you know, we're investing in things like our S3 batch operations to really help give the end users of that data, the business owners that leverage to manage their data at scale and apply their new ideas to the data and generate, you know, pilots and production work that really drives their business forward. And so I think that, you know, ChaosSearch, again, I would just say is a good example of, you know, the kind of software that I think helps go upstack, automate some of that data management, and just help customers focus really specifically on the things that they want to accomplish for their business. >> So this is really important. I mean, we've talked for well over a decade, how to get more value out of data, and it's been challenging for a lot of organizations. But we're seeing themes of scale, automation, fine-grain tooling ecosystem participating on top of that data and then extracting that data value. Kevin, I'm really excited to see you face to face at Reinventing, and learn more about some of the announcements that you're going to make. We'll see you there. >> Yeah. Stay tuned. Looking forward to seeing you in person. Absolutely. >> All right. Great to have Kevin on. Keep it right there because in a moment we're going to get the customer perspective on how a leading practitioner is applying ChaosSearch on top of S3 to create business value from data. You're watching The Cube, your leader at digital high-tech coverage. (uplifting music)
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Kevin Miller, Amazon Web Services | ChaosSearch: Make Your Data Lake Deliver
>>Welcome back. I really liked the drill down a data lakes with ed Walsh and Thomas Hazel. They building some cool stuff over there. The data lake we see it's evolving and chaos search has built some pretty cool tech to enable customers to get more value out of data that's in lakes so that it doesn't become stagnant. Time to dig, dig deeper, dive deeper into the water. We're here with Kevin Miller. Who's the vice president and general manager of S3 at Amazon web services. We're going to talk about activating S3 for analytics. Kevin, welcome. Good to see you again. >>Yeah, thanks Dan. It's great to be here again. So >>S3 was the very first service offered by AWS 15 years ago. We covered that out in Seattle. It was a great event you guys had, it has become the most prominent and popular example of object storage in the marketplace. And for years, customers use S3 is simple, cheap data storage, but because there's so much data now stored in S3 customers are looking to do more with the platform. So Kevin, as we look ahead to reinvent this year, we're super excited about that. What's new. What's got you excited when it comes to the AWS flagship storage offering. >>Yeah. Dan, well, that's right. And we're definitely looking forward to reinvent. We have some fun things that we're planning to announce there. So stay tuned on those, but I'd say that one of the things that's most exciting for me as customers do more with their data and look to store more, to capture more of the data that they're generating every day is our storage class that we had an announced a few years ago, but we, we actually just announced some improvements to the S3 intelligent tiering storage class. And this is really our storage class. The only one in the cloud at this point that delivers automatic storage cost savings for customers where the data access patterns change. And that can happen. For example, as customers have some data that they're collecting and then a team spins up and decides to try to do something more with that data and that data that was very cool and sitting sort of idle is now being actively used. And so with intelligent tiering, we're automatically monitoring data. And then there's for customers. There's no retrieval costs and no tiering charges. We're automatically moving the data into an access tier that reduces their costs though. And that data is not being accessed. So we've announced some improvements to that just a few months ago. And I'll just say, I look forward to some more announcements at reinvent that will extend, continue to extend what we have in our intelligent tiering storage class. >>That's cool, Kevin. I mean, you've seen, you know, that technology, that tiering concept had been around, you know, but since back in the mainframe days, the problem was, it was always inside a box. So you, you didn't have the scale of the cloud and you didn't have that automation. So I want to ask you as the leader of that business, when you meet with customers, Kevin, what do they tell you that they're there they're facing as challenges when they want to do more, get better insights out of all that data that they've moved into S3? >>Well, I think that's just it, Dave. I think that most customers I speak with they, of course they have the things that they want to do with their storage costs and reducing storage costs and just making sure they have capacity available. But increasingly I think the real emphasis is around business transformation. What can I do with this data? That's very unique and different than either that unlike, you know, prior optimizations where it would just reduce the bottom line, they're saying, what can I do that will actually drive my top line more by either, you know, generating new product ideas, um, allowing for faster, you know, close, closed loop process for acquiring customers. And so it's really that business transformation and all, everything around it that I think is really exciting. And for a lot of customers, that's a pretty long journey and, and helping them get started on that, including transforming their workforce and up-skilling, you know, parts of their workforce to be more agile and more oriented around software development, developing new products using software. >>So w when I first met the folks at, at chaos search, you know, Thomas took me through sort of the architecture w with ed as well. They had me at, you don't have to move your data. That was saying that was the grabber for me. And there are a number of public customers that digital river, uh, Blackboard or Klarna, we're going to get the customer perspective little later on and others that use both AWS S3 and chaos search. And they're trying to get more out of their, their S3 data and execute analytics at scale. So wonder if you could share with us Kevin, what types of activities and opportunities do you see for customers like these that are making the move to put their enterprise data in S3 in terms of capabilities and outcomes that they are trying to achieve and are able to achieve beyond using S3 is just a Bitbucket, >>Right? Well, Dan, I think you hit the nail on the head when you talk about outcomes. Cause that I think is, is key here. Customers want to reduce the time it takes to get to a tangible result that it affects the business that improves their business. And so that's one of the things that I excites me about what CAS search is doing here specifically is that automatic indexing, being able to take the data as it is in their bucket, index it and keep that index fresh and then allow for the customers to innovate on top of that and to try to experiment with a new capability, see, see what works and then double down on the things that really do work to drive that business. And so I just think that that capability reduces the amount of what I might call undifferentiated, heavy, lifting the work to just sort of index and organize and catalog data. And instead allow customers to really focus on here's the idea. Let's try to get this into production or into a test environment as quickly as possible to see if this can really drive some value for our business. >>Yeah. So you're seeing that sort of value that you've mentioned the non-differentiated heavy lifting, moving up the stack, right. It used to just be provisioning and managing the, now it's all the layers above that and it would go and beyond that. So my question to you, Kevin, is how do you see the evolution of this, all this data at scale I'm especially interested in, as it pertains to data that's of course, an S3, which is your swim lane. When you talk to customers who want to do more with their data and analytics, and by the way, even beyond analytics, you know, where it's having conversations now in the community about, about building data products and creating new value, but how do you respond and how do you see chaos search fitting in to those outcomes? >>Well, I think that's, that's it Dave, it's about kind of going up the stack and instead of spending time organizing and cataloging data, particularly as the data volumes give much larger when the modern customers and modern data lakes that we're seeing quickly go from a few petabytes to tens, to hundreds of petabytes or more. And when you reaching that kind of scale of data, it's a single person can reasonably kind of wrap their head around all that data. You need tools as three provides a number of first party tools and, you know, we're investing in things like our S3 batch operations to really help give the end users of that data, the business owners that leverage to manage their data at scale and apply their new ideas to the data and generate, you know, pilots and production work that really drives their business forward. And so I think that, you know, cast search again, I would just say as a good example of, you know, the kind of software that I think helps go, upstack automate some of that data management and just help customers focus really specifically on the things that they want to accomplish for their, their business. >>So this is, >>I mean, we've talked for well over a decade, how to get more value out of data. And it's been challenging for a lot of organizations, but we're seeing, we're seeing themes of scale automation, fine-grain tooling ecosystem participating, uh, on top of that data and then extracting that, that data value who Kevin, I'm really excited to see you face to face at re-inventing and learn more about some of the announcements that you're going to make. We'll see you there. >>Yeah. Stay tuned. Looking forward to seeing in person absolutely >>Have Kevin on, keep it right there because in a moment we're going to get the customer perspective on how a leading practitioner is applying chaos search on top of S3 to create a business value from data you're watching the cube, your leader, digital high tech coverage.
SUMMARY :
Good to see you again. So stored in S3 customers are looking to do more with the platform. And I'll just say, I look forward to some more announcements at reinvent that will extend, that business, when you meet with customers, Kevin, what do they tell you that they're And so it's really that business transformation and all, everything around it that I think is really exciting. So w when I first met the folks at, at chaos search, you know, And so that's one of the things that I excites So my question to you, Kevin, is how do you see the evolution of this, And so I think that, you know, cast search again, I would just say as a good example of, you know, I'm really excited to see you face to face at re-inventing and learn more about some Looking forward to seeing in person absolutely of S3 to create a business value from data you're watching the cube,
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Linda Tong, AppDynamics & Dave McCann, Amazon Web Services | AWS re:Invent 2020
>> Narrator: From around the globe, it's theCUBE with digital coverage of AWS re:Invent 2020 sponsored by Intel, AWS and our community partners. >> Hello, welcome back to theCUBE's Virtual Coverage of AWS re:Invent 2020 virtual. Normally we're in person. This year because of the pandemic, we're doing it remote. We're Cube Virtual covering AWS re:Invent Virtual. I'm John for your host. We are theCUBE Virtual, two great guests here Linda Tong a general manager, AppDynamics and Dave McCann vice-president of AWS migration, marketplace and control services. Welcome to theCUBE. >> Thanks so much for having us. >> Good to see you again John. >> Linda we were talking to some AppDynamics folks and some of your customers, obviously we've been following the growth of the marketplace for many years. The confluence of the tailwinds of the innovation going on with COVID and post COVID strategies is about helping customers where they are and they're not in the office anymore. They got to get the job done. This is really important on this cloud migration of getting software in the hands of people to write these modern apps. It's a big theme. What's your perspective on this right now, because you guys are partnered with Amazon, share your vision. >> Yeah, absolutely. And you nailed it. It's with COVID-19 our customers like IT organizations are finding this need to accelerate their migration to the cloud. And what's more important is they're finding that more and more of their customers are engaging through digital experiences and with the influx of people leaning on those digital experiences during COVID, performance issues are becoming more and more apparent. And so we're helping our customers as they migrate to the cloud. And specifically to AWS, it's a big partnership for us because we need to understand how our customers and how they manage performance through these transitions can stay flawless so that they can manage those experiences for their end users. >> Yeah, Dave, I've been watching this discovery observation space, observability, service meshes, Kubernetes, cloud native higher level services have really gotten popularity have gone mainstream. So there's more and more demand for I won't call it point products. That's an old term, but in the cloud, these are just higher level services that people are adopting more of. You're seeing huge pickup in the marketplace of companies who are selling through there and engaging but it's not just selling, you're integrating. What's your vision for all of this? >> So, John, you're absolutely right. Our customers as they migrate more and more applications to the cloud and in some regulated industries they still have applications running on premise. They're really actually standing up a new operating model where they not only want observability of what's going on but I feel what we would call service management framework or a set of tools to manage the application portfolio. And companies around the world are putting together new common instance of AWS native services, such as CloudWatch CloudTrail, Service Catalog, AWS Config, Control Tower with best in class vendors like Cisco AppDynamics. And each company is building their own collection of tools into management framework that allows them to optimally modernize and manage their application portfolio. And it's a rising topic around the world. >> Linda, I want to get back to you on AppDynamics you're the leader of the team as general manager, congratulations. You know a little bit about software in the cloud and CloudScale and your career going back to Google now at AppDynamics you've seen a lot of the changes. What specifically value do you see AppDynamics and Amazon bringing to the market today? Because the world's changed. It's still large scale, there's faster speed but you can't just buy things like anymore, I've got to go in send a ticket request, go to procurement, developers want to integrate immediately. They need to integrate when they see a problem they got to integrate technology. This seems to be a trend. What's your, where is AppDynamics bringing the value of AWS to the market? >> Absolutely I think it's threefold. One it's for a lot of these developers, as they start to migrate their applications and modernize them with AWS and all the great services that are available we can partner to help them with that modernization effort while giving them visibility into the performance of those applications to make sure that they don't miss a beat as they deploy those on these new sets of services over AWS. The second thing is, for those customers that are leveraging AWS for that migration, we have a seamless integration between AppDynamics and AWS. So you can buy our service directly through AWS marketplace. So that becomes a really easy procurement. And then on top of that, as, a lot of developers have to manage hybrid employments, so new modern applications has done AWS as well as some of their traditional applications that are talking to each other. They can get that full end to end visibility leveraging AppDynamics so that they can understand what's going on across the entirety of their business as they start to lead these transformations across our organization. >> Dave, just comment on if you can, 'cause I know a little bit about some of the things you put in place, the enterprise I forget development or sales program where at the prices can be more friendly. I think this is kind of a use case where this is proving enterprises can get what they need in the marketplace that not only is it successful but you have traction with this. What's you take on... >> There's a number of motions that we're doing there John, to help large companies around the world who may have, dozens, hundreds and in comes cases with fortune 100 they're thousands of applications. And so you actually have to solve multiple challenges that the company has. On the procurement side, we're obviously working with AppDynamics to publish as a service right in AWS marketplace. And we have over 300,000 customers worldwide only AWS marketplace who are subscribing to software and provisioning out to hundreds and thousands of developers, all of whom are using their own AWS accounts. So on that provisioning and subscription experience we work deeply with the AppDynamics team to meet that a really seamless experience from discovery to provision to meter and billing. On the interoperability front, as Linda mentioned, our customers want these best in class tools like AppDynamics to work well with the other AWS services so that they can really have a very modern DevOps pipeline for those applications that are moving to more of a CICD model. And for people who are still running in a bit more of an Intel, ITSM model, they've still got to manage and monitor applications that haven't quite got there in the full modernization stack. So this is actually happening not just with the customer, the enterprise or with the ISV AppDynamics, this transitions' also working with all the consulting firms. And a lot of the large software resellers around the world, the computer centers of Europe the right spaces, the presidios of North America. The DXEs of Asia Pacific. These consulting partners are also using tools such as AppDynamics so to become a managed service provider. And in some cases on that journey to the cloud no join the customer saying I'm really busy I'm modernizing applications. Hey consulting partner, can you manage some part of my infrastructure, some part of my stack? And tools like AppDynamics and Kubernetes and AWS become really central tool kits to the new emerging managed service providers that are all around the world. >> Yeah, and I talked about this years ago with Andy Jassy and I think we were riffing on this run this new set of category creations of services and companies. Linda this appears to be one of those cases where, there's a category with existing spend and existing customers. So what he just said is interesting. And I want to get your thoughts because these are these points of these new areas where AppDynamics can potentially help enterprises. What are some of the areas that you see AppDynamics helping enterprises in their cloud adoption journey 'cause they want some cloud native we see Hybrid and all the announcements, Outpost, now Edge it's a distributed computer. You need to have software at every piece of the puzzle. So what's your, what areas can you share specifically? >> Absolutely and so, like Dave was just saying it's, as these organizations start to make these major cloud migrations, one, their applications are getting actually significantly more complex than they've ever been. And they're now spanning a much broader ecosystem than they've ever spanned before. So that the kind of coverage that IT organizations and DevOps needs to cover not only is seeing this explosion of data but it's also now spanning areas of control that some of these folks have never had to think about before. And so the value of AppDynamics is our ability to be able to ingest data from your cloud native applications your traditional applications, all different sources of domain data that you want to get including things like security data. So we can start to correlate that in a meaningful way and then tie that back to business insights. And so the way that AppDynamics is actually bringing value to the table is not only helping our customers get visibility across the entire stack, but actually only surfacing the most meaningful insights to help them act on that those performance issues that they might see and more meaningfully manage their businesses. >> Linda I think you guys are onto something really big not just on the wave and just the positioning but one of the trends that we're reporting and we're going to be teasing out all week three weeks here is automation is great but that's just baseline. Everything is a service really speaks to some of the things that you guys have to put in place 'cause the mandate is everything should be a service. Now, I mean, I'm overgeneralizing but that's generally the ivory tower C suite message. Make it as a service cloud scale is beautiful, but then you when you pass it down to the teams, that's like that's not easy boss. It's not easy to do. That's really kind of what you're getting at here. It's not just automation and DevOps. It's the business model. >> Absolutely it's the intelligence it's once you create thousands and thousands of services, how do you manage them effectively and know what matters and what doesn't? >> Dave your final word here on on this point is when you think about that if you believe that to be true, then I'm just going to be downloading services whenever I need them. So it's almost like quasi self service managed services kind of coming together in real time or with my off base there. What's your take on that? >> No, we're actually working together with that dynamic and so all these kinds of things. So as we proliferate services, John and, AWS has got over 175 services and application is made up of many components. So how do you actually correlate an associate all the resources that make up that application? And if you think about dynamics name is the application and dynamics what's going on with the application. So we actually just launched today service catalog application registry, which is a new API surface for the AWS service catalog that allows you to define NGS on all the AWS resources from a cloud formation stack set all the way down into an easy to instance and associate that's an application known. And so the higher level of abstraction is what we talked about is management of the application. And what customers want to do, CIO's want to manage the application all the resources associated through the application whether the application is running well, is it secure? Is it on budget? Whether it's actually running? So application management is kind of where people are going even though their application is made up of dozens of associated services. So this is the next frontier. >> Well you guys are just great to have on world-class partnership two leaders, AppDynamics, story history they continue to do well. And even now with the world going on, Dave congratulations on your success. Final question for both of you is, where's the partnership go from here? I think it's a great success story. What's in the store for the future? >> Linda. >> Yeah to the moon. It's look AWS is an amazing partner. And Dave is a great guy to work with and where we are going is to help our customers build world-class applications and be able to manage them and modernize those effectively. And there's no way we could do that without partners at AWS. So it's a, there's a long-term relationship here. >> Well, congratulations, Linda Tong general manager AppDynamics. Thanks for coming on, and virtually at least we'll see you on the Interwebs during the next couple of weeks here, Virtual re:Invent Dave McCann. Of course, we'll see you again and great to watch you continue to grow. Is there any new title is going to add to your thing marketplace now it's migration, control services come on. >> With innovation culture we keep innovating. >> Great to have you guys on. Thanks for, thanks for sharing, appreciate it. >> John, Linda thank you very much. >> Thanks. >> Thanks for that great insight. Really appreciate it. I'm John from theCUBE you're watching coverage of re:Invent 2020. This is theCUBE virtual. (upbeat music)
SUMMARY :
Narrator: From around the globe, Welcome to theCUBE. in the hands of people to as they migrate to the cloud. pickup in the marketplace And companies around the world of AWS to the market? as they start to lead about some of the things you put And a lot of the large software Linda this appears to be So that the kind of coverage of the things that you going to be downloading about is management of the application. story history they continue to do well. And Dave is a great guy to work with and great to watch you continue to grow. we keep innovating. Great to have you guys on. Thanks for that great insight.
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Wayne Duso, Amazon Web Services | AWS Storage Day 2019
>>This is >>Dave Volante, and welcome to Storage Day. We're here at Amazon and Boston and you're watching the Cube. Wayne do so is here. He's the general manager of a lot of stuff. File hybrid edge transfer and data protection Service is at Amazon. Web service is good to see you, Wayne. Thanks. >>Good to see you. >>So let's talk about that. That's a pretty vast portfolio that you have explained that to our audience. >>Sure thinks so. The portfolio that I'm responsible for covers a vast swath of our stories portfolio on AWS. So in that we cover all of our files. Service's s Oh, that's E f s and FSX. Our data transport service is which includes data sync, transfer for sftp and our snowball or snow service's. And then also hybrid edge, which includes our snowball, compute and our stories, Gateway Service's and then data protection, which includes a W s back up. >>Wow. Okay, great. Congratulations on that portfolio. And, you know, I said I said earlier on it started with s3, and it's just exploded. Now all the service is this is part of what we sometimes call tongue and cheek cloud to 0.0, there's more work loads, more capabilities, more granularity. But talk about some of the big picture macro trends that you guys see in the marketplace. Specific Thio Sort of your area. >>Yes. So, uh, actually, it's so many, uh, think you said things are expanding. Things are accelerating in our space. One of things I like thio talk about with respect to our portfolio is we have storage service is dated. Transport service is to match the needs of your workloads and your applications. So all of these service is a purpose built for the type of storage that you need, the programming model that you need for your applications and workloads. So whether it's object storage with s3 and glacier or block storage with BBS or most recently, file service with F s and F S X file service is so you have the tools at your disposal. It'll that you need based on your on your application. Workloads. >>Talk more about the programming model. What? How do you envision that? What do you What do you mean? What's your mental model of the different >>process? You're so forever. People have been programming based on, you know, whether it's performance or or some scale of some sort. Um, you know, uh, databases traditionally used block storage because they don't need a lot of logic between them and the storage medium itself. File storage is been used for 50 years and has a very specific program model that exist in every operating system in every programming language. You know, whether it's an open, ah, read right, see close. It's a common paradigm that is used all over the place and that capability in the performance that you need to satisfy those applications and workloads very specific. And so for for aws, we provide those final systems for for Lennox, if you would with F House Windows, which is ever sex for Windows and for very high performance computing on luster. We've had an amazing storage platform, which is s3 and S three forms the basis for a lot of our customers data lakes on and basically storage data repositories, for which there are many integrations. With that, there are other >>sword service's. I often joke that, you know, if your expertise is is unpacking boxes plugging in setting up storage arrays, managing London's you, you might want to think about updating. You know your skill sets right, But so that's another big mega trend that we certainly see is people just don't see a lot of value in planning and managing and migrating over six month periods. Storage a raise. It's It's something that really doesn't have a lot of value to the business. So you guys have announced all these service is over the years and you've got some new announcements as well, that kind of play into some of the trends that we've been talking about. Talk about the news. >>Yes, that the news is pretty rich. Uh, for this season, let's let's start off with FSX eso FSX is our service for bringing fully manage third party or open source file systems, um, to our customers. And so Fsx Windows, as example, was launched last year, reinvent and has been rolling out the whole Siri's of features throughout the year, and we have a nice set of features coming out this year. So, as example today, effort, Sex Windows is a single ese service. We are rolling out multi easy capability, >>okay? And you you Sometimes you guys make the point that the beauty is there's no change required in APS, and we talked earlier about the program. We'll talk a little bit more about that. Why is that important to customers, >>you know and all index on FXX windows for another minute. A lot of abs been written to use the semantics of a particular file system in case of Windows will say NT f s and their written for that specific file system. We've provided customers with the capability of bringing those applications to AWS without any wary of compatibility. It's a pure lift and shift model. S O makes it really easy for them to bring their workloads. They should bring their workload so they don't have to deal with some of things you brought up early around provisioning buying systems, having to worry about saying that, planning for all of that. We take all of that work away from them and they get full compatibility based on what they need today and with some of the additional capabilities we're bringing to bear with the integrations in the ecosystem and heat up US ecosystem, they'll be able to appreciate those as well. >>Let's talk a little bit about more about that because you're basically, I'm inferring you're saying, Hey, this compelling reasons why you should move into the cloud. For instance, File Service's into the cloud. What's the difference between my on Prem? Isn't just on Prem Nass stuffing it into the cloud? Or is it more than you touched on integration? So convince me, why should I move? >>It's so much more than that. So if we if we look at the basic infrastructure once you literally click three or four buttons, Thio started files and creative file system, you no longer have to worry about it ever again. So the things that you have done on Prem, you no longer have to worry about having a sword administrator or having to provision in by storage and maintain it. We take care of all that would take care of all the security elements. I'm so important to your data to make sure that's in a in a secure environment. Security. It's job number one for us. So all of these capabilities and the ability to stand it up to never have to manage it never adorable security. We take care of all the capabilities like you should really be bringing those workloads onto a platform like this so that you can spend your time on added value. Um, service is our applications for your >>business, while in the integration is also a key piece of it. I mean, you know, for years, customers and customers still sometimes want to roll their own. You know, they like to have the you know, the knobs and turn them. But but many customers that we talked or saying Listen, it's too expensive. I don't want to be a systems integrator anymore in the cloud. How can they take advantage of those? Like sometimes they call it the flywheel effect. But the other innovations that you're bringing, whether it's machine learning or other service, is that you guys are bringing in. Is that how tight is that? Integration. >>So those integrations are ongoing, and they're there forever. It goes back to what I said a minute around over a three year period. All of these capabilities gonna be delivered to them, if you would at this at the same cost as the basic service. So let's talk about what happened this year. Um ah, lot of our customers are using sage maker for their M. L A I capabilities and sage maker is deeply integrated with both fsx luster and uh, E F s so that customers again don't have to worry about stories. They're not the way about sharing that are scaling. It's all there for them. >>You mentioned. Also you responsible for the snow product convention an edge. I was what it was to me. It was your first move, so the hybrid, I'll call it. But I always joke that, but it's true. The fastest way to get data from Point A to point B is a Chevy truck, and so, but you're referring to a sort of an edge play. You talk a little bit more about that, help us understand it. >>Sure, so Snowball, a service launched about five years ago. We initially launched a service as a bulk data migration service, and it's it's been that service for roughly four years. About a year ago, a little over a year ago, we started introducing thehe bility to have compute as part of that device, and the reason for that was customers were telling us as we're moving the data, we would like to be able to do some pre processing before it makes it onto AWS before it goes into history, is example. So we started providing that capability that ended up expanding into a full blown if you would cloud platform on a device that could be run in disconnected environments or stare environments. So with Snowball today of the ability to have easy two instances CBS storage s3 storage all in one device. And so that's a really powerful construct because you can build your applications on AWS using the same service is prove out if you wouldn't Dev UPS model that there what you need to be and then literally lift them onto, ah, snowball device and have those executing in the field as if they were running directly in the cloud. >>Change the subject a little bit when I look at the logo slide of all your customers, a lot of big names on their their global companies, a lot of things. So I run a cloud and they got a data center. You know he's Boston or something. No offense if you have a data center, East Boston, but regions are critical, um, especially for global scale. Cloud brings global scale, but it's also important to have data approximate to the users. So you're reducing late and see there's availability and redundancy aspects. Talk about your philosophy around regions and how it fits into your portfolio. How do you take advantage of all that capability? >>So a lot of our customers have global presence and the ability for them to have their application to have their business function in the regions that they're doing business and have those little agencies and also the availability model of being in multiple places. Case of disasters super important. Um, are regions are built, have at minimum three availability zones and an availability zone. You could think of boat as, ah, data center. So, for example, with the F. S. When you stand up a file system with the F S, your file system is automatically distributed, replicated across all three availability zones within that region. But as the user, you don't worry about any of that. We take care of it all for you. In the unfortunate event that our availability zone is made unavailable, your data is still fine. You still have access to that data all time? >>Yeah, and your customers, I think increasingly understanding this the beginning toe architect around regions and availability zones. It's a different way of thinking, but it's in some respects sort of the modern way of thinking. >>If you if you if you go back a few years and you think about all of the disaster recovery or business continue in software and capabilities that had been created, we're providing all of those capabilities today in our regional construct. >>Yeah, well, you know this. I mean, you both better have been around for a while, and we've seen the unnatural acts that you had to do to sort of create that level of redundancy and business continuance. And it was extremely expensive, complex and really risky to test. So I'll, uh, I'll leave you with the last word. Any other thoughts that you want to share with our audience? We're >>We're We're just first off. Thank you for giving you the time. Today. We're really excited about what we're doing with each of these. Service is we're very excited about the portfolio overall on the value that it's going to bring, and he's bringing to our customers today. We're excited about all the announcements. >>Yeah, we'll say we're seeing a lot of innovation. Expansion of the Amazon portfolio. Optionality, granularity performance, horses for courses, the right tool for the right job way. Thanks so much for coming to >>my pleasure. Thank you. >>You're welcome. All right. Keep it right to everybody. You watching the cube storage day from Amazon in Boston? Right back.
SUMMARY :
He's the general manager of a lot of stuff. That's a pretty vast portfolio that you have explained that to our audience. So in that we cover all of our files. And, you know, I said I said earlier on it started with s3, and it's just exploded. the programming model that you need for your applications and workloads. What do you What do you mean? that you need to satisfy those applications and workloads very specific. I often joke that, you know, if your expertise is is unpacking boxes Yes, that the news is pretty rich. And you you Sometimes you guys make the point that the you know and all index on FXX windows for another minute. Hey, this compelling reasons why you should move into the cloud. So the things that you have done on Prem, you no You know, they like to have the you know, the knobs and turn them. All of these capabilities gonna be delivered to them, if you would Also you responsible for the snow product convention an edge. you can build your applications on AWS using the same service is prove How do you take advantage of all that capability? So a lot of our customers have global presence and the ability for them to but it's in some respects sort of the modern way of thinking. If you if you if you go back a few years and you think about all of the disaster recovery or business continue in acts that you had to do to sort of create that level of redundancy and business continuance. Thank you for giving you the time. Expansion of the Amazon portfolio. Thank you. Keep it right to everybody.
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Duncan Lennox, Amazon Web Services | AWS Storage Day 2019
[Music] hi everybody this is David on tape with the Cuban welcome to Boston we're covering storage here at Amazon storage day and we're looking at all the innovations and the expansion of Amazon's pretty vast storage portfolio Duncan Lennox is here is the director of product management for Amazon DFS Duncan good to see it's great to be here so what is so EF s stands for elastic file system what is Amazon EFS that's right EFS is our NFS based filesystem service designed to make it super easy for customers to get up and running with the file system in the cloud so should we think of this as kind of on-prem file services just stuck into the cloud or is it more than that it's more than that but it's definitely designed to enable that we wanted to make it really easy for customers to take the on pram applications that they have today that depend on a file system and move those into the cloud when you look at the macro trends particularly as it relates to file services what are you seeing what a customer's telling you well the first thing that we see is that it's still very early in the move to the cloud the vast majority of workloads are still running on Prem and customers need easy ways to move those thousands of applications they might have into the cloud without having to necessarily rewrite them to take advantage of cloud native services and that's a key thing that we built EFS for to make it easy to just pick up the application and drop it into the cloud without the application even needing to know that it's now running in the cloud okay so that's transparent to the to the to the application and the workload and it absolutely is we built it deliberately using NFS so that the application wouldn't even need to know that it's now running in the cloud and we also built it to be elastic and simple for the same reason so customers don't have to worry about provisioning the storage they need it just works NFS is hard making making NFS simple and elastic is not a trivial engineering task is it it hadn't been done until we did it a lot of people said it couldn't be done how could you make something that truly was elastic in the cloud but still support that NFS but we've been able to do that for tens of thousands of customers successfully and and what's the real challenge there is it to maintain that performance and the recoverability from a technical standpoint an engineering standpoint what's yes sir it's all of the above people expect a certain level of performance whether that's latency throughput and I ops that their application is dependent on but they also want to be able to take advantage of that pay-as-you-go cloud model that AWS created back with s3 13 years ago so that elasticity that we offer to customers means they don't have to worry about capex they don't have to plan for exactly how much storage they need to provision the file system grows and shrinks as they add and remove data they pay only for what they're using and we handle all the heavy lifting for them to make that happen this this opens up a huge new set of workloads for your customers doesn't it it absolutely does and a big part of what we see is customers wanting to go on that journey through the cloud so initially there starting with lifting and shifting those applications as we talked about it but as they mature they want to be able to take advantage of newer technologies like containerization and ultimately even service all right let's talk about EFS ia infrequently access files is really what it's designed for tell us more about it right so one of the things that we heard a lot from our customers of course is can you make it cheaper we love it but we'd like to use more of it and what we discovered is that we could develop this infrequent access storage class and how it works is you turn on a capability we call lifecycle management and it's completely automated after that so we know from industry analysts and from talking to customers that the majority of data perhaps as much as 80% goes pretty cold after about a month and it's rarely touched again so we developed the infrequent access storage class to take advantage of that so once you enable it which is a single click in the console or one API call you pick a policy 14 days 30 days and we monitor the readwrite IO to every file individually and once a file hasn't been read from or written to in that policy period say 30 days we automatically and transparently move it to the infrequent access storage class which is 92% cheaper than our standard storage class it's only two and a half cents in our u.s. East one region as opposed to 30 cents for our standard storage class two and a half cents per per gigabyte per gigabyte month we've done about four customers that were particularly excited about is that it remains active file system data so we move your files to the infrequent access storage class but it does not appear to move in the file system so for your applications and your users it's the same file in the same directory so they don't even need to be aware of the fact that it's now on the infrequent access storage class you just get a bill that's 92 percent cheaper for storage for that file like that ok and it's and it's simple to set up you said it's one click and then I set my policy and I can go back and change my that's exactly right we have multiple policies available you can change it later you can turn off lifecycle management if you decide you no longer need it later so how do you see customers taking advantage of this what do you expect the adoption to be like and what are you hearing from them well what we heard from customers was that they like to keep larger workloads in their file systems but because the data tends to go cold and isn't frequently accessed it didn't make economic sense to say to keep large amounts of data in our standard storage class but there's advantages to them in their businesses for example we've got customers who are doing genomic sequencing and for them to have a larger set of data always available to their applications but not costing them as much as it was allows them to get more results faster as one example you obviously see that yeah what we're what we're trying to do all the time is help our customers be able to focus less on the infrastructure and the heavy lifting and more on being able to innovate faster for their customer so Duncan Duncan some of the sort of fundamental capabilities of EFS include high availability and durability tell us more about that yeah when we were developing EFS we heard a lot from customers that they really wanted higher levels of durability and availability than they typically been able to have on Prem it's super expensive and complex to build high availability and high durability solutions so we've baked that in as a standard part of EFS so when a file is written to an EFS file system and that acknowledgement is received back by the client at that point the data is already spread across three availability zones for both availability and durability what that means is not only are you extremely unlikely to ever lose any data if one of those AZ's goes down or becomes unavailable for some reason to your application you continue to have full read/write access to your file system from the other two available zones so traditionally this would be a very expensive proposition it was sort of on Prem and multiple data centers maybe talk about how it's different in the clouds yeah it's complex to build there's a lot of moving parts involved in it because in our case with three availability zones you were talking about three physically distinct data centers high-speed networking between those and actually moving the data so that it's written not just to one but to all three and we handled that all transparently under the hood in EFS it's all included in our standard storage to your cost as well so it's not something that customers have to worry about more either a complexity or a cost point of view it's so so very very I guess low RPO and an RTO and my essentially zero if you will between the three availability zones because once your client gets that acknowledgement back it's already durably written to the three availability zones all right we'll give you last word just in the world of file services what should we be paying attention to what kinds of things are you really trying to achieve I think it's helping people do more for less faster so there's always more we can do and helping them take advantage of all the services AWS has to offer spoken like a true Amazonian Duncan thanks so much for coming on the queue for thank you good all right and thank you for watching everybody be back from storage day in Boston you watching the cute
SUMMARY :
adoption to be like and what are you
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Edward Naim, Amazon Web Services | AWS Storage Day 2019
>>We're back in storage day at Amazon in Boston, on detente with the Cube. Ed name is here. He's the general manager of X ed. Welcome to the Cube. Good to say thanks for having me, Dave. Okay, So explain to me why you guys launched FSX for Windows File server. You know why now? >>Well, we did it because customers asked us to do it. What customers told us was that they were tired of all of the effort and overhead in managing windows, file systems and Windows file servers on their own, everything from routine maintenance, like patching to provisioning. They just didn't wanna have to do all of that heavy lifting. So they asked us for a simple solution. Fully manage solution on the cloud. There's a lot of windows data out there. A lot of data that's access from windows computers. Eight of us is the cloud that has the most windows workloads running on it. So it was a very natural ask for customers to ask us as they're moving their windows workloads onto eight of us to have a file system that's fully managed for them that could be accessed by those workloads. So it was It was actually very natural and, uh, unexpected. Ask from customers, >>you know, love. You may not know that, but it does kind of make sense. Is so much windows out there You're the cloud leader. So peanut butter and jelly. Um, how do you see customers using FSX for Windows? >>Yeah, What's really exciting is they're using it for a really broad spectrum of workloads eso everything from traditional user shares and home directories, Thio development environments to analytics, workloads, tau video, trance coating. So it's a very wide spectrum of workloads that they're on the service and we're continuing to see new new types of workloads every day, which is really exciting. >>So we're hearing stories. What exactly is new around FSX for Windows file service Specifically. >>Yeah, Well, we've launched a number of capabilities this year throughout the year s 01 of the significant ones that we launched was the ability for customers to use their self managed active directories on dhe join their FSX file systems to those. So we now have two options. Customers can use a fully managed AWS fully managed active directory or their own with FSX. We launched a number of capabilities around access from on premises. For example, customers can now access. Or when we launched it, we announced that they could now access their file systems over direct connect connections over VPN so they can access the Windows file systems from computers and from end users that are running on premises. So quite a few announcements this year Those are just two examples, and we're really excited about really a slew of announcements and feature features that we're launching now. And I can get into those if you like, give me some examples of your work. So one of the, uh, the most common questions we've had from customers is. Can we offer a native multi daisy capability, multi availabilities own capability? So a lot of customers are running enterprise grade workloads on FXX, and they want to move more and more of those workloads onto AWS, and they don't wanna have to, ah, manage the overhead of using something like a distributed file system or D F s replication between fsx file systems and different disease. So we're launching a fully managed, super simple, multi easy capability, and that's a deployment options that custom the customers will have in addition to what we already had, which was the single easy deployment options. >>Let me see some recurring themes when you talkto folks at Amazon announced service is it's the it's the same sort of mantra. Be able to reduce that heavy lifting, shift your focus to things that will add more value to your business. Take advantage of these other service is through these integrations that that we're doing. So I mean, it kind of feels like a no brainer, but I give you the last the last word. I mean, is it Why is it why should customers, you know, sell me on why I should move by dated to the cloud? >>Yeah. I mean, we we like to think of it as a no brainer because we are fully managing everything for the customer. Um, the the service is built on top of Windows server, so provides a fully compatible Windows file system, and we've managed that fully four customers, So you get complete compatibility with us and be complete compatibility with Auntie if s file system semantics and features. So it's a very simple move for customers to move their existing workloads onto the service and have it before we managed a couple of the other features that we're launching that I do want to mention our We're launching data D duplication we're launching. Ah, whole bunch of administrative capabilities, like user quotas were extending. Our administrative CIA lied to do things like a lot of customers to create shares programmatically so really a very exciting set of capabilities that we really think make this a ah no brainer for customers. >>Well, that's another recurring themes. You guys, you know, you dropped prices and look at the moors losses. Prices continue to drop. The differences them is on. You make it transparent on DDE. If I use a service is lower cost, my bill goes down and then, of course, I end up using more because this is an elastic world. So that's a good thing. But, Ed, thanks so much for coming on. The key. Thank you. Share is that any other thing is you guys only window specialists. It's just kind of ironic, you know, leader in windows. And, uh, >>well, it really comes from What are our customers are asking us for? So they see moving their windows. Workloads is the first step to the full modernization and being all in on the cloud. >>Great. We'll get exit. Thank you. All right. And thank you for watching everybody right back after this. Short break, Dave. A lot with the Cube.
SUMMARY :
Okay, So explain to me why you guys launched FSX for Windows File server. So it was a very natural ask for customers to ask us as they're moving their windows workloads onto eight of us So peanut butter and jelly. So it's a very wide spectrum of workloads that they're on the service and we're continuing So we're hearing stories. So a lot of customers are running enterprise So I mean, it kind of feels like a no brainer, a couple of the other features that we're launching that I do want to mention our We're It's just kind of ironic, you know, leader in windows. Workloads is the first step to the full modernization and being all in on the cloud. And thank you for watching everybody right back after this.
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Asa Kalavade, Amazon Web Services | AWS Storage Day 2019
(upbeat music) >> Hi, everybody, we're back. This is Dave Vellante with theCUBE. We're here talking storage at Amazon in Boston. Asa Kalavade's here, she's the general manager for Hybrid and Data Transfer services. >> Let me give you a perspective of how these services come together. We have DataSync, Storage Gateway, and Transfer. As a set of Hybrid and Data Transfer services. The problem that we're trying to address for customers is how to connect their on premises infrastructure to the cloud. And we have customers at different stages of their journey to the cloud. Some are just starting out to use the cloud, some are migrating, and others have migrated, but they still need access to the cloud from on-prem. So the broad charter for these services is to enable customers to use AWS Storage from on-premises. So for example, DataStorage Gateway today is used by customers to get unlimited access to cloud storage from on-premises. And they can do that with low latency, so they can run their on-prem workloads, but still leverage storage in the cloud. In addition to that, we have DataSync, which we launched at re:Invent last year, in 2018. And DataSync essentially is designed to help customers move a lot of their on-premises storage to the cloud, and back and forth for workloads that involve replication, migration, or ongoing data transfers. So together, Gateway and DataSync help solve the access and transfer problem for customers. >> Let's double down on the benefits. You started the segment just sort of describing the problem that you're solving, connecting on-prem to cloud, sort of helping create these hybrid environments. So that's really the other benefit for customers, really simplifying that sort of hybrid approach, giving them high performance confidence that it actually worked. >> Maybe talk a little bit more about that. >> So with DataSync, we see two broad use cases. There is a class of customers that have adopted DataSync for migration. So we have customers like Autodesk who've migrated hundreds of terabytes from their on-premises storage to AWS. And that has allowed them to shut down their data center, or retire their existing storage, because they're on their journey to the cloud. The other class of use cases is customers that have ongoing data that they need to move to the cloud for a workload. So it could be data from video cameras, or gene sequencers that they need to move to a data pipeline in the cloud, and they can do further processing there. And in some cases, bring the results back. So that's the second continuous data transfer use case, that DataSync allows customers to address. >> You're also talking today, about Storage Gateway high availability version of Storage Gateway. What's behind that? >> Storage Gateway today is used by customers to get access to data in the cloud, from on-premises. So if we continue this migration story that I mentioned with DataSync, now you have a customer that has moved a large amount of data to the cloud. They can now access that same data from on-premises for latency reasons, or if they need to distribute data across organizations and so on. So that's where the Gateway comes into play. Today we have 10's of thousands of customers that are using Gateway to do their back-ups, do archiving, or in some cases, use it as a target to replace their on-premises storage, with cloud backed storage. So a lot of these customers are running business critical applications today. But then some of our customers have told us they want to do additional workloads that are uninterruptible. So they can not tolerate downtime. So with that requirement in mind, we are launching this new capability around high availability. And we're quite excited, because that's solving, yet allowing us to do even more workloads on the Gateway. This announcement will allow customers to have a highly available Gateway, in a VMware environment. With that, their workloads can continue running, even if one of the Gateways goes down, if they have a hardware failure, a networking event, or software error such as the file shares becoming unavailable. The Gateway automatically restarts, so the workloads remain uninterrupted. >> So talk a little bit more about how it works, just in terms of anything customers have to do, any prerequisites they have. How does it all fit? >> Customers can essentially use this in their VMware H.A. environment today. So they would deploy their Gateway much like they do today. They can download the Gateway from the AWS console. If they have an existing Gateway, the software gets updated so they can take advantage of the high availability feature as well. The Gateway integrates into the VMware H.A. environment. It builds up a number of health checks, so we keep monitoring for the application up-time, network up-time, and so on. And if there is an event, the health check gets communicated back to VMware, and the Gateway gets restarted within, in most typical cases, under 60 seconds. >> So customers that are VMware customers, can take advantage of this, and to them, it's very non disruptive it sounds like. That's one of the benefits. But maybe talk about some of the other benefits. >> We saw a large number of our on-premises customers, especially in the enterprise environments, use VMware today. And they're using VMware HA for a number of their other applications. So we wanted to plug into that environment so the Gateway is as well highly available. So all their applications just work in that same framework. And then along with high availability, we're also introducing two additional capabilities. One is real time reports and visibility into the Gateway's resource consumption. So customers can now see embedded cloud watch graphs on how is their storage being consumed, what's their cache utilization, what's the network utilization. And then the administrators can use that to, in fairly real time, adapt the resources that they've allocated to the Gateway. So with that, as their workloads change, they can continue to adapt their Gateway resources, so they're getting the maximum performance out of the Gateway. >> So if they see a performance problem, and it's a high priority, they can put more resources on it-- >> They can attach more storage to it, or move it to a higher resourced VM, and they can continue to get the performance they need. Previously they could still do that, but they had to have manual checks. Now this is all automated, we can get this in a single pane of control. And they can use the AWS console today, like they do for their in cloud workloads. They can use that to look at performance of their on-premises Gateway's as well. So it's one pane of control. They can get CloudWatch health reports on their infrastructure on-prem. >> And if course it's cloud, so I can assume this is a service, I pay for it when I used it, I don't have to install any infrastructure, right? >> So the Gateways, again, consumption based, much like all AWS services. You download the Gateway, it doesn't cost you anything. And we charge one cent per gigabyte of data transfer through the Gateway, and it's capped at $125 a month. And you just pay for whatever storage is consumed by the Gateway. >> When you talk to senior exec's like Andy Jassy, always says "We focus on the customers." And sometimes people roll their eyes, but it's true. This is a hybrid world. Years ago, you didn't really hear much talk about hybrid. You talked to your customers and say, "Hey, we want to connect our on-prem to the public cloud." You're bringing services to do that. Asa, thanks so much for coming to theCUBE. Appreciate it. >> Thank you, thanks for your time. >> You're welcome. And thank you for watching everybody. This is Dave Vellante with theCUBE. We'll be back right after this short break. (upbeat music)
SUMMARY :
Asa Kalavade's here, she's the general manager for but they still need access to the cloud from on-prem. So that's really the other benefit for customers, or gene sequencers that they need to move to You're also talking today, about Storage Gateway for latency reasons, or if they need to distribute just in terms of anything customers have to do, So they would deploy their Gateway So customers that are VMware customers, they can continue to adapt their Gateway resources, and they can continue to get the performance they need. So the Gateways, again, consumption based, You talked to your customers and say, This is Dave Vellante with theCUBE.
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Kevin Miller, Amazon Web Services | AWS Storage Day 2019
>>day, Volonte. And welcome to the Cuban special presentation here from Amazon in Boston. We're talking storage, really? A group of intelligent people here in the storage world and really excited to have Kevin Miller. You got hard news today around this. Think of replication. Time >>control. Yeah. >>What? What's that all about? What should we know about S3 replication? What problems isn't solving for customers? Why'd you do this? >>Yeah, absolutely. So we're very >>pleased to announce the launch today of s three replication Time control. >>This is a >>future that a number of customers across really across the board, large enterprise as well as public sector customers have asked us >>for to really give >>them insight and confidence that critical data they need to have replicated will be done in the time frames that they require. So we're actually today offering industry first s L. A of 99.9% of data will be replicated with within 15 minutes when using replication, time control and really, most data is replicated within a matter of seconds. But then having that escalate to >>back up that promise. So >>we have a number of customers who >>use as three replication today. Both is in our cross region replication as well as same region replication. And so the >>use cases really >>span the gamut from customers were looking to just back up their data so they might make a copy into a lower cost storage class to have a backup of that data. A CZ well as customers that I want to have on always on disaster recovery site, where they can replicate the data and then have a live hot, ready to go replication in another region for disaster >>recovery. Okay, so let's double click on that a little bit. Cross region replication. C r R >>r r >>Tell us more about that. What should we know there? >>Well, see, you're ours. The >>capability we've had for a long time, and it's it's a really critical capability. Ah, building block that our customers used to ensure that they can maintain a second copy of the data in another region. And so, and with Sierra are they can not only replicate the data, but they can actually replicate it into a completely different accounts so they can actually have two accounts that with potentially different access control and different administrators who can access those accounts. So they really have confidence that even if there was a knish you with their application in one region, that they can immediately begin operating in that second region. And so so we have customers who use replication for backup recovery, but also for a ZAY said sort of live replication to have ah always on D our site. >>Okay. And you also just recently announced the same reason. Region replication tell us more about that. >>Well, same region replication. It provides many of the benefits of cross region replication, but does so within one region. So we do have some customers >>who would like to, for example, make a backup copy of their data into a different account. But they >>need to >>maintain that data within the same geography, but perhaps for data, sovereignty reasons, or that they just want Thio, keep everything in one region but still have that second copy. So with same region replication, it's really just one parameter in the replication configuration, and they have all the benefits that we have historically had with across region application. >>So, Kevin, what should we be watching for? Just in terms of s three replication. Replication generally is very important for customers, really. But what's next for Amazon as three replication that we should be paying attention to? >>Well, you know, we, uh we think the >>replication today has ah range of different differentiated capabilities in terms of the ability to replicate on a tag level or replicate subset of the data. And so, you know, >>really, our goal with replication is just to make it as easy as >>possible for customers. Thio configure the replication they need Andi provide that flexibility while also providing the sort of the fully managed experience that we have with us three where you don't have to build your own software to do it. So, >>you know, we're gonna be continuing >>to work with customers. Uh, Thio simplify the things that they need to d'oh to configure replication for their different use cases. >>Let's talk about that customer angle you're just thinking about as three replication time control. What you expect customers to be saying about this, how they're going to be using it. What kind of problems are they going to be solved? >>Yeah, well, we have customers, you know, particularly those >>in regulated industries or in government public sector where they are under very stringent requirements. Thio be able to prove that they always have a second copy of the data and and this is the way that they can do that. So we air, you know, working with customers in with some of the tightest regulations you can imagine who were saying, Yeah, this is what I need with this capability Now I can I can watch it. I can monitor it. Ah, and I and more importantly, I know that the data is there for them. They can't start processing the data until they know that that second copy is is made. So they're using the replication time control metrics to really look at it real time and say, Okay, I'm ready to begin processing this data because I know I have both copies made. >>Well, it's great to see you guys really expanding the storage portfolio again. It started very simple, but you get that flywheel effect going. It's it's a critical part of the value chain. So congratulations. I'll give you last word, and >>I I just think that >>obviously that s three stands for simple storage service. And despite all of the flexibility and capability we're trying to build in. At the same time, simplicity is job number one for us. And so >>we're just >>really excited about with replication. Time control. Uh, we think that we've built something that both hits, that that mark of being simple but also provides just a lot of capability that that otherwise, you know, would take quite a bit of effort. >>Always a balance. Right? The simple you make it that war customers wants. Kevin, thanks so much for coming on The Cube. Really appreciate it. >>Absolutely. Thanks for having me. >>You're welcome. And thank you for watching everybody, right? Right back, Right after this short break.
SUMMARY :
people here in the storage world and really excited to have Kevin Miller. control. So we're very them insight and confidence that critical data they need to have replicated will be done So And so the to have on always on disaster recovery site, where they can replicate the data Okay, so let's double click on that a little bit. What should we know there? Well, see, you're ours. And so so we have customers who use replication tell us more about that. So we do have some customers But they and they have all the benefits that we have historically had with across region application. Just in terms of s three replication. in terms of the ability to replicate on a tag level or replicate subset that we have with us three where you don't have to build your own software to do it. things that they need to d'oh to configure replication for their different use cases. What kind of problems are they going to be solved? of the tightest regulations you can imagine who were saying, Yeah, this is what I need with this capability Well, it's great to see you guys really expanding the storage portfolio again. And despite all of the flexibility and capability that otherwise, you know, would take quite a bit of effort. The simple you make it that war customers wants. Thanks for having me. And thank you for watching everybody, right?
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Ashish Palekar, Amazon Web Services | AWS Storage Day 2019
>>This is Dave Violante. We're here at a W s with the Keep talking About Storage palate cars. Here is the director of product management for E B s Elastic block storage. Welcome. Good to see again. >>Nice to see it. If >>so, let's talk about E b s. You know, it all started with us. Three and course customers demand Maur. What do we need to know about E b s? Like, what are the options that you provide? Give us the late low down. >>Yeah. So the way to think about block storage in the AWS eight abreast constructors. Really two kinds of offerings. One is around instant storage, which is a form of block strategy. And then you have a block started service, which is E. B s Andi. Sort of. The key thing they're from customer standpoint of different shit between the two is if you warn your storage like cycle to be coincident with your instance like cycle, then you use instant surgeon. That's why we see a lot of our customers using since storage, because they won't want that experience if you want. On the other hand, it's storage life cycle that's different from your instance life cycle. So the ability to change instances, the ability to grow size is the ability to to take back ups. Then you want to choose the obvious experience. And there we have a series of volume types that customers can consume. Be a GP two we have, I want. We have our stream volumes, which are a C one and C one. >>So she's when you talk to customers of block stores. What did they tell you that they most care about? >>Yeah, uh, it is. It is a Lord around performance. It is a lot around. Availability is a lot on your ability. He's a fuse. Those of the core characteristics that that customers care about earlier this year as an example, one of the things that we launched for customers was the ability to encrypt their volumes by default on you. Say, Well, why is that important? So security becomes a big concern for customers a day as they think about their environment and with encryption by default. We just made it simple. With a single setting, you can now, at an account level, ensure that all your PBS volumes created from that point on our fully encrypted. >>Okay, let's talk about snapshots. So how o r r. Snapshots in the cloud? Different. And how are your customers using stamps? >>Yeah, that's great. Great. Great. Cigarette in tow. Common conversation. Customers who are coming from on premises environment are used to snapshots is being sort of this copy on right type attack volumes. The way to think about aws snapshot. Devious snapshots in particular are really to think of them as backup. And so that is the one sort of key thing that I always tell customers is to think of what we call snapshots, really as backups. Especially if you're coming from a non premises environment. >>Okay, um, how about things you're doing to really improve? Uh, EBS snapshots. I mean, is it more performance? Is it making simple Are expanding use cases. Yeah. >>Yeah. Let's talk about the use case scenario Is that that snapshots get use, and snapshots are really the underlying storage for water called Amazon machine images. Our aim eyes. That is how snaps that is, how our instances boot. That is also the way that customers create CBS Williams from, so you can create an obvious volume from a snapshot. So on that on that particular use case, one of the things that we're we're now launching is a capability via calling far snapshot restored. So you can now take a knee, be a snapshot and then within an availability is soon. Make it such that you can. You can now launch volumes from it without encountering any Leighton sing and back on DDE. That we think is a tremendously powerful capability for customs. Because if you can, it takes away all the undifferentiated heavy lifting that they had to do in order to lure the data from the snapshot into the volume completely out of the picture and allows them to focus on getting their data to their applications. That's right. >>All right, we'll give you the last word. Final thoughts on the innovations that you had. Congratulations on all the hard work. >>No, actually, this is the team has done a tremendous amount of work in art launches. Couldn't be happier to see this in the hands of customers. We look forward to seeing what they build from from the things that we provided them so excited to see that happen. >>That's actually quite amazing. It started all very simple with us three. And now we've seen service is just become more granular. Higher performance. Really meeting customer demands. She's thanks so much. Thank you so much. All right. Thanks for watching. Your body will be back right after this short break.
SUMMARY :
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Bina Khimani, Amazon Web Services | Splunk .conf18
>> Announcer: Live from Orlando, Florida, it's theCUBE, covering .conf2018. Brought to you by Splunk. >> Welcome back to .conf2018 everybody, this is theCUBE the leader in live tech coverage. I'm Dave Vellante with Stu Miniman, wrapping up day one and we're pleased to have Bina Khimani, who's the global head of Partner Ecosystem for the infrastructure segments at AWS. Bina, it's great to see you, thanks for coming on theCUBE. >> Thank you for having me. >> You're very welcome. >> Pleasure to be here. >> It's an awesome show, everybody's talking data, we love data. >> Yes. >> You guys, you know, you're the heart of data and transformation. Talk about your role, what does it mean to be the global head Partner Ecosystems infrastructure segments, a lot going on in your title. >> Yes. >> Dave: You're busy. (laughing) >> So, in the infrastructure segment, we cover dev apps, security, networking as well as cloud migration programs, different types of cloud migration programs, and we got segment leaders who really own the strategy and figure out where are the best opportunities for us to work with the partners as well as partner development managers and solution architects who drive adoption of the strategy. That's the team we have for this segment. >> So everybody wants to work with AWS, with maybe one or two exceptions. And so Splunk, obviously, you guys have gotten together and formed an alliance. I think AWS has blessed a lot of the Splunk technology, vice versa. What's the partnership like, how has it evolved? >> So Splunk has been an excellent partner. We are really joined hands together in many fronts. They are fantastic AWS marketplace partner. We have many integrations of Splunk and AWS services, whether it is Kinesis data, Firehose, or Macy, or WAF. So many services Splunk and AWS really are well integrated together. They work together. In addition, we have joined go to market programs. We have field engagement, we have remand generation campaigns. We join hands together to make sure that our customers, joint customers, are really getting the best value out of it. So speaking of partnership, we recently launched migration program for getting Splunk on prem, Splunk Enterprise customers to Splunk Cloud while, you know, they are on their journey to Cloud anyway. >> Yeah, Bina let's dig into that some, we know AWS loves talking about migrations, we dig into all the databases that are going and we talk at this conference, you know Splunk started out very much on premises but we've talked to lots of users that are using the Cloud and it's always that right. How much do they migrate, how much do they start there? Bring us instead, you know, what led to this and what are the workings of it. >> So what, you know if you look at the common problems people have customers have on prem, they are same problems that customers have with Splunk Enterprise on prem, which is, you know, they are looking for resiliency. Their administrator goes on vacation. They want to keep it up and running all the time. They help people making some changes that shouldn't have been made. They want the experts to run their infrastructure. So Splunk Cloud is run by Splunk which is, you know they are the best at running that. Also, you know I just heard a term called lottery proof. So Splunk Cloud is lottery proof, what that means the funny thing is, that you know, your administrator wins lottery, you're not out of business. (laughs) At the same time if you look at the the time to value. I was talking to a customer last night over dinner and they were saying that if they wanted to get on Splunk Enterprise, for their volume of data that they needed to be ingested in Splunk, it would take them six months to just get the hardware in place. With Splunk Cloud they were running in 15 minutes. So, just the time to value is very important. Other things, you know, you don't need to plan for your peak performance. You can stretch it, you can get all the advantages of scalability, flexibility, security, everything you need. As well as running Splunk Cloud you know you are truly cost optimized. Also Splunk Cloud is built for AWS so it's really cost optimized in terms of infrastructure costs, as well as the Splunk licensing cost. >> Yeah it's funny you mentioned the joke, you know you go to Splunk cloud you're not out of a job, I mean what we've heard, the Splunk admins are in such high demand. Kind of running their instances probably isn't, you know a major thing that they'd want to be worrying about. >> Yes, yes, so-- >> Dave: Oh please, go. >> So Splunk administrators are in such a high demand and because of that, you know, not only that customers are struggling with having the right administrators in place, also retaining them. And when they go to Cloud, you know, this is a SAS version, they don't need administrators, nor they need hardware. They can just trust the experts who are really good at doing that. >> So migrations are a tricky thing and I wonder if we can get some examples because it's like moving a house. You don't want to move, or you actually do want to move but it's, you have be planful, it's a bit of a pain, but the benefits, a new life, so. In your world, you got to be better, so the world that you just described of elastic, you don't have to plan for peaks, or performance, the cost, capex, the opex, all that stuff. It's 10 X better, no debate there. But still there's a barrier that you have to go through. So, how does AWS make it easier or maybe you could give us some examples of successful migrations and the business impact that you saw. >> Definitely. So like you said, right, migration is a journey. And it's not always easy one. So I'll talk about different kinds of migration but let me talk about Splunk migration first. So Splunk migration unlike many other migration is actually fairly easy because the Splunk data is transient data, so customers can just point all their data sources to Splunk Cloud instead of Splunk Enterprise and it will start pumping data into Splunk Cloud which is productive from day one. Now if some customers want to retain 60 to 90 days data, then they can run this Splunk Enterprise on prem for 60 more days. And then they can move on to Splunk Cloud. So in this case there was no actual data migration involved. And because this is the log data that people want to see only for 60 to 90 days and then it's not valuable anymore. They don't really need to do large migration in this case it's practically just configure your data sources and you are done. That's the simplest part of the migration which is Splunk migration to Splunk Cloud. Let's talk about different migrations. So... you have heard many customers, you know like Capital One or many other Dow-Jones, they are saying that we are going all in on AWS and they are shutting down their data centers, they are, you know, migrating hundreds of thousands of applications and servers, which is not as simple as Splunk Cloud, right? So, what AWS, you know, AWS does this day in and day out. So we have figured it out again and again and again. In all of our customer interactions and migrations we are acquiring ton of knowledge that we are building toward our migration programs. We want to make sure that our customers are not reinventing the wheel every time. So we have migration programs like migration acceleration program which is for custom large scale migrations for larger customers. We have partner migration programs which is entirely focused on working with SI partners, consulting partners to lead the migrations. As well as we're workload migration program where we are standardizing migrations of standard applications like Splunk or Atlassian, or many of their such standard applications, how we can provide kind of easy button to migrate. Now, when customers are going through this migration journey, you know, it's going to be 10 X better like you said, but initially there is a hump. They are probably needing to run two parallel environments, there is a cost element to that. They are also optimizing their business processes there is some delay there. They are doing some technical work, you know, discovery, prioritization, landing zone creations, security, and networking aspects. There are many elements to this. What we try to do is, if you look at the graph, their cost is right now where this and it's going to go down but before that it goes up and then goes down. So what we try to do is really provide all the resources to take that hump out in terms of technical support, technical enablement, you know, partner support, funding elements, marketing. There are all types of elements as well as lot of technical integrations and quick starts to take that hump out and make it really easy for our customers. >> And that was our experience, we're Amazon customer and we went through a migration about, I don't know five or six years ago. We had, you know, server axe and a cage and we were like, you know, moving wires over and you'd get an alert you'd have to go down and fix things. And so it took us some time to get there, but it is 10 X better now though. >> It is. >> The developers were so excited and I wanted to ask you about, sort of the dev-ops piece of it because that's really, it became, we just completely eliminated all the operational pieces of it and integrated it and let the developers take care of it. Became, truly became infrastructure as code. So the dev-ops culture has permeated our small organization, can't imagine the impact on a larger company. Wonder if you could talk about that a little bit. >> Definitely. So... As customers are going through this cloud migration journey they are looking at their entire landscape of application and they're discovering things that they never did. When they discover they are trying to figure out should I go ahead and migrate everything to AWS right now, or should I a refactor and optimize some of my applications. And there I'm seeing both types of decisions where some customers are taking most of their applications shifting it to cloud and then pausing and thinking now it is phase two where I am on cloud, I want to take advantage of the best of the breed whatever technology is there. And I want to transform my applications and I want to really be more agile. At the same time there are customers who are saying that I'm going to discover all my workload and applications and I'm going to prioritize a small set of applications which we are going to take through transformation right now. And for the rest of it we will lift and shift and then we will transform. But as they go through this transformation they are changing the way they do business. They are changing the way they are utilizing different technology. Their core focus is on how do I really compete with my competition in the industry and for that how can IT provide me that agility that I need to roll out changes in my business day in day out. And for that, you know, Lambda, entire code portfolio, code build, code commit, code deploy, as well as cloud trail, and you know all the things that, all the services we have as well as our partners have, they provide them truly that edge on their industry and market. >> Bina, how has the security discussion changed? When Stu and I were at the AWS public sector summit in June, the CIO of the CIA stood up on stage in front of 10,000 people and said, "The cloud on my worst day from a security perspective "is better than my client server infrastructure "on a best day." That's quite an endorsement from the CIA, who's got some chops in security. How has that discussion changed? Obviously it's still fundamental, critical, it's something that you guys emphasize. But how has the perception and reality changed over the last five years? >> Cloud is, you know, security in cloud is a shared responsibility. So, Amazon is really, really good at providing all the very, very secure infrastructure. At the same time we are also really good at providing customers and business partners all of the tools and hand-holding them so that they can make their application secure. Like you said, you know, AWS, many of the analysts are saying that AWS is far more secure than anything they can have within their own data center. And as you can see that in this journey also customers are not now thinking about is it secure or not. We are seeing the conversation that, how in fact, speaking of Splunk right, one customer that I talked to he was saying that I was asking them why did you choose Splunk cloud on AWS and his take was that, "I wanted near instantaneous SOA compliant "and by moving to Splunk cloud on AWS "I got that right away." Even I'm talking to public sector customers they are saying, you know, I want fair DRAM I want in healthcare industry, I want HIPPA Compliance. Everywhere we are seeing that we are able to keep up with security and compliance requirements much faster than what customers can do on their own. >> So they, so you take care of, certainly from the infrastructure standpoint, those certifications and that piece of the compliance so the customer can worry about maybe some of the things that you don't cover, maybe some of their business processes and other documentation, ITIL stuff that they have to do, whatever. But now they have more time to do that presumably 'cause that's check box, AWS has that covered for me, right? Is that the right thinking? >> Yes, plus we provide them all the tools and support and knowledge and everything so that they, and even partner support who are really good at it so that not only they understand that the application and infrastructure will come together as entire secure environment but also they have everything they need to be able to make applications secure. And Splunk is another great example, right? Splunk helps customer get application level security and AWS is providing them infrastructure and together we are working together to make sure our customers' application and infrastructure together are secure. >> So speaking about migrations database, hot topic at a high level anyway, I wonder if you could talk about database migrations. Andy Jassy obviously talks a lot about, well let's see we saw RDS on Prim at VMworld, big announcement. Certainly Aurora, DynamoDB is one of the databases we use. Redshift obviously. How are database migrations going, what are you doing to make those easier? >> So what we do in a nutshell, right for everything we try to build a programatic reputable, scalable approach. That's what Amazon does. And what we do is that for each of these standard migrations for databases, we try to figure out, that let's take few examples, and let's figure out Play Books, let's figure out runbooks, let's make sure technical integrations are in place. We have quick starts in place. We have consulting partners who are really good at doing this again and again and again. And we have all the knowledge built into tools and services and support so that whenever customers want to do it they don't run into hiccups and they have really pleasant experience. >> Excellent. Well I know you're super busy thanks for making some time to come on theCUBE I always love to have AWS on. So thanks for your time Bina. >> Thank you very nice to meet you both. >> Alright you're very welcome. Alright so that's a wrap for day one here at Splunk .conf 2018, Stu and I will be back tomorrow. Day two more customers, we got senior executives coming on tomorrow, course Doug Merritt, always excited to see Doug. Go to siliconangle.com you'll see all the news theCUBE.net is where all these videos live and wikibon.com for all the research. We're out day one Splunk you're watching theCUBE we'll see you tomorrow. Thanks for watching. >> Bina: Thank you. (electronic music)
SUMMARY :
Brought to you by Splunk. for the infrastructure segments at AWS. everybody's talking data, we love data. You guys, you know, Dave: You're busy. That's the team we have for this segment. you guys have gotten together and formed an alliance. you know, they are on their journey to Cloud anyway. and we talk at this conference, you know Splunk started out the funny thing is, that you know, your administrator Kind of running their instances probably isn't, you know and because of that, you know, and the business impact that you saw. They are doing some technical work, you know, and we were like, you know, moving wires over and I wanted to ask you about, sort of the dev-ops And for the rest of it we will lift and shift it's something that you guys emphasize. they are saying, you know, I want fair DRAM and that piece of the compliance so the customer but also they have everything they need to be able Certainly Aurora, DynamoDB is one of the databases we use. and they have really pleasant experience. to come on theCUBE I always love to have AWS on. we'll see you tomorrow. Bina: Thank you.
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Sandy Carter, Amazon Web Services | Girls in Tech Catalyst Conference 2018
>> From San Francisco, it's theCUBE, covering Girls in Tech Catalyst Conference, brought to you by Girls in Tech. >> Hey, welcome back, everybody. Jeff Frick here at theCUBE. We're in downtown San Francisco at the Girls in Tech Catalyst Conference, about 700 professionals. It's a really cool conference. It's a single track, two days. All the presentations are about 15, 20 minutes of people telling their stories, vast majority of women, a couple of men. I think they brought in some younger kids to get inspired. So we're excited to be here. Been coming for a couple years. And our next guest, many time CUBE alum, I just know her as Sandy Carter. She does have a title, VP of Enterprise Workloads at AWS, but I dunno, Sandy, how long have you been coming on the CUBE, how many years? >> Oh, wow, I don't know. >> Too many to count, and we don't want to admit to it. >> Yeah, it's true, but thank you guys for supporting events like this, Jeff, because I know that you guys have been supporting Women in Tech, and Girls in Tech for so long, and we really appreciate that very much. Thank you. >> And it's so important, and we love to do it, and we especially love when it's right in our backyard. It makes it really easy just to grab some crew and run up here. >> (laughing) That's right. >> So give us an update. You are chairman of the board now, and I think we've probably talked to probably three or four board members today. It's a really impressive group of people, and Adriana has done amazing things with this organization in the last 11 years. And you're sittin' watching it grow internationally, the number of events, the types of events. Give us your perspective. >> Yeah, so I think Girls in Tech is an amazing organization. That's why I decided to join the board and then to take on the chairman of the board position. And the reason I think it's so powerful is that it's really focused on young women, millennial women who are looking to become business owners, leaders, entrepreneurs and who want to apply technology to make themselves more competitive. You know, I know Adriana came up with this in 2007, but even today, the mission and the values are still really relevant. These are the top things that women need to know about today, and this is really about filling up the pipeline, sharing experiences. The conference today, I don't know if you got to hear any of the sessions, but they're really not about, you know, let me do technical skills. It's really about how do you break through the next level, how do you grow your business, how do you scale. And so it's really those type of topics that we can share experiences as experienced businesswomen with others so that they can learn and grow from that. >> Right, and just really simple stuff, like raise your hand, take the new assignment, take a risk. >> You got it, the crooked path. >> The crooked path, that was the one I was looking for. And do something that you don't necessarily have experience in, whether it's finance or accounting or HR or product management, sales. You know, take a risk, and chances are you're going to get paid off for it, and I think those simple lessons are so, so important. And then, of course, which comes up time and time again is just to have role models, senior role models who've been successful, who have an interesting story, they have a crooked path, it wasn't easy it wasn't even defined, but here they are as successful so that the younger women can look up to them. >> Yeah, absolutely, and I think that it's, you know the big message today, I think, for women was have the confidence. Basically that sums up what you just said, right? Be confident, and even if you don't feel confident, show confidence. >> Right, right. >> Which I think is so important.. >> Fake it 'til you make it, right >> That's right. You got it, you got it. >> 'Cause everybody else is, you just don't know it. >> That's right. >> You think they know what they're doing. They're doing the same thing. >> That's right. Well, it's interesting, one of the stats today said that men will apply for a job if they have 60% of the qualifications. Women will only apply if they have between 90 or 95%. So I think being able to know that you're confident and that you're going to make it, that you're going to do things and going ahead and taking that risk is really important. >> So the other big shift that we've seen in this conference is really the corporate sponsorship. So AWS is here obviously. You're here. You're on the board. But the amount of logos, the size of the companies on the logos has really grown a lot since I think we were first at this one in Phoenix in 2016. >> Phoenix, yeah, yeah, yeah, yeah. >> So not only, again, is that the right thing to do, but it's also really good business to get involved, and you great ROI for being involved in these types of organizations. >> That's right. You know, innovation is really about having diversity of thought, and so having women, having different colleges, having different sexual orientation, just diversity really helps you to innovate. >> Right. >> 93% of CEOs said that innovation is their number one competitive advantage. So we're seeing a lot of companies now pick up on that and know that they've got to come and they've got to be attractive, not only as a company that people would want to work at, an employer, but also just as a company that you might want to do business with. So today, I love the story of GoDaddy. She was saying GoDaddy was targeting small businesses. Well, most of those are run by women, but they weren't doing the right targeting. So I think it's a phenomenal change that we're seeing with companies like this doing the support. AWS, Amazon Web Services is proud to be one of the major sponsors. We had Charlie, one of our SVPs on stage today, chatting about lessons he've learned, but we've also don't things like understanding how women are buying, and we're doing focus groups, and we're doing different things like that to really help us gain insight. >> Right, so final question, from the board point of view as you look forward in the expansion opportunities, they seem almost unlimited between the countries, the participants and the variation in types of events that you guys are undertaking. It's really quite a bit to bite off. >> Well, you know, we have kind of a two prong mission. One is for entrepreneurs, and so you're seeing us really emphasize classes and things like our Amplify event where we have women come and pitch ideas that really grow that side of the business. In fact, I was just in Cuba last week, on behalf of Girls in Tech, talking to female entrepreneurs there and how we could help them because they really want us to set up some classes there to teach these entrepreneurs how to grow. And the second prong of our mission is around technology and coding. So we've got classes. We've got things with AWS like We Power Tech, so that women can learn technology and use it for their competitive advantage. So while it seems like we're doing a lot of things, it's really around that two prong mission, entrepreneurship and that coding technology focus. >> Alright, well, Sandy, thanks again for stopping by, and really congratulations to you, not only in what you do at AWS, but really just some very, very important work with Girls in Tech. >> Great, thank you, and thank you for being so supportive. We appreciate it very much. >> Our pleasure. Alright, She's Sandy Carter. I'm Jeff Frick. You're watching theCUBE from Girls in Tech Catalyst in downtown San Francisco. Thanks for watchin'. (upbeat electronic music)
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Vincent Quah, Amazon Web Services | AWS Public Sector Summit 2018
(electronic music) >> Live, from Washington, DC, it's theCUBE, covering AWS Public Sector Summit 2018. Brought to you by Amazon Web Services and its ecosystem partners. >> Hey, welcome back. We're here live in Washington, D.C. It's theCUBE's coverage of AWS Public Sector Summit Amazon Web Services, Public Sector Summit. It's like re:Invent but also for public sector. But it's a global public sector. I'm John Furrier with Stu Miniman. Our next guest is Vincent Quah, who's the head of education, nonprofits, and healthcare in Asia, Pacific, and Japan for AWS. Welcome to theCUBE. >> Thanks, John, Stu. Great to be able to be here. >> You know, we constantly talk about cloud in the United States here, and people use the word GovCloud, and Teresa and I always kind of jokingly say, "No, it's bigger than GovCloud. It's global public sector." You bring an international perspective covering APAC for AWS? >> Yes, absolutely. >> Public sector, okay. Outside of China, which is a different division with Amazon, you got the whole world. So Teresa and the team are looking not just at the US, it's the entire world. What's different? How's that working? Give us an update. >> I think one of the key differences that we see is that the US has really led the way in terms of the adoption of cloud technologies. We had great examples of universities that have really gone all in. What we are seeing now is that universities and education institutions in Asia, they're beginning to pick up their pace. And it's exciting to see some of the universities really coming very strongly using AWS. And we're seeing this across not just in mature countries and developed countries, but also in developing countries. And so it is a very widespread adoption of the cloud. And we're very excited by that. Tell us about the AWS Educate. Teresa Carlson on stage yesterday very highlighted much in her keynote about education, as well as some of the that they're doing with retraining and educating young people and whatnot, but really education has been a real growth area, from interest with cloud. Because old IT (laughs) okay, you look at that, okay, there's never had a lot of IT guys. (Vincent laughs) But it's really changed both technology procurement and delivery, but also the impact. >> Right. >> Talk about the AWS Educate program. >> So the AWS Educate program is a free program that all institutions can join. It comes with content from AWS, it comes with content from some of the top computer science universities in the world, as well as Cloud Credits, where individual student members, or the educator's members, they can actually get access to using the real platform that AWS provide. Now, this is really game-changing for students and for the institution. And it's game-changing because they have exactly the same access to all the 125-plus technologies that AWS provide to enterprises and now they are in the hands of students. So can you imagine, if they have the experience using some of these services, building capabilities, building solutions and services, and bringing out to the market. So now, innovation is in the hands of every single individual. And Educate is such an important program to re-skill and skill graduates to be ready for the working world. >> I love that, Vincent. I think back, most of my career, when you talked about education, you talked about research and universities. So it was a certain top-tier and a very limited amount. You're really democratizing what's happening. Wonder if you have any examples, or what sort of innovations are coming out of some of these global initiatives? One of the great example is NOVA, right. So we've announced that NOVA is now building this cloud associate degree as part of their information systems technology. >> What's NOVA again? >> The Northern Virginia Community College. >> In the keynote yesterday, not to be confused with Villanova, the basketball champs. >> Northern Virginia, got it, sorry. >> So, there's a need there that the institutions see because there's so much that the industry would need in terms of skills and graduates graduating with the right skill set. If you look at the World Economic Forum that was published in 2016, more about Internet and cloud computing are the two key technological drivers that's creating all these change in the industry. And many, many organizations are now investing into skilling and re-skilling. Educate sits so nicely to this particular part of the agenda. Apart from what NOVA has done here in the US, there are two other examples I want to quickly highlight to you. The first is, in the first week of June, we actually did an event in the Philippines. It was a large-scale student event. We had more than hundreds of students in a single location, with probably close to 100 educators. We took them through a four-day event. Two days of skills and content learning with hands-on experience. A third day on a gamified challenge that we put the students through so that they can compete with one another in groups, and thereby achieving top-notch scores in the leaderboard. And at the end of the day, they actually get to also develop a curriculum vitae, a CV, that they can actually submit to companies. And on the fourth day, we brought more than 20 companies as part of this whole event, and we got the students to actually connect with the companies, and the companies to the students, so that where the companies are looking for jobs, these are the students that are ready with skills that they have learned over the past three days, that they can apply to jobs that these company are looking for. So that's a really strong case of what we see working. Connecting skills to companies that are looking for students with the right set of skills. >> Talk about the international global landscape for a minute. You have a unique perspective in your job. What are the key things going on out there? What's the progress look like? What are some of the successes? Can you share a little bit about what's going on in Asia, Pacific, and Japan? >> Sure. There'll be two examples that I'll be sharing. The first is, we know that AWS Educate started off at the tertiary level. But then, last re:Invent is now being extended to 14 years and above. So now children at that age can learn about the cloud and be made aware of what's the potential of the cloud and what they can learn and use the cloud for. We've also begun to extend that work into the adult working workforce. One very specific example that I can share with you. There is an organization in Singapore called the National Trade Unions Congress LearningHub. They're an education service provider and they provide education services to citizens of Singapore. We have worked with them. They're using the AWS Educate content, and they develop two courses. Fundamentals in Cloud Computing and Fundamentals in IoT. They bring this pilot courses for the Fundamentals in IoT to a group of individuals age 45 to 74 years old. And they came away, the course just simply blew their mind away. They were so excited about what they have learned. How to program, actually, I have with me, an Internet of Things button. Now they can actually come up with an idea, program an activity on this button, so that it trigger off a particular reaction. And that's the excitement that these individuals 45 to 74 years old. They have the domain expertise, now they need is just an idea and a platform. >> It's also entrepreneurial too. >> Absolutely. >> They can tinker with the software, learn about the cloud at a very young age, and they can grow into it and maybe start something compelling, have a unique idea, fresh perspective. >> Correct. >> Or, someone who's retraining, to get a new job. >> Correct. And innovation is, we keep thinking of innovation as something that's really big. But actually, innovation doesn't have to be that way. It can start very small and then scale up from there. And all you need is just an idea to apply. >> All right. So Vincent, one of the themes we've been talking a lot about at the show is cybersecurity. Can you speak how that discussion plays specifically in the education markets? >> What we want to do is really raise the awareness of every individual's understanding of cloud computing. And by that I mean from 14 years to 74 years old. We want to let them know, actually, they are already interacting with cloud technologies. For example, if you have a Samsung Smart TV at home, if you have made a hotel booking through Expedia or through Airbnb, or if you have called for home delivery from McDonald's, you've already interacted with the cloud. And so what we want to do is make sure that everybody actually understand that. And then through some of these courses that are being provided by our partners, then they can go and learn about the security part of it. And help them have a much better sense of idea of, look, the cloud is actually a lot more secure. And we've heard many examples of that today and yesterday. And we want to give them that assurance that what they are doing and consuming, they can be part of that entrepreneur process to create something new and very exciting. >> Vincent, I'll give you the last word on this interview by sharing the update from Asia Pacific AWS. How many people are out there's a growing, you're hiring. What are some of the priorities you guys have. They have a big event in Singapore, I know that. We've been watching it, thinking about bringing theCUBE there. Give us some idea of the growth around the AWS people. What's the head count look like, give us some estimations. John, you know I can't really talk too much about head count, but I can say that we are definitely growing our AWS head count very, very rapidly. The needs and requirements out there in the market is so tremendous, and we want to be able to serve the customer as best as we can. We are a customer-obsessed company, and so we want to be there with the customer, work with them to really meet the objective and the goals that they have. And help them achieve that vision. And so we are just the enabler. We empower the customer to make. >> You have events out there too, right? You have the re:Invent, Summit? >> We have the AWS Summit, and this coming October we have the Public Sector Summit here in Singapore, as well as in Canberra in September. >> Right. >> And there's an education event coming domestically, too. >> And there's an education, a global education event that is happening in Seattle in August. So we're very excited about that. >> Lot of action on the AWS ecosystem. Congratulations, you guys do a great job. Thanks for coming on theCUBE, Vincent. Really appreciate it. We're here live in Washington, DC. It's theCUBE's coverage of AWS Public Sector Summit. I'm John Furrier with Stu Miniman. We've got Dave Vellante here as well, coming and joining us for some interviews. We'll be right back, stay with us for more coverage after this short break. (electronic music)
SUMMARY :
Brought to you by Amazon Web Services Welcome to theCUBE. in the United States here, and people use the word GovCloud, So Teresa and the team are looking is that the US has really led the way the same access to all the 125-plus technologies One of the great example is NOVA, right. In the keynote yesterday, not to be confused with And at the end of the day, they actually get to also What are some of the successes? And that's the excitement that these individuals learn about the cloud at a very young age, And innovation is, we keep thinking So Vincent, one of the themes look, the cloud is actually a lot more secure. We empower the customer to make. We have the AWS Summit, and this coming October And there's an education, a global education event Lot of action on the AWS ecosystem.
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Hardik Bhatt, Amazon Web Services | AWS Public Sector Summit 2018
(techno music) >> Live, from Washington DC, it's theCUBE. Covering AWS Public Sector Summit, 2018. Brought to you by Amazon Web Services and its ecosystem partners. >> Okay, welcome back, everyone, this is the live CUBE coverage here in Washington DC for AWS Public Sector Summit 2018. This is the, kind of like the reinvent for Public Sector. I'm John Furrier, f my co-host Stu Miniman, our next guest is Hardik Bhatt, Smart Cities Vertical Lead for Amazon Web Services, been a former CIO, knows the state and local governments cold. This is a very key area around Internet of Things and technology with cloud, because smart cities have to do not only technology roll outs for some of the new capabilities, but all manage some of the societal changes, like self-driving cars and a variety of other things, from instrumenting sensors and traffic lights and video cam ... I mean, this is a little, just a little ... Welcome to theCUBE. >> Thank you very much, John. Good to see you, Stu, good morning. Looking forward to having a great conversation. >> So, smart cities obviously is really hot, but we love it, because it brings life, and work, life, and play together, because we all live in towns, and we live in cities, and the cities provide services to the residents, transportation, sidewalks, and things that we take for granted in the analog world. Now there's a whole digital set of services coming big time. So, are they prepared? (laughs) It used to be buy a mainframe, then move it to a minicomputer, get a Local Area Network, buy some PCs, buy some network tablets, now the cloud's here. What's your assessment of the smart cities landscape for state and local governments? Because it really is something that's on the front burner, in terms of figuring it out. What's the architecture? Lot of questions. What's your, what's the state of the union, if you will, for-- >> You know it has been, like, how the governments have been for many years, right? Governments exist so that they can provide better services, they can provide better quality of life, they can create an environment where businesses thrive, jobs can be created, education can be given, and you can build a workforce and talent, et cetera. And smart cities is just, I'd say, a trend where, you know, you're using multitudes of technology to kind of help the government get its mission accomplished in a smoother, faster, better, cheaper manner. And a lot of times, I've seen, because how smart cities movement started a decade ago, we kind of compare smart cities with the Internet of Things or the sensors, but smart cities is much more than just the IoT, or the Internet of Things, I mean if you're talking about creating a new stream of data that is real-time, whether coming in from sensors, coming from video, you already as a government, I used to be a CIO for the City of Chicago, we used petabytes of data that was already sitting in my data center, and then there's also this whole third-party data. So smart cities is a lot about how do you as a city are aggregating this different sources of data and then making some action from it, so that ultimately, going back to the city's priorities, you are giving better public safety, or you're providing better public health, or you're providing better education or you're providing, better providing government services. So that's what we are seeing. Our customers are, when we say smart cities, they jump right into, "What problems are you solving?" And that, to me, is the core for Amazon, core for Amazon Web Services. We want to know our customers' problems and then work backwards to solve them. >> What are some of the problems right now that are low-hanging fruit? Because obviously it's an evolution. You set the architecture up, but ultimately governments would love to have some revenue coming in from businesses. You mention that. Education is certainly there. What are some of the challenges there? Is it pre-existing stuff, or is it new opportunities? What are some of the trends you're seeing for use cases? It is actually both pre-existing stuff that they are trying to solve, as well the new stuff, the new opportunities that are getting created, because the technology is much different than what it used to be 10 years ago. The cloud, especially, is creating a lot more new opportunities, because of the nimbleness it brings, the agility it brings. So, in transportation side, we are seeing on one hand, multiple departments, multi-jurisdictional, so state transportation department, as well as a local transportation department, working together to create kind of a virtual information sharing environment or a virtual command center, so that they can detect an accident, a traffic incident, much quicker and respond to that, because now they can aggregate this data. And they're also now adding to that some public safety information. So whether it is a police department, fire department, EMS, so that they can address that incident quickly and then not only clear the traffic and clear the congestion, or reduce the congestion time, but they can also address the, any public safety issue that may have arisen out of that incident that has happened. So, the Department of Transportation, the USDOT, through the Federal Highway Administration, has been giving out $60 million worth of grants to six to ten recipients. The grant, this year's grant period, just closed on Monday, and we worked with multiple customers who are looking to kind of respond to that. So on one hand, it is that. So this is an age-old problem, but new technology can help you solve that. On the other hand, another customer that we worked with is looking for on-demand micro-transit solutions. As you can see, all the ride-sharing applications are making easier to jump in a car and move to one place to the other. It is causing a dip in transit ridership. So the public transit agents, they are looking for solutions to that. So they are looking at, "Can we build an on-demand microtransit "so you can pool your friends and jump into a transit van, as opposed to a private car?" And then you can go from point A to point B in a much more affordable manner. So they are looking at that. On the public health side, you know, we have the DC Benefits Exchange, Health Benefits Exchange, is on AWS, and they have seen significant savings. They have seen $1.8 million of annual savings because they are using cloud and cloud services. On the other hand, you have State of Georgia, which is using Alexa. So they have built Alexa Skills where you can ask, as a resident of State of Georgia getting SNAP benefit, the Supplemental Nutritional Assistance, the food-stamp program, you can say, "Alexa, what's my SNAP balance?" So based on the answer then, based on the balance you know, you can plan your, you know, where you're going to use that money. So we are seeing large volume of data now coming on the cloud where the governments are looking to move kind of the needle. We are also seeing this nimble, quick solutions that can start going out. And we are seeing a lot of driver behind the innovation is our City on a Cloud challenge. So we have seen the City on a Cloud winners, since last so many years, are kind of the ones who are driving innovation and they're also driving a lot of collaboration. So I can, there are three trends that I can jump into as we kind of talk more. >> Yeah, it's interesting. I think back a decade ago, when you talk smarter cities, you'd see this video, and it would look like something out of a science fiction. It's like, you know, "Oh, the flying taxi'll come, "and it will get you and everything." But what I, the stories I have when I talk to CIOs in cities and the like, it's usually more about, it's about data. It's about the underlying data, and maybe it's a mobile app, maybe it's a thing like Alexa Skills. So help us understand a little bit, what does the average citizen, what do they see? How does their, you know, greater transparency and sharing of information and collaboration between what the agencies are doing and, you know, the citizenship. >> I think that's a great question. I mean that is what, as a former CIO, I always had to balance between, what I do creates internal government efficiency, but the citizens don't feel it, don't see it, they don't, it doesn't get in the news media. And on the other hand, I also have to, to my governor, to my mayor, to the agency directors, have to give them visible wins. So, I'll give you an example, so City of Chicago, back in the day, in 2010 when I was the CIO. We did a contract with our AWS, currently AWS Partner Socrata, to open up the data. So that was kind of the beginning of the Open Data Movement, and eventually, I left the city, I went work for Cisco, and the city government continued to kind of build on top of Socrata. And they build what they called the Windy Grid, which is basically bringing all of their various sets of data, so 311, code violations, inspections, crime, traffic, and they built an internal data analytics engine. So now, agencies can use that data. And now, what they did, two years ago, they were one of the City on a Cloud Challenge winners, and they, Uturn Data Solutions is our partner that was the winner of that, and they built Chicago Open Grid. So they basically opened that up on a map-based platform. So now as a citizen of Chicago, I can go on Chicago Open Grid, and I can see which restaurants in, surrounding my area, have failed inspections. Have they failed inspection because of a mice infestation, or was it something very minor, so I can decide whether I want to go to that restaurant or not. I can also look at the crime patterns in my area, I can look at the property values, I can look at the education kind of quality in the schools in my neighborhood. So, we have seen kind of now, and it's all on AWS cloud. >> This open data is interesting to me. Let's take that to another level. That's just the user side of it, there's also a delivery value. I saw use cases in Chicago around Health and Human Services, around being more efficient with either vaccines, or delivery of services based on demographics and other profile, all because of open data. So this brings up a question that comes up a lot, and we're seeing here is a trend, is Amazon Web Services public sector has been really good. Teresa Carlson has done an amazing job leaning on partners to be successful. Meaning it's a collaboration. What's that like in the state and local government? What's the partner landscape look like? What are the benefits for partners to work with AWS? Because it seems obvious to me, it might not be obvious to them. But if they have an innovative idea, whether it's to innovate something on the edge of the network in their business, they can do it, and they can scale with Amazon. What is the real benefits of partnering with AWS? >> You hit a key point on there. Teresa has done a fantastic job in customer management as well as building our partners. Similarly, we have a great leader within the state and local government, Kim Majerus. She leads all of our state and local government business. And her focus is exactly like Teresa: How can we help the customers, and also how can we enable partners to help customers? So I'll give you and example. The City of Louisville in Kentucky. They were a City on a Cloud winner, and they, basically what they're building with a partner of ours, Slingshot, they (laughs) get, I was, I used to be in Traffic Management Authority, back in my days, and we used to do traffic studies. So, basically, they send an intern out with clicker or have those black strips to count the number of cars, and based on that, we can plan whether we want to increase the signal timing on this approach, or we can plan the detours if we close the street, what's the, and it's all manual. It used to take, cost us anywhere from 10 to 50 thousand dollars, every traffic study. So what Louisville did with Slingshot is they got the free Waze data that they get gives all of the raw traffic information. Slingshot brought that on to a AWS platform, and now they are building a traffic analysis tool, which now you can do like a snap of a finger, get the analysis and you can manage the signal-approach timing. The cool thing about this is, they're building it in open source code. And the code's available on GitHub, and I was talking to the Chief Data Officer of Louisville, who's actually going to be speaking at this event later today. 12 other cities have already looked into this. They've started to download the code, and they are starting to use it. So, collaboration through partners also enables collaboration amongst all of our customers. >> And also, I'd just point out, that's a great example, love that, and that's new for me to hear that. But also, to me the observation is, it's new data. So being able to be responsive, to look at that opportunity. Now, it used to be in the old world, and I'm sure you can attest to this, being a CIO back in the day, is okay, just say there's new data available, you have to provision IT. >> Oh my God, yeah. >> I mean, what, old way, new way. I mean, compare and contrast the time it would take to do that with what you can do today. >> It's a big, huge difference. I'll tell you as the CIO for the State of Illinois, when I started in early 2015, in my first performance management session, I asked my Infrastructure Management Team to give me the average days it takes to build a server, 49 days. I mean, you're talking seven weeks or maybe, if you talk, 10 business weeks. It's not acceptable. I mean the way the pace of innovation is going, with AWS on cloud, you are talking about minutes you can spin up that server. And that's what we are seeing, a significant change, and that's why Louisville-- >> And I think you got to think it's even worse when you think about integration, personnel requirements, the meetings that have to get involved. It's a nightmare. Okay, so obviously cloud, we know cloud, we love cloud, we use cloud ourselves. So I got to ask you this could, City in a Cloud program, which we've covered in the past, so last year had some really powerful winners. This has been a very successful program. You're involved in it, you have unique insights, you've been on both sides of the table. How is that going? How is it inspiring other cities? What's the camaraderie like? What's the peer review? Is there a peer, is there a network building? How is that spreading? >> That is actually enabling collaboration in a significant manner. Because, you know, you are openly telling what you want to do, and then you are doing that. Everybody is watching you. Like Louisville is a perfect example where they built this, they're building this, and they're going to share it through open source code to all the cities. 12 is just the beginning. I'd not be surprised if there are 120 cities that are going to do this. Because who doesn't want to save two hundred, three hundred thousand dollars a year? And also lots of time to do the traffic studies. Same thing we have seen with, as Virginia Beach is building their Early Flood Warning System. There are other cities who are looking into, like how do we, New Orleans? And others are looking at, "How do we take what Virginia Beach has built? "And how can we use it for us?" And yesterday, they announced this year of the winners that includes Las Vegas, that includes LA Information Technology Department, that includes the City of Philadelphia, and I've been in conversations with all of the CIOs, CDOs, and the leaders of these agencies. The other thing, John, I have seen is, there's a phenomenal leadership that's out there right now in the cities and states that they want to innovate, they want to collaborate, and they want to kind of make a big difference. >> Hold on, hold on, so one more question, this is a really good question, want to get, follow-up on that. But this, what you're talking about to me signifies really the big trend going on right now in this modern era. You've got large cloud scale. You have open source, open sharing, and collaboration happening. This is the new network effect. This is the flywheel. This is uniquely different. This kind of categorizes cloud. And this wasn't available when IT systems and processes were built, 20, 30 years ago. I mean, this is the big shift, you, I mean do you agree? >> Absolutely, this is the big shift, the availability of the cloud, the ubiquitous nature of mobile platform that people have. The newer way of, like, the natural language processing, use of Alexa is becoming so prevalent in government. I mean, in City of Chicago, 50% of the 311 calls that we used to get in 2010, 3 1/2 million of those were informational in nature. If I could offload that on to my Alexa Skills, I can free up my workforce, the 311 call-takers, to do much better, higher-level, you know, call-taking, as opposed to this. So you're absolutely right. I've seen the trends we are seeing is, there is lots of collaboration going on between the governments and partners. I'm also seeing the governments are going at modernization from different points based on their pain points. And I'm also seeing a definite acceleration in modernization. Government, because the technology, AWS, the cloud, our services that we are seeing. And the pace of innovation that AWS brings is also enabling the acceleration in governments. >> Yeah, to help put a point on the, on the conversation here, there's been for years discussion about, "Well, what is the changing role of the CIO?" You've sat on that side of the table, you know, worked with lots of COs, what do you see is the role of the future for the CIO when, specifically when you talk state and local governments? >> I would say CIO is the kind of has to be an enabler of government services. Because if I go back to my city days and working with a mayor, or my state days, working with a governor, at the end of the day, the governor or the mayor is looking at creating better quality of life, providing better health, better education, better safety, et cetera. And CIO is kind of the key partner in that metrics to enable what the governor, what the mayor, the agency directors want to do. And because now data enables the CIO to kind of quickly give solutions, or AI services, Alexa and Polly and Rekog ... All of these things give you, give me as a CIO, ability to provide quick wins to the mayor, to the governor, and also very visible wins. We are seeing that, you know, CIO is becoming a uniquely positioned individual and leader to kind of enable the government. >> All right, thanks so much for comin' on theCUBE. Love the insight, love to follow up. You bring a great perspective and great insight and Amazon's lucky to have you on the team. Lot of great stuff goin' on in the cities and local governments. It's a good opportunity for you guys. Thanks for coming on, appreciate it. >> Thank you very much. >> It's theCUBE live here in Washington DC for AWS, Amazon Web Services Public Sector Summit, I'm John Furrier, Stu Miniman, again second year of live coverage. It's a packed house, a lot of great cloud action. Again, the game has changed. It's a whole new world, cloud scale, open source, collaboration, mobile, all this new data's here. This is the opportunity, this is what theCUBE's doing. We're doin' our part, sharing the data with you. Stay with us, more coverage from day two, here in Washington, after this short break. (techno music)
SUMMARY :
Brought to you by Amazon Web Services for some of the new capabilities, Good to see you, Stu, good morning. and the cities provide services to the residents, and you can build a workforce and talent, et cetera. So based on the answer then, based on the balance you know, It's about the underlying data, and eventually, I left the city, I went work for Cisco, What are the benefits for partners to work with AWS? get the analysis and you can manage and that's new for me to hear that. the time it would take to do that I mean the way the pace of innovation is going, the meetings that have to get involved. in the cities and states that they want to innovate, This is the new network effect. I mean, in City of Chicago, 50% of the 311 calls And CIO is kind of the key partner in that metrics and Amazon's lucky to have you on the team. This is the opportunity, this is what theCUBE's doing.
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Sandy Carter, Amazon Web Services | Girls in Tech Catalyst Conference 2018
>> From San Francisco, it's theCUBE, covering Girls in Tech Catalyst Conference, brought to you by Girls in Tech. >> Hey, welcome back, everybody. Jeff Frick here at theCUBE. We're in downtown San Francisco at the Girls in Tech Catalyst Conference, about 700 professionals. It's a really cool conference. It's a single track, two days. All the presentations are about 15, 20 minutes of people telling their stories, vast majority of women, a couple of men. I think they brought in some younger kids to get inspired. So we're excited to be here. Been coming for a couple years. And our next guest, many time CUBE alum, I just know her as Sandy Carter. She does have a title, VP of Enterprise Workloads at AWS, but I dunno, Sandy, how long have you been coming on the CUBE, how many years? >> Oh, wow, I don't know. >> Too many to count, and we don't want to admit to it. >> Yeah, it's true, but thank you guys for supporting events like this, Jeff, because I know that you guys have been supporting Women in Tech, and Girls in Tech for so long, and we really appreciate that very much. Thank you. >> And it's so important, and we love to do it, and we especially love when it's right in our backyard. It makes it really easy just to grab some crew and run up here. >> (laughing) That's right. >> So give us an update. You are chairman of the board now, and I think we've probably talked to probably three or four board members today. It's a really impressive group of people, and Adriana has done amazing things with this organization in the last 11 years. And you're sittin' watching it grow internationally, the number of events, the types of events. Give us your perspective. >> Yeah, so I think Girls in Tech is an amazing organization. That's why I decided to join the board and then to take on the chairman of the board position. And the reason I think it's so powerful is that it's really focused on young women, millennial women who are looking to become business owners, leaders, entrepreneurs and who want to apply technology to make themselves more competitive. You know, I know Adriana came up with this in 2007, but even today, the mission and the values are still really relevant. These are the top things that women need to know about today, and this is really about filling up the pipeline, sharing experiences. The conference today, I don't know if you got to hear any of the sessions, but they're really not about, you know, let me do technical skills. It's really about how do you break through the next level, how do you grow your business, how do you scale. And so it's really those type of topics that we can share experiences as experienced businesswomen with others so that they can learn and grow from that. >> Right, and just really simple stuff, like raise your hand, take the new assignment, take a risk. >> You got it, the crooked path. >> The crooked path, that was the one I was looking for. And do something that you don't necessarily have experience in, whether it's finance or accounting or HR or product management, sales. You know, take a risk, and chances are you're going to get paid off for it, and I think those simple lessons are so, so important. And then, of course, which comes up time and time again is just to have role models, senior role models who've been successful, who have an interesting story, they have a crooked path, it wasn't easy it wasn't even defined, but here they are as successful so that the younger women can look up to them. >> Yeah, absolutely, and I think that it's, you know the big message today, I think, for women was have the confidence. Basically that sums up what you just said, right? Be confident, and even if you don't feel confident, show confidence. >> Right, right. >> Which I think is so important.. >> Fake it 'til you make it, right >> That's right. You got it, you got it. >> 'Cause everybody else is, you just don't know it. >> That's right. >> You think they know what they're doing. They're doing the same thing. >> That's right. Well, it's interesting, one of the stats today said that men will apply for a job if they have 60% of the qualifications. Women will only apply if they have between 90 or 95%. So I think being able to know that you're confident and that you're going to make it, that you're going to do things and going ahead and taking that risk is really important. >> So the other big shift that we've seen in this conference is really the corporate sponsorship. So AWS is here obviously. You're here. You're on the board. But the amount of logos, the size of the companies on the logos has really grown a lot since I think we were first at this one in Phoenix in 2016. >> Phoenix, yeah, yeah, yeah, yeah. >> So not only, again, is that the right thing to do, but it's also really good business to get involved, and you great ROI for being involved in these types of organizations. >> That's right. You know, innovation is really about having diversity of thought, and so having women, having different colleges, having different sexual orientation, just diversity really helps you to innovate. >> Right. >> 93% of CEOs said that innovation is their number one competitive advantage. So we're seeing a lot of companies now pick up on that and know that they've got to come and they've got to be attractive, not only as a company that people would want to work at, an employer, but also just as a company that you might want to do business with. So today, I love the story of GoDaddy. She was saying GoDaddy was targeting small businesses. Well, most of those are run by women, but they weren't doing the right targeting. So I think it's a phenomenal change that we're seeing with companies like this doing the support. AWS, Amazon Web Services is proud to be one of the major sponsors. We had Charlie, one of our SVPs on stage today, chatting about lessons he've learned, but we've also don't things like understanding how women are buying, and we're doing focus groups, and we're doing different things like that to really help us gain insight. >> Right, so final question, from the board point of view as you look forward in the expansion opportunities, they seem almost unlimited between the countries, the participants and the variation in types of events that you guys are undertaking. It's really quite a bit to bite off. >> Well, you know, we have kind of a two prong mission. One is for entrepreneurs, and so you're seeing us really emphasize classes and things like our Amplify event where we have women come and pitch ideas that really grow that side of the business. In fact, I was just in Cuba last week, on behalf of Girls in Tech, talking to female entrepreneurs there and how we could help them because they really want us to set up some classes there to teach these entrepreneurs how to grow. And the second prong of our mission is around technology and coding. So we've got classes. We've got things with AWS like We Power Tech, so that women can learn technology and use it for their competitive advantage. So while it seems like we're doing a lot of things, it's really around that two prong mission, entrepreneurship and that coding technology focus. >> Alright, well, Sandy, thanks again for stopping by, and really congratulations to you, not only in what you do at AWS, but really just some very, very important work with Girls in Tech. >> Great, thank you, and thank you for being so supportive. We appreciate it very much. >> Our pleasure. Alright, She's Sandy Carter. I'm Jeff Frick. You're watching theCUBE from Girls in Tech Catalyst in downtown San Francisco. Thanks for watchin'. (upbeat electronic music)
SUMMARY :
brought to you by Girls in Tech. on the CUBE, how many years? Too many to count, and we because I know that you and we love to do it, You are chairman of the board now, And the reason I think Right, and just really simple stuff, so that the younger women and I think that it's, You got it, you got it. is, you just don't know it. They're doing the same thing. and that you're going to make it, is really the corporate sponsorship. that the right thing to do, helps you to innovate. and know that they've got to come that you guys are undertaking. it's really around that two prong mission, and really congratulations to you, you for being so supportive. from Girls in Tech Catalyst
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Sandy Carter, Amazon Web Services | AWS Summit SF 2018
>> Announcer: Live from the Moscone Center, it's theCUBE covering AWS Summit San Francisco, 2018, brought to you by Amazon Web Services. (techy music playing) >> Welcome back, I'm Stu Miniman joined by my cohost Jeff Frick, and this is theCUBE's live coverage of AWS Summit San Francisco. We are thrilled to welcome back to the program Sandy Carter, who's a vice president with Amazon Web Services. Been with the company about a year. We've had you on the program many times, but first time since you've been at AWS, so... >> That's right, I'm celebrating my year yesterday with Amazon Web Services. >> Stu: And no cake, all right. >> I had a cake yesterday, actually, cake and champagne, by the way. (laughing) >> Sandy, we always love to hear, you know, you talk to so many customers, you know, bring us back for a little bit. What brought you to AWS, what's exciting to your customers when you're talking to them today? >> Well, you know, I really love innovation, I love being innovative, and you know, bar none Amazon is the most innovative company out there today, but really what brought me to Amazon was their focus on the customer, really "obsession" on the customer. When they say obsession they really mean obsession. They work backwards from the customer. We really have this big, big thrust. In fact, one of my favorite stories is when I first came to Amazon we'd be in these meetings and people would say, "Well, what does Low Flying Hawk think about this," or "What does Low Flying Hawk think about that," and I was like, "Who is Low Flying Hawk?" Well, he's a person who would give comments on a forum and just a person who wasn't even spending millions of dollars with Amazon but just had a lot of big clout. We actually just opened a building named Low Flying Hawk, believe it or not. >> Jeff: Have you identified this person? >> They do know who he is, yes. (laughing) But it's really, it just symbolizes the focus that Amazon has on the customer and why that's so important. >> And Sandy, at re:Invent you actually, you spoke to the analyst, I was listening to the session. It's not just kind of, people think AWS they think public cloud. You work for Amazon, it's everything kind of across what you think of Amazon.com, AWS, everything from drones and using Kindles and everything like that. Can you give us a little bit of kind of that pan view of how Amazon looks at innovation? >> Yeah, so it's really interesting. Amazon is very methodical in the way that we innovate, and what we do is we really try to understand the customer. We work backwards from the customer, so we do a press release first, we do frequently asked questions next, and then we do a narrative-- >> You're saying you do an internal press release, yes, yes. >> Yeah, internal press release. Internal frequently asked questions, and then we review a six-page document, no PowerPoints whatsoever, which enables us to debate and learn from each other and just iterate on the idea that makes it better and better and better so that when we come out with it it's a really powerful idea and powerful concept, something that the customers really want. >> So, we'll ask you what you're doing now, but one more kind of transition question, what was your biggest surprise? You know, there's a lot of kind of mystery from people on the outside looking in in terms of culture, and we know it's car driving and innovative growing like crazy company, not only in business but in terms of people. What was your biggest surprise once you kind of got on the inside door? >> My biggest surprise was just how incredibly encouraging and supportive the team is at AWS. My boss is Matt Garman, he's been supportive since day one, you know, Andy, they just cheer you on. They want you to do well and I've really never been at a company that everybody's really pulling for you to be successful, not political infighting but really pulling for you to be successful. So, that's really was the biggest surprise to me, and then that customer obsession. Like, it's not customer focus, it really is customer obsession. >> Right, I think it's so well illustrated by the, again not AWS, but Amazon with the store, right, with no cash register, no people. >> Sandy: Amazon Go. >> To think about that-- >> Sandy: Yeah. >> From the customer point of view is nobody likes to stand in line at the grocery store, so it's such a clean illustration of a customer centric way to attack the problem. >> And I love that because what we did is we opened up the beta first for employees, so we would go in and play with it and test it out, and then we opened it up in Seattle and we would give customer tours. Now it's open to the public in Seattle, so it just again shows you that iterative process that Amazon uses and it's super cool, have you guys been? >> Jeff: Have not been. >> Ugh, in fact, my daughter went in. She put on a mask, she was going to fool the system but it wasn't fooled. All the ML and all the AI worked brilliantly. >> I love how everyone loves to get so creative and try to, you know, get through the system, right, try to break the system. >> I know, but my daughter, that's what I would figure for sure. (laughing) >> So, what are you working on now? You've been there a year, what are you working on? >> So, we are innovating around the enterprise workload, so we know that a lot of startups and cloud native companies have moved to the cloud, but we're still seeing a lot of enterprises that are trying to figure out what their strategy is, and so, Stu and Jeff, what I've been working on is how do we help enterprises in the best way possible. How can we innovate to get them migrated over as fast as possible? So for instance, we have Windows that runs on AWS. It's actually been running there longer than with any other vendor and we have amazing performance, amazing reliability. We just released an ML, machine learning OMI for Windows so that you can use and leverage all that great Windows support and applications that you have, and then you guys saw earlier I was talking to VMware. We know that a lot of customers want to do hybrid cloud on their journey to going all-in with the cloud, and so we formed this great partnership with VMware, produced an offering called VMware Cloud on AWS and we're seeing great traction there. Like Scribd's network just talked about how they're using it for disaster recovery. Other customers are using it to migrate. One CIO migrated 143 workloads in a weekend using that solution. So, it just helps them to get to that hybrid state before they go all-in on the cloud. >> So, are they, I was going to say, are they building a mirror instance of what their on-prem VMware stack is in the Amazon version? Is that how they're kind of negotiating that transition or how does that work? >> So, with VMware they don't have to refactor, so they can just go straight over. With Microsoft workloads what we're seeing a lot of times is maybe they'll bring a sequel app over and they'll just do a lift and shift, and then once they feel comfortable with the cloud they'll go to Aurora, which as you've found was the fastest growing service that AWS has ever had, and so we see a lot of that, you know, movement. Bring it over, lift and shift, learning and you know, if you think about it, if you're a large enterprise one of your big challenges is how do I get my people trained, how do I get them up to speed, and so we've done... Like, we've got a full dot net stack that runs on AWS, so their people don't even have to learn a new language. They can develop in Visual Studio and use PowerShell but work on AWS and bring that over. >> You know, Sandy, bring us inside your customers because the challenge for most enterprises is they have so many applications. >> Sandy: Yeah. >> And you mentioned lift and shift. >> Sandy: Yeah. >> You know, I know some consultant's out there like, "Lift and shift is horrible, don't do it." It's like, well, there's some things you'll build new in the cloud, there's some things you'll do a little bit, and there's some stuff today lift and shift makes sense and then down the road I might, you know, move and I've seen, you know, it was like the seven Rs that Amazon has as to do you re-platform, refactor-- >> That's right. >> You know, all that and everything, so I mean, there's many paths to get there. What are some of the patterns you're hearing from customers? How do they, how is it easier for them to kind of move forward and not get stuck? >> Well, we're seeing a lot of data center evacuations, so those tend to be really fast movement and that's typically-- >> Jeff: Data center evacuation-- >> Yeah, that's what-- >> I haven't heard that one. >> Yeah, that's what, evacuation, they've got to get out of their data center buyer for a certain date for whatever reason, right? They had a flood or a corporate mandate or something going on, and so we are seeing those and those are, Stu, like lift and shift quickly. We are seeing a lot of customers who will create new applications using containers and serverless that we talked about today a lot, and that's really around the innovative, new stuff that they're doing, right. So, Just Eat, for instance, is a large... They do online food service out of the UK. I love their solution because what they're doing is they're using Alexa to now order food, so you can say, "Alexa, I want a pizza delivered "in 20 minutes, what's the best pizza place "that I can get in 20 minutes?" Or "I want sushi tonight," and Alexa will come back and say, "Well, it's going to take "an hour and a half, you had sushi two days ago. "Maybe you want to do Thai food tonight." (laughing) And so it's really incredible, and then they even innovated and they're using Amazon Fire for group ordering. So, if there's a big football game or something going on they'll use Amazon Fire to do that group ordering. All that is coming in through Alexa, but the back end is still Windows on AWS. So, I love the fact that they're creating these new apps but they're using some of that lift and shift to get the data and the training and all that moving and grooving, too. >> Yeah, what do you, from the training standpoint, how, you know, ready are customers to retrain their people, you know, where are there shortages of skillsets, and how's Amazon, you know, helping in that whole movement? >> Well, training is essential because you've got so many great people at enterprises who have these great skills, so what we see a lot of people doing is leveraging things like dot net on AWS. So, they actually... They have something they know, dot net, but yet they're learning about the cloud, and so we're helping them do that training as they're going along but they still have something very familiar. Folks like Capital One did a huge training effort. They trained 1,000 people in a year on cloud. They did deep dives with a Tiger Team on cloud to get them really into the architecture and really understanding what was going on, so they could leverage all those great skills that they had in IT. So, we're seeing everything from, "I got to use some of the current tools that I have," to "Let me completely move to something new." >> And how have you, you've been in the Bay Area also for about a year, right, if I recall? >> Actually, I just moved, I moved to Seattle. >> Jeff: Oh, you did make the move, I was going to say-- >> I did. (laughing) >> "So, are they going to make you move up north?" >> I did because I was-- >> You timed it in the spring, not in November? >> I did, there you go. (laughing) When it's nice and sunny, but it's great. >> Exactly. >> It's great to live in Seattle. Amazon has such a culture that is in person, you know, so many people work there. It's really exhilarating to go into the office and brainstorm and whiteboard with people right there, and then our EBCs are there, so our executive briefing center is there, so customers come in all the time because they want to go see Amazon Go, and so it's really an exciting, energizing place to be. >> Yeah, I love the line that Warner used this morning is that AWS customers are builders and they have a bias for action. So, how do you help customers kind of translate some of the, you know, the culture that Amazon's living and kind of acting like a startup for such a large company into kind of the enterprise mindset? >> That's a great question, so we just proposed this digital innovation workshop. We are doing this now with customers. So, we're teaching them how to work backwards from the customer, how to really understand what a customer need is and how to make sure they're not biased when they're getting that customer need coming in. How to do, build an empathy map and how to write that press release, that internal press release and think differently. So, we're actually teaching customers to do it. It's one of our hottest areas today. When customers do that they commit to doing a proof of concept with us on AWS on one of the new, innovative ideas. So, we've seen a lot of great and exciting innovation coming out of that. >> All right, well, Sandy Carter, so glad we could catch up with you again. Thanks for bringing discussion of innovation, what's happening in the enterprise customers to our audience. For Jeff Frick, I'm Stu Miniman, we'll be back will lots more coverage here, you're watching theCUBE. (techy music playing)
SUMMARY :
2018, brought to you We are thrilled to welcome back That's right, I'm celebrating my cake and champagne, by the way. love to hear, you know, I love being innovative, and you know, Amazon has on the customer across what you think of Amazon.com, AWS, that we innovate, and what we do You're saying you do an and just iterate on the idea that makes it So, we'll ask you they just cheer you on. again not AWS, but Amazon with the store, is nobody likes to stand in And I love that because what we did All the ML and all the and try to, you know, I know, but my daughter, that's what for Windows so that you and so we see a lot of because the challenge for most enterprises as to do you re-platform, refactor-- there's many paths to get there. and serverless that we and so we're helping them do that training moved, I moved to Seattle. I did. I did, there you go. you know, so many people work there. So, how do you help to doing a proof of concept with us we could catch up with you again.
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Tricia Davis-Muffet, Amazon Web Services | AWS Public Sector Q1 2018
(techno music) >> (Narrator) Live from Washington, DC. It's Cube conversations with John Furrier. (techno music) >> Hello and welcome to the special exclusive Cube Conversations here in Washington, DC. I'm John Furrier host of the Cube. Here at Amazon Web Services Headquarter World Headquarters for Public Sector Summit in Arlington, Virginia. Our special guest is Tricia Davis-Muffett, who is the Director of Marketing for Worldwide Amazon Web Services. Thanks for joining me. >> Yep. >> So we see each other and reinvent Public Sector Summit, but you're always running around. You got so many things going on. >> I am. >> Big responsibility here. (Tricia laughs) >> You guys are running hard and you have great culture, Teresa's team. Competitive, like to have fun. Don't like to lose. (Tricia laughs) >> What's it like being a marketer for the fastest growing hottest product in Washington, DC and around the world? >> Yeah. I mean it's really been amazing. When I came here, I kind of took a leap of faith on the company because it's four and a half years ago that I came. I literally accepted the job before we had even gotten our first fed ramp approval. So it wasn't entirely sure that this was going be the place to go to for technology for the government, but I really loved the way that we were helping the government innovate and save money of course. I think most of us who are in Public Sector have a passion for citizens, and for making government better and so that's really what I saw in Teresa and her team that they had such a passion to do that and that the technology was going to help the government really improve the lives of citizens. It's been great. One of the things that's been amazing is the passion that our customers have for our technology. I think they get a little taste of it and they go "Wow, I can't believe what I can do "that I thought was impossible before." And so I love seeing what our customers do with the technology. >> It's something people would think might be easy to be a marketer for Amazon, but if you think about it, you have so much speed in your business. You have a cult of personality in the Cloud addiction, or Cloud value. In addition to the outcomes that are happening. >> Uh huh. >> We're a customer and one kind of knows that's pretty biased on it. We've seen the success ourselves, but you guys have a community. Everywhere you go, you're seeing Amazon as they take more territory down. Public Cloud originally, and now Enterprise, and Public Cloud, Public Sector Enterprise, Public Cloud. Each kind of wave of territory that Amazon goes in to Amazon Web Services, is a huge community. >> Yeah. >> And so that's another element. I mean Public Sector Summit last year it felt like Reinvent. So this years going to be bigger. >> Yeah. We had 65 hundred plus people attend last year, just in the Washington DC area and we've also expanded that program now and we are taking our Public Sector Summit specifically for government education non-profit around the world. So this year we will be in Brussels, and Camber, Australia. We have great adoption in Australia as well with the government there. In Singapore, Ottawa. So we're really expanding quite a bit and helping governments around the world to adopt. >> So if that's a challenge, how are you going to handle that because you guys have always been kind of with Summits. Do you coattail Summits? Do you go separate? >> No. We go separate. We actually have the Public Sector Summits we take the experience of our technology to government towns that wouldn't typically get a Summit. So for instance here in the United States of course, San Francisco and New York there's a lot of commercial businesses. We have our big Summits there, but there's not as much commercial business here in Washington DC, so really Public Sector takes the lead here. And then we focus on some of the things that really are most important to our Public Sector customers. Things like, procurement and acquisition. Things like the security and compliance that's so critical in the government sector. And then also, we do a really careful job of curating our customers, because we know that our government customers want to hear from each other. They want to hear from people who are blazing a trail within the Public Sector. They don't necessarily want to hear about what we want to say. They want to hear what their peers are doing with the technology. So last year, we had over a hundred of our Public Sector customers speaking to each other about what they were doing with the Cloud. >> And I find that's impressive. I actually commented on the Cube that week that it's interesting you let the customers do the talking. I mean, that's the best ultimate sign of success and traction. >> Yeah. And the great thing is, you know I've worked in other places in the Public Sector and government customers can be kind of shy about talking about what they're doing. You know, there are very motivated to just keep things going calmly, quietly, you know get their jobs done. But I think... >> Well, it doesn't hurt when you have the top guy at the CIA say, "Best decision we've ever made." "It's the most innovative thing we've ever done." I mean talk about being shy. >> Yeah. >> That's the CIA, by the way. That's the CIA. And we've also had, people like NASA JPL who've been very outspoken. Tom Soderstrom said that it was conservatively 1/100th of the cost of what it would have been if he had built out the infrastructure himself to build the infrastructure for his Mars landing. I mean that kind of... >> It just keeps giving. You lower prices. Okay I got to change gears, because a couple things that I've observed to every Reinvent, as being a customer and I think I've used Amazon I first came out as an entrepreneur. (inaudible) had no URL support, but that's showing my age. (Tricia laughs) But, here's the thing, you guys have enabled customers to solve problems that they couldn't solve in the past. >> (Tricia) Right. >> You mentioned NASA and then a variety of other (inaudible). But you guys are also in Public Sectors specifically are doing new things. New problems that no ones ever seen before. And society, entrepreneurship, diversity inclusion, education, non-profits. You don't think of Gov Cloud and Public Sector; you think non-profits, education. So it's kind of these sectors that are coming together. This is a new phenomenon. Can you talk and explain the dynamic behind that and the opportunity? >> Sure. I love to hear the stories of what our customers are doing when they really are tackling a problem that no one had thought of before. So for instance, at Reinvent this year, one of our Public Sector customers who spoke was Thorne. And they are using AI to crawl the dark web and help find people who are trafficking children in human trafficking, and that's a great use of AI and that's the kind of thing. It also helps our public servants because it helps to make police officers' jobs more effective. So of course we know that police officers, there are never enough police officers to go around. There's never enough detectives to look into everything that they need to and this makes them so much more effective to make the world a safer, better place. I also love some of the things about educational outcomes. Ivy Tech Community College is one of our great community college customers. And their using big data analysis to put together all of the different data sets that they have about their students and identify who might be at risk of failing a class 10 days into the semester so that they can help intervene with those students. >> Where was that class when I needed it? >> I know. >> Popup and say, "Hey homework time." >> I mean it really is looking at what kind of issues that they're having very early on with attendance, with different behavioral things. >> A great example at Reinvent with the California Community College system. That was a very interesting way. He was up there bragging like it was nobody's business. >> Yeah, and I think the community colleges that really goes into this idea of we're trying to expand opportunity for a wide-range of people. You might think of computer scientists as that's going to be all the Carnegie Mellon and Stanford and MIT people. And of course those are great contributors to computer science, but the fact is that computer science is so critical in so many aspects of life and in so many different kinds of careers. We know that one of the limiters to our own growth is going to be the talent that we have available to take advantage of the technology. We've been really working hard to expand opportunity for a wide-range of people, so that any smart person with an idea, can be using our technology, that's part of what's behind building the AWS Educate Program, which is a program to offer free computer science training to any university student or college student anywhere in the world. >> So it's a program you guys are doing? >> (Tricia) This is a program we are doing, >> What's it called again? >> AWS Educate. And it's a program that offers free credits to use AWS to any student who is enrolled in any kind of university or college anywhere around the world. >> That's a gateway drug to Cloud computing. >> Absolutely. >> Free resources. >> Yeah, and we're giving them a training path so that they can... >> So they want to write some code, or whatever they want to do. >> Yeah, and they can take different paths and learn. Okay, I want to learn a data science pathway, so I'm going to go that way. I want to learn a websites pathway. And they can go through things and build a portfolio of projects that they've actually built. >> So can they tap into some of the AWS AI tools too? >> They can tap into a wide range of tools and they have different levels of tiers of credits that they get, so it's a really great program to really open up Cloud computing. >> Now is there any limitations on that? What grade levels, is it college and above? >> Actually at Reinvent we just opened it up to students 14 and above. >> (John) Beautiful. That's awesome. >> And we also have a program called... >> How do they prove they're a student? >> Having a school, an EDU email address, or their school being registered through the program. >> (John) Okay, that's awesome. >> And then we also have another program called We Power Tech, and that really is a program to help open up the talent pool again to women to underserved communities, to people of different ethnic backgrounds who might not see themselves in technology because they don't see themselves as computer programmers on TV or whatever. >> Or they don't see their peer group in there, or some sort of might be an inclusion issue. >> Right and we're looking at if you take educate and We Power Tech, we're looking at that full pipeline of talent all the way from kids who are deciding should I pursue computer science or not, all the way through to professionals and getting them to try to stay in technology. >> So you guys are legit on this. You're not going to just check the box and focus on narrow things. A lot of companies do that, where they go oh we're targeting young girls or women. You guys are looking at the spectrum broader. >> Yep. And we're really looking at different communities and helping people to find their community in technology so that they can find supportive networks and also find people to mentor them or find people to mentor who are elsewhere. >> How big of a problem is it right now in today's culture and in the online culture to find peers and friends to do work like this? Because it just doesn't seem to me like there's been any innovation in online message groups. Seems like so 30 years ago. (Tricia laughs) >> Yeah. I think it is tough and I think there are somethings that we're trying to break through. For instance, a lot of the role models out there are the same people over and over again. We're trying to find new role models. And we find that through our customers. We find customers who are doing interesting work and we're trying to cultivate their voice and help put them on stage. >> New voices because it's new things. Machine learning, these are new disciplines. Data science across the board. >> Yeah, and one of the things that I love about the technology is it really is has democratizing affect. If you have an idea, you can make that idea happen for very little money, with just your ingenuity and your ability to stick to it. >> I got to ask you the hard question. Shouldn't be hard for you, but Amazon is gritty. It's been called gritty by me, hustling, but they're very good with their money. They don't really waste a lot in marketing. >> Yeah we're frugal. >> Very frugal, but you're very efficient, so I got to ask your favorite gorilla marketing technique. Cause you guys do more with less. >> (Tricia) We do. >> Once been criticized in Wired magazine. I remember reading years ago about they were comparing the Schwag bag to Reinvent. (Tricia laughs) Google almost gave out phones. It's kind of like typical reporter, but my point is you guys spend your money on education to engineers. You don't skip on that, but you might not put the flair onto an event, but now you guys are doing it. >> I think there are two things. So one of them is the aesthetic of our events. We typically do have a very stripped down aesthetic and we've made frugal look cool. I think that's one of the things I learned when I came here was go ahead and have the concrete floor and put quotes from customers there instead of paying to carpet it. So don't waste money on things that don't add value that's one of the core tenants of what we do in marketing. >> Get a better band instead of the rug. You guys have always had great music. >> We do always have great music. >> Tricia, tell me about your favorite program or project you've done a lot over the years. Pick your favorite child. What's your favorite? You have a lot of great stuff going on. Do you have a favorite? >> I think that my favorite is probably the City on a Cloud Innovation Challenge which is something we've done every year for the last four years. And we really went and asked cities, "Tell us what you're doing with our technology." Because we weren't sure what they were doing cause it's not very expensive for cities to run on us. We found that they were doing incredible things. They were doing water monitoring in their cities to help improve the quality of life of their citizens. They were delivering education more effectively. They were helping their transportation run in a more effective way. New York City Department of Transportation was doing really cool citizen facing apps to help them manage their transportation challenges and also cities all around the world. We've had people put in things about garbage management in Jerusalem and about lighting management in a Japanese city. We've had all kinds of really interesting stories come out and I just love hearing what the customers are doing and this year we added a Dream Big category where we said, "If you had the money, what would "you do with technology in your city?" and we've been really thrilled to be able to offer grants and fund some of those things to help cities get started. >> That's awesome. Not only is it engaging for them to engage with you through the program, it's inspirational. The use cases are everything from IOT to every computer. >> Yeah and we've also had partners submit as well, and we've learned about things like parking applications that cities are putting in place to help their citizens find better parking or all kinds of really interesting. How to keep track of the tree and do a tree census in their cities. Things like that. >> Maybe I'll borrow that and give you credit for it as a Cube question. What would you do if you had unlimited money? >> Exactly. (John laughs) Well the great part is that most of the cities find out that they can do what they want to do with very little money. They think it's going to be millions of dollars and then they realize, "Oh my gosh, it's going to be hard "for me to spend this 50 thousand dollar grant "because it doesn't cost that much." >> That's awesome and you got a big event coming up in June. Public Sector Summit again. Any preview on that? Any thing you can share? I'm sure it's a lot of things up in the air. >> A lot of really cool things. We are very excited to have some of our great customers on stage again. We're also this year going to have a pre day where we're going to feature Air and Space workloads on AWS. So that's going to be really interesting. I think we're going to have Blue Origin there and we're going to talk about what it's going to take to get to the next planet. >> And certainly that's beautiful for Cloud and also a huge robotics trend. People love to geek out on space related stuff. >> Yep. >> Awesome. Well the Cube will be there. Any numbers? Is it going to be the same location? >> It's going to be the same location at the Convention Center June 20th and 21st. We're going to have boot camps and certification labs and all that kind of stuff. I expect we'll grow again, so definitely more than seven thousand people. >> How big was the first one? >> Oh my gosh, the first one was in a little hotel conference room. I think there were a hundred and 50 people there. (Tricia laughs) >> Sounds like Reinvent happening all over again. We've seen this movie before. >> (Tricia) Yep. >> Tricia, thanks so much for coming on the Cube here. In the headquarters of Amazon Web Services Public Sector Summit in Washington DC. We're in Arlington, Virginia, right next to the nation's capital. I'm John Furrier. Thanks for watching. (techno music)
SUMMARY :
It's Cube conversations with John Furrier. I'm John Furrier host of the Cube. You got so many things going on. (Tricia laughs) Competitive, like to have fun. be the place to go to for technology for the government, to be a marketer for Amazon, but if you think about it, We've seen the success ourselves, And so that's another element. and helping governments around the world to adopt. So if that's a challenge, how are you going to handle that So for instance here in the United States I mean, that's the best ultimate sign And the great thing is, you know I've worked "It's the most innovative thing we've ever done." of the cost of what it would have been But, here's the thing, you guys have enabled customers and the opportunity? and that's the kind of thing. I mean it really is looking at what kind of issues A great example at Reinvent with the We know that one of the limiters to our own growth And it's a program that offers free credits to use AWS Yeah, and we're giving them a training path So they want to write some code, so I'm going to go that way. of credits that they get, so it's a really great to students 14 and above. That's awesome. or their school being registered through the program. We Power Tech, and that really is a program Or they don't see their peer group in there, of talent all the way from kids who are deciding You guys are looking at the spectrum broader. and also find people to mentor them and in the online culture to find peers and friends For instance, a lot of the role models out there Data science across the board. Yeah, and one of the things that I love I got to ask you the hard question. so I got to ask your favorite gorilla marketing technique. the Schwag bag to Reinvent. that's one of the core tenants of what we do in marketing. Get a better band instead of the rug. You have a lot of great stuff going on. and also cities all around the world. Not only is it engaging for them to engage with you that cities are putting in place to help their citizens Maybe I'll borrow that and give you credit for it and then they realize, "Oh my gosh, it's going to be hard That's awesome and you got a big event coming up in June. So that's going to be really interesting. People love to geek out on space related stuff. Is it going to be the same location? It's going to be the same location Oh my gosh, the first one was We've seen this movie before. right next to the nation's capital.
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Lowell Anderson, Amazon Web Services Inc - #AWS - #theCUBE
live from san jose in the heart of Silicon Valley it's the cube covering AWS summit 2016 hey welcome back everyone we are live in Silicon Valley for AWS Amazon Web Services summit in Silicon Valley this is the cube silicon angles flagship program we go out to the events and extract the signal from the noise i'm john for with my co-host Lisa Martin our next guest is Lowell anderson senior manager product marketing of AWS Amazon Web Services welcome to the cube thanks for having me it's great to be here first time cube alumni welcome to the cube alumni list love to get you on because you know you're in the product team and you're in go to market as well as you gotta look into the product suites and one of the things that's been super impressive of AWS over the years since I've been following you guys for a decade since you started in the cube of the past four years is the tsunami of product releases the cadence of jesse's law I call it yeah and Amazon's law which is just constant slew of releases more and more every time not just reinvented yeah you get the summit's which are exploding right there were tiny right years ago right got new york and here what's what's coming out now what's the secret sauce how do you guys do it and give us some insight into what's what's happening here well you know for us innovations in our blood it's a part of our DNA it's what we do we're really except to over 460 new services and features and we'll hit over a thousand this year of new services and features launched compared to last year when we hit like 720 I think something about in that range so the innovation train train keeps going and you know the way we do it is we number one we really focus on our customers one of the benefits of the cloud is that we can innovate and roll out changes really rapidly for them so just that the whole cloud environment allows us to innovate very quickly and very rapidly so that that's exciting and you see that in just a number of releases that we think that I just asked the previous guest on how do you explain the Phenom that is AWS and you know Andy Jassy went to business school the same year us I did and back then the competitive strategy ethos was built some proprietary technology build a fence protected with guards and guys with guns and old you fold the line yeah with open source though the new model is you can't do that anymore so there's one the open source is now a Tier one citizen right and two there's no real walls to build around proprietary technology so the name of the game is speed yeah it's all about speed and the cloud really enables that agility that's one of the biggest benefits that our customers talk about is how freeing up breaking down the walls of your data center effectively so that now your compute and your analytics and your storage expand beyond the walls of that building as rapidly as possible and and the use of open source as you measured I mean we're we're big proponents of open source we have a lot of open source services that that we support as well and and trying to help the developer community really bring those types of technologies to the cloud and enable that's a big part of our success as well it's clear that the competitive strategy game in this new world that Andy and the team are executing is really just more features faster than the competition there is kind of an arms race going on but that is the open source game so with that what is the are the big announcements here obviously this show is much more developer focused yeah yes more getting get getting the weeds breakout sessions one of the key goods that are being talked about here down here in Silicon Valley we really wanted to bring some more technical topics to the table and talk in that vein talk about a couple really key areas around focused around big data and what we're doing to help enable both small and large enterprises use data across their companies in in a more and to develop more competitive applications and make it cheaper make it easier to use and make it more performant than they could possibly imagine without the cloud so using big data is one of the key themes of the conference that we had today and then the other thing that we wanted to talk about was this movement from how we've been architecting services our applications in the past from being based on server to using server list which is really a whole new architectural concept that's allowing our customers to build applications in ways that they could never do before and do it at a cost that they could never make feasible in the past there's some great examples of customer successes that dr. Matt would talked about in the keynote one redfin I think we've all in orcutt have experiences with buying and selling homes but i loved how we talked about friends don't let friends build data centers that in the future it's most organizations are going to run their own data centers are not going to run their own Dana centers and move to AWS benefits like becoming data-driven big data the more users more data more insight you also talked about some of the things coming up you mentioned it to about building with services versus building with servers talk to us about some of the if you could spend a little bit on some of those examples one that particularly spoke to me was what alumina is doing and germs of genome sequencing I got my masters in biological sciences a long time ago and that wasn't even a thought back then or certainly was a massively expensive Todd was a little bit more about how alumina is doing that with AWS and scaling at cost to really facilitate breakthroughs they're saving lives right right well you know that's an exciting example because people that weren't able to see the keynote alumina is the largest genomic secant sequencing company in the world and they've really been able to implement a new architecture that's brought genomic sequencing from an industry that was done you know just for very specific scientific purposes to now something that can be done all over the world to support disease research yeah and and its really the power of big data that's made that happen and the reason they selected AWS for that is really just the breadth and depth of the big data services that we provide along with the global deployments that we support with genomic data they mentioned that for many many many many countries in the world they don't want that genomic data to move outside the boundaries of their specific geographic region and so sensitivity eight AWS is one of the few very few cloud providers that has that level of geographic specificity so you can keep the data within that specific compliance issues as well with that too lots of compliance issues of course genomic sequencing lots of federal and health care and HIPAA type requirements surrounding all of that type of information that AWS with our focus on security you know is able to achieve so so number one you know it's this Geographic capability which is a lot of luminarias lee deploy this in a global way but second it's really just a depth of services that we offer whether it's the data warehousing using redshift whether it's the ability to process that data at scale on Hadoop using EMR whether it's the ability to then deliver that data across the world and visualize it and upload it from all those different genomic machines that they've that they put into their individual customers research facilities all of that is capability that AWS is able to deliver to them at a cost I think one of the things he talked about they were looking for I think a hundred percent reduction or a hundred times reduction in cost over trying to do this themselves and and we've achieved that help working together with them you know they've been able to achieve that well I got to get your thoughts on the hybrid cloud because you know I'll see Amazon gets was traditionally pigeon-holed as just public cloud the lines are blurring clearly the success you guys are having it's been moving into the enterprise obviously the CIA delia beat IBM on that was a again different instance in the gov cloud but again in the enterprise deals you're seeing yeah it's up against the Oracles and the IBM's yeah what and they're all talking hybrid yeah yeah how are you guys are dressing it from a product standpoint how do you talk to a customer says hey amazon slow down i love you guys but yeah we need a hybrid on-premise solution yeah that's great great great question i think you know first of all I would say that what we've always said at AWS is really in the fullness of time we expect that you know no Enterprise is really going to want to run their own data center and so we still see that as the end vision that that's that we're gonna achieve in the long run and that most of our customers want to achieve in the long run as well but a critical conversations that they have what are their requirements and you got here is it migration of data yeah that's it so that said you know there's there's a lot of work to do between now and this in this long-term vision and so you know a few of those things that need to be addressed like data migration and we're working really hard to help enterprises move data up into the cloud it seems like it'd be a simple thing right you you take a picture you upload it to dropbox what why is that so hard but when you're talking about terabytes of data that have been in the corporate data centers with applications for years and years and years moving that volume of data up to the cloud is a significant about moving back to the enterprise and then vice versa again making it available for them to use and to and to move back and forth is a critical component so we've done a lot of work on a specific set of features and capabilities to make that happen amazon direct connect or AWS direct connect is one of those services that allows our enterprise customers to establish a high bandwidth connection to AWS regions so that they can move data back and forth the interconnect or to direct connect not going through the internet yes direct connect allows them to leverage private backhaul to establish a really high bandwidth connection and so we'd curity wise alone that's a big deal absolutely it is and then earlier or last year we announced amazon s3 transfer acceleration which is a service that allows them to utilize our backhaul to actually accelerate the upload of data into s3 before you has to use the internet to upload data to s3 and now what we've done is really extended that down to customers where if we can accelerate the transfer of their data to s3 will do that using our backhaul network for them so the next question on top that compounds the problem with data which you guys are solving and because this is I agree is a big challenge for enterprise customers IOT just complicates the hell out of it so yeah that's all about moving data around putting computer where the edges yeah this whole edge of the network definition really plays into some of the train around serverless concepts that you were mentioning earlier how does that relate to the data equation yeah so a couple of things let's touch on IOT for so I OT brings a whole new level of complexity in terms of the number of devices and the distribution of data that you need to bring up into the cloud and so we released this service we call AWS IOT last year at reinvent and what that does is it makes it really easy for customers to acquire data from billions of devices that might be generating trillions of messages at a time and when you think about IOT devices it becomes almost more complex because these devices may or may not be online all the time they may not have a high bandwidth connection they may not have the processing capability on the device itself to be able to update and optimize and do a lot of complex computing so you need a specialized service that can work with those devices when there's intermittent connections pull very small messages from those devices and ingest them on a huge huge scale and so aw SI io t is a service that does that allows our customers to ingest those billions of messages and then connect them to AWS endpoints big data services like red shift and s3 and Kinesis and lambda to process that data and generate applications that could never really be conceived before and today i thought i thought that the the whole discussion from iRobot was super interesting about how they're using AWS IOT to connect their what they call their home robots it's as you know their Roomba vacuum devices to the cloud and and really enable a whole new set of applications and vision for the connected home really interesting stuff enabled by the clouds well before at least answer question I just want to quote been Keogh who was with iRobot his analyst over there Sarah scientist transition I won't get your reaction to maybe Lisa you can chime in he just tweeted transition to the cloud colon treat servers like cattle not pets transition to server less cloud architecture yeah Crete servers like roaches wow that's a pretty bold statement yeah yeah it is but note note not a pet yeah I don't cuddle like a roach Amy's not not cattle it's roaches put the roaches out so taught some mean serverless sure caring servers to roaches let's talk about that that's that yeah let's talk about the evolution a little bit i mean if you went back you know a few years back to when i was writing software as a graduate from from college when you wanted to start off a project first thing you had to do was go buy a server have it delivered find a place to put it plug it in cola network guys get aboard cole router your security what you had it all plugged in you had to put the operating system on it and then you could put your development system on it and then you could finally get started to be months later before you could actually get the project started and it seems strange to even talk about it now but back then this was a a key thing that that limited our ability to start projects forget the cost yeah it's the time and then when you when you finally got it done and you release the application and you wanted to scale it you had to buy more servers and put them in the racks and figure out where to put them and so this just slowed everything down and so when we move to the cloud and we got the ability to lease or really rent servers in the cloud it took away a lot of the hardware aspects of that but still when you had to scale you still needed to provision more servers and you still needed to maintain and patch those operating systems in that software stack and so now what's happening with serverless and with services like lambda is all that goes away now it doesn't mean there aren't servers under the hood of course lambda has lots of servers under the hood that are cranking away and implementing your code at lightning speed but the difference is is you don't have to manage them anymore you don't have to think about them you don't have to worry about them and so with lambda all you do is is load your code up into the cloud it's executed instantaneously when you need it to be executed it scales on demand so as your application scale we can scale the number of lamb functions in parallel to execute your code depending on the load that you're putting on it and you only pay when that code is actually running so you're no longer paying every month for those servers that are sitting in that room whether you're using them or not so we've talked a lot about the services a tremendous amount of services that that AWS is offering compared with the three that you started with ten years ago we've talked about hybrid cloud the opportunities there enterprise in fact you're CTO just last week in London was talking about the challenges with enterprise are really kind of the shift that they want to help customers grow through a lot of capabilities a lot of speeds and feeds what's the the message brother who's the target audience as we wrap up here who are you selling these services to within organizations as we see the empowerment moving from IT to the c-suite two lines of business who are you going after to share with them and get them to come on board as customers whether it's Enterprise yeah yeah I think that's a really good question and it speaks a little bit to our evolution of as a company as well wear when AWS started over 10 years ago really focused on our developer messaging but what we've seen is the just the impact of the cloud is so significant that across the entire suite of different whether that's executives whether that's IT managers whether that's developers there's a significant value proposition that that really at every level across the organization high level of interest and so we're starting to see I think you saw today just across all sizes of companies across all industries and in even within government and an education and public sector a strong interest in motion there's really no industry or government type of agency that's not you know right now looking at not just are they going to move to the cloud but how quickly can we get to the cloud and so that's that's really expanded the scope gray synopsis that actually what dr. Matt would talked about with how infiltrated amazon is into of all the industries big in public sector big and startups born in the cloud now getting to be big and enterprise yeah so low we got one minute left I want to get your thoughts on as an insider at Amazon I'll see you out in the field here you talk to customers in the product marketing you have to look at that 20 mile stare in the marketplace but also talk to the folks internally engineering product management or talk about the coolest things that are going on right now in AWS that people should know about is the machine learning is it lamb does it rip yeah Reds what's the fastest growing what's the coolest tech yeah what is what are the jewels on the table right now that we should look at it and then explore and discover more about you touch on so many cool things I mean the fastest growing service now today is Aurora Aurora is our own my sequel database engine that runs on RDS and it's responsive that's been tremendous it it really offers enterprise-class database capability at a tenth the cost of on-premises solution so that's been that's really our fastest-growing service now it's really exciting in terms of this other stuff that we're just seeing tremendous excitement about you mentioned machine learning predictive analytics a lot of the work that we've been doing at amazon it's been part of our history at amazon for a long time mike says that was thing all everyone wants that right right right so machine learning of course is is something that you know we're gonna continue to see significant cars coming soon I don't know about flying cars it's certainly not on our roadmap that I'm aware of but you know who knows what Steve or what Jeff is working on right now so but we don't have flying cars on our super exciting I'm yeah I'm sure this is but it's again it's a software driven world mark injury since new thesis is not software eating the world but software powering the world and I think that's a whole nother concept its patents you know it's a global economy so a lot of great stuff always a great surprise to see the coolness yeah they did to us the new stuff thanks so much for sharing thank you in the cube this is the cube bringing you all the goodness of AWS here at if your summit in Silicon Valley I'm John Ford Lisa Martin you're watching the q
SUMMARY :
the cube alumni list love to get you on
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Breaking Analysis: Databricks faces critical strategic decisions…here’s why
>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Spark became a top level Apache project in 2014, and then shortly thereafter, burst onto the big data scene. Spark, along with the cloud, transformed and in many ways, disrupted the big data market. Databricks optimized its tech stack for Spark and took advantage of the cloud to really cleverly deliver a managed service that has become a leading AI and data platform among data scientists and data engineers. However, emerging customer data requirements are shifting into a direction that will cause modern data platform players generally and Databricks, specifically, we think, to make some key directional decisions and perhaps even reinvent themselves. Hello and welcome to this week's wikibon theCUBE Insights, powered by ETR. In this Breaking Analysis, we're going to do a deep dive into Databricks. We'll explore its current impressive market momentum. We're going to use some ETR survey data to show that, and then we'll lay out how customer data requirements are changing and what the ideal data platform will look like in the midterm future. We'll then evaluate core elements of the Databricks portfolio against that vision, and then we'll close with some strategic decisions that we think the company faces. And to do so, we welcome in our good friend, George Gilbert, former equities analyst, market analyst, and current Principal at TechAlpha Partners. George, good to see you. Thanks for coming on. >> Good to see you, Dave. >> All right, let me set this up. We're going to start by taking a look at where Databricks sits in the market in terms of how customers perceive the company and what it's momentum looks like. And this chart that we're showing here is data from ETS, the emerging technology survey of private companies. The N is 1,421. What we did is we cut the data on three sectors, analytics, database-data warehouse, and AI/ML. The vertical axis is a measure of customer sentiment, which evaluates an IT decision maker's awareness of the firm and the likelihood of engaging and/or purchase intent. The horizontal axis shows mindshare in the dataset, and we've highlighted Databricks, which has been a consistent high performer in this survey over the last several quarters. And as we, by the way, just as aside as we previously reported, OpenAI, which burst onto the scene this past quarter, leads all names, but Databricks is still prominent. You can see that the ETR shows some open source tools for reference, but as far as firms go, Databricks is very impressively positioned. Now, let's see how they stack up to some mainstream cohorts in the data space, against some bigger companies and sometimes public companies. This chart shows net score on the vertical axis, which is a measure of spending momentum and pervasiveness in the data set is on the horizontal axis. You can see that chart insert in the upper right, that informs how the dots are plotted, and net score against shared N. And that red dotted line at 40% indicates a highly elevated net score, anything above that we think is really, really impressive. And here we're just comparing Databricks with Snowflake, Cloudera, and Oracle. And that squiggly line leading to Databricks shows their path since 2021 by quarter. And you can see it's performing extremely well, maintaining an elevated net score and net range. Now it's comparable in the vertical axis to Snowflake, and it consistently is moving to the right and gaining share. Now, why did we choose to show Cloudera and Oracle? The reason is that Cloudera got the whole big data era started and was disrupted by Spark. And of course the cloud, Spark and Databricks and Oracle in many ways, was the target of early big data players like Cloudera. Take a listen to Cloudera CEO at the time, Mike Olson. This is back in 2010, first year of theCUBE, play the clip. >> Look, back in the day, if you had a data problem, if you needed to run business analytics, you wrote the biggest check you could to Sun Microsystems, and you bought a great big, single box, central server, and any money that was left over, you handed to Oracle for a database licenses and you installed that database on that box, and that was where you went for data. That was your temple of information. >> Okay? So Mike Olson implied that monolithic model was too expensive and inflexible, and Cloudera set out to fix that. But the best laid plans, as they say, George, what do you make of the data that we just shared? >> So where Databricks has really come up out of sort of Cloudera's tailpipe was they took big data processing, made it coherent, made it a managed service so it could run in the cloud. So it relieved customers of the operational burden. Where they're really strong and where their traditional meat and potatoes or bread and butter is the predictive and prescriptive analytics that building and training and serving machine learning models. They've tried to move into traditional business intelligence, the more traditional descriptive and diagnostic analytics, but they're less mature there. So what that means is, the reason you see Databricks and Snowflake kind of side by side is there are many, many accounts that have both Snowflake for business intelligence, Databricks for AI machine learning, where Snowflake, I'm sorry, where Databricks also did really well was in core data engineering, refining the data, the old ETL process, which kind of turned into ELT, where you loaded into the analytic repository in raw form and refine it. And so people have really used both, and each is trying to get into the other. >> Yeah, absolutely. We've reported on this quite a bit. Snowflake, kind of moving into the domain of Databricks and vice versa. And the last bit of ETR evidence that we want to share in terms of the company's momentum comes from ETR's Round Tables. They're run by Erik Bradley, and now former Gartner analyst and George, your colleague back at Gartner, Daren Brabham. And what we're going to show here is some direct quotes of IT pros in those Round Tables. There's a data science head and a CIO as well. Just make a few call outs here, we won't spend too much time on it, but starting at the top, like all of us, we can't talk about Databricks without mentioning Snowflake. Those two get us excited. Second comment zeros in on the flexibility and the robustness of Databricks from a data warehouse perspective. And then the last point is, despite competition from cloud players, Databricks has reinvented itself a couple of times over the year. And George, we're going to lay out today a scenario that perhaps calls for Databricks to do that once again. >> Their big opportunity and their big challenge for every tech company, it's managing a technology transition. The transition that we're talking about is something that's been bubbling up, but it's really epical. First time in 60 years, we're moving from an application-centric view of the world to a data-centric view, because decisions are becoming more important than automating processes. So let me let you sort of develop. >> Yeah, so let's talk about that here. We going to put up some bullets on precisely that point and the changing sort of customer environment. So you got IT stacks are shifting is George just said, from application centric silos to data centric stacks where the priority is shifting from automating processes to automating decision. You know how look at RPA and there's still a lot of automation going on, but from the focus of that application centricity and the data locked into those apps, that's changing. Data has historically been on the outskirts in silos, but organizations, you think of Amazon, think Uber, Airbnb, they're putting data at the core, and logic is increasingly being embedded in the data instead of the reverse. In other words, today, the data's locked inside the app, which is why you need to extract that data is sticking it to a data warehouse. The point, George, is we're putting forth this new vision for how data is going to be used. And you've used this Uber example to underscore the future state. Please explain? >> Okay, so this is hopefully an example everyone can relate to. The idea is first, you're automating things that are happening in the real world and decisions that make those things happen autonomously without humans in the loop all the time. So to use the Uber example on your phone, you call a car, you call a driver. Automatically, the Uber app then looks at what drivers are in the vicinity, what drivers are free, matches one, calculates an ETA to you, calculates a price, calculates an ETA to your destination, and then directs the driver once they're there. The point of this is that that cannot happen in an application-centric world very easily because all these little apps, the drivers, the riders, the routes, the fares, those call on data locked up in many different apps, but they have to sit on a layer that makes it all coherent. >> But George, so if Uber's doing this, doesn't this tech already exist? Isn't there a tech platform that does this already? >> Yes, and the mission of the entire tech industry is to build services that make it possible to compose and operate similar platforms and tools, but with the skills of mainstream developers in mainstream corporations, not the rocket scientists at Uber and Amazon. >> Okay, so we're talking about horizontally scaling across the industry, and actually giving a lot more organizations access to this technology. So by way of review, let's summarize the trend that's going on today in terms of the modern data stack that is propelling the likes of Databricks and Snowflake, which we just showed you in the ETR data and is really is a tailwind form. So the trend is toward this common repository for analytic data, that could be multiple virtual data warehouses inside of Snowflake, but you're in that Snowflake environment or Lakehouses from Databricks or multiple data lakes. And we've talked about what JP Morgan Chase is doing with the data mesh and gluing data lakes together, you've got various public clouds playing in this game, and then the data is annotated to have a common meaning. In other words, there's a semantic layer that enables applications to talk to the data elements and know that they have common and coherent meaning. So George, the good news is this approach is more effective than the legacy monolithic models that Mike Olson was talking about, so what's the problem with this in your view? >> So today's data platforms added immense value 'cause they connected the data that was previously locked up in these monolithic apps or on all these different microservices, and that supported traditional BI and AI/ML use cases. But now if we want to build apps like Uber or Amazon.com, where they've got essentially an autonomously running supply chain and e-commerce app where humans only care and feed it. But the thing is figuring out what to buy, when to buy, where to deploy it, when to ship it. We needed a semantic layer on top of the data. So that, as you were saying, the data that's coming from all those apps, the different apps that's integrated, not just connected, but it means the same. And the issue is whenever you add a new layer to a stack to support new applications, there are implications for the already existing layers, like can they support the new layer and its use cases? So for instance, if you add a semantic layer that embeds app logic with the data rather than vice versa, which we been talking about and that's been the case for 60 years, then the new data layer faces challenges that the way you manage that data, the way you analyze that data, is not supported by today's tools. >> Okay, so actually Alex, bring me up that last slide if you would, I mean, you're basically saying at the bottom here, today's repositories don't really do joins at scale. The future is you're talking about hundreds or thousands or millions of data connections, and today's systems, we're talking about, I don't know, 6, 8, 10 joins and that is the fundamental problem you're saying, is a new data error coming and existing systems won't be able to handle it? >> Yeah, one way of thinking about it is that even though we call them relational databases, when we actually want to do lots of joins or when we want to analyze data from lots of different tables, we created a whole new industry for analytic databases where you sort of mung the data together into fewer tables. So you didn't have to do as many joins because the joins are difficult and slow. And when you're going to arbitrarily join thousands, hundreds of thousands or across millions of elements, you need a new type of database. We have them, they're called graph databases, but to query them, you go back to the prerelational era in terms of their usability. >> Okay, so we're going to come back to that and talk about how you get around that problem. But let's first lay out what the ideal data platform of the future we think looks like. And again, we're going to come back to use this Uber example. In this graphic that George put together, awesome. We got three layers. The application layer is where the data products reside. The example here is drivers, rides, maps, routes, ETA, et cetera. The digital version of what we were talking about in the previous slide, people, places and things. The next layer is the data layer, that breaks down the silos and connects the data elements through semantics and everything is coherent. And then the bottom layers, the legacy operational systems feed that data layer. George, explain what's different here, the graph database element, you talk about the relational query capabilities, and why can't I just throw memory at solving this problem? >> Some of the graph databases do throw memory at the problem and maybe without naming names, some of them live entirely in memory. And what you're dealing with is a prerelational in-memory database system where you navigate between elements, and the issue with that is we've had SQL for 50 years, so we don't have to navigate, we can say what we want without how to get it. That's the core of the problem. >> Okay. So if I may, I just want to drill into this a little bit. So you're talking about the expressiveness of a graph. Alex, if you'd bring that back out, the fourth bullet, expressiveness of a graph database with the relational ease of query. Can you explain what you mean by that? >> Yeah, so graphs are great because when you can describe anything with a graph, that's why they're becoming so popular. Expressive means you can represent anything easily. They're conducive to, you might say, in a world where we now want like the metaverse, like with a 3D world, and I don't mean the Facebook metaverse, I mean like the business metaverse when we want to capture data about everything, but we want it in context, we want to build a set of digital twins that represent everything going on in the world. And Uber is a tiny example of that. Uber built a graph to represent all the drivers and riders and maps and routes. But what you need out of a database isn't just a way to store stuff and update stuff. You need to be able to ask questions of it, you need to be able to query it. And if you go back to prerelational days, you had to know how to find your way to the data. It's sort of like when you give directions to someone and they didn't have a GPS system and a mapping system, you had to give them turn by turn directions. Whereas when you have a GPS and a mapping system, which is like the relational thing, you just say where you want to go, and it spits out the turn by turn directions, which let's say, the car might follow or whoever you're directing would follow. But the point is, it's much easier in a relational database to say, "I just want to get these results. You figure out how to get it." The graph database, they have not taken over the world because in some ways, it's taking a 50 year leap backwards. >> Alright, got it. Okay. Let's take a look at how the current Databricks offerings map to that ideal state that we just laid out. So to do that, we put together this chart that looks at the key elements of the Databricks portfolio, the core capability, the weakness, and the threat that may loom. Start with the Delta Lake, that's the storage layer, which is great for files and tables. It's got true separation of compute and storage, I want you to double click on that George, as independent elements, but it's weaker for the type of low latency ingest that we see coming in the future. And some of the threats highlighted here. AWS could add transactional tables to S3, Iceberg adoption is picking up and could accelerate, that could disrupt Databricks. George, add some color here please? >> Okay, so this is the sort of a classic competitive forces where you want to look at, so what are customers demanding? What's competitive pressure? What are substitutes? Even what your suppliers might be pushing. Here, Delta Lake is at its core, a set of transactional tables that sit on an object store. So think of it in a database system, this is the storage engine. So since S3 has been getting stronger for 15 years, you could see a scenario where they add transactional tables. We have an open source alternative in Iceberg, which Snowflake and others support. But at the same time, Databricks has built an ecosystem out of tools, their own and others, that read and write to Delta tables, that's what makes the Delta Lake and ecosystem. So they have a catalog, the whole machine learning tool chain talks directly to the data here. That was their great advantage because in the past with Snowflake, you had to pull all the data out of the database before the machine learning tools could work with it, that was a major shortcoming. They fixed that. But the point here is that even before we get to the semantic layer, the core foundation is under threat. >> Yep. Got it. Okay. We got a lot of ground to cover. So we're going to take a look at the Spark Execution Engine next. Think of that as the refinery that runs really efficient batch processing. That's kind of what disrupted the DOOp in a large way, but it's not Python friendly and that's an issue because the data science and the data engineering crowd are moving in that direction, and/or they're using DBT. George, we had Tristan Handy on at Supercloud, really interesting discussion that you and I did. Explain why this is an issue for Databricks? >> So once the data lake was in place, what people did was they refined their data batch, and Spark has always had streaming support and it's gotten better. The underlying storage as we've talked about is an issue. But basically they took raw data, then they refined it into tables that were like customers and products and partners. And then they refined that again into what was like gold artifacts, which might be business intelligence metrics or dashboards, which were collections of metrics. But they were running it on the Spark Execution Engine, which it's a Java-based engine or it's running on a Java-based virtual machine, which means all the data scientists and the data engineers who want to work with Python are really working in sort of oil and water. Like if you get an error in Python, you can't tell whether the problems in Python or where it's in Spark. There's just an impedance mismatch between the two. And then at the same time, the whole world is now gravitating towards DBT because it's a very nice and simple way to compose these data processing pipelines, and people are using either SQL in DBT or Python in DBT, and that kind of is a substitute for doing it all in Spark. So it's under threat even before we get to that semantic layer, it so happens that DBT itself is becoming the authoring environment for the semantic layer with business intelligent metrics. But that's again, this is the second element that's under direct substitution and competitive threat. >> Okay, let's now move down to the third element, which is the Photon. Photon is Databricks' BI Lakehouse, which has integration with the Databricks tooling, which is very rich, it's newer. And it's also not well suited for high concurrency and low latency use cases, which we think are going to increasingly become the norm over time. George, the call out threat here is customers want to connect everything to a semantic layer. Explain your thinking here and why this is a potential threat to Databricks? >> Okay, so two issues here. What you were touching on, which is the high concurrency, low latency, when people are running like thousands of dashboards and data is streaming in, that's a problem because SQL data warehouse, the query engine, something like that matures over five to 10 years. It's one of these things, the joke that Andy Jassy makes just in general, he's really talking about Azure, but there's no compression algorithm for experience. The Snowflake guy started more than five years earlier, and for a bunch of reasons, that lead is not something that Databricks can shrink. They'll always be behind. So that's why Snowflake has transactional tables now and we can get into that in another show. But the key point is, so near term, it's struggling to keep up with the use cases that are core to business intelligence, which is highly concurrent, lots of users doing interactive query. But then when you get to a semantic layer, that's when you need to be able to query data that might have thousands or tens of thousands or hundreds of thousands of joins. And that's a SQL query engine, traditional SQL query engine is just not built for that. That's the core problem of traditional relational databases. >> Now this is a quick aside. We always talk about Snowflake and Databricks in sort of the same context. We're not necessarily saying that Snowflake is in a position to tackle all these problems. We'll deal with that separately. So we don't mean to imply that, but we're just sort of laying out some of the things that Snowflake or rather Databricks customers we think, need to be thinking about and having conversations with Databricks about and we hope to have them as well. We'll come back to that in terms of sort of strategic options. But finally, when come back to the table, we have Databricks' AI/ML Tool Chain, which has been an awesome capability for the data science crowd. It's comprehensive, it's a one-stop shop solution, but the kicker here is that it's optimized for supervised model building. And the concern is that foundational models like GPT could cannibalize the current Databricks tooling, but George, can't Databricks, like other software companies, integrate foundation model capabilities into its platform? >> Okay, so the sound bite answer to that is sure, IBM 3270 terminals could call out to a graphical user interface when they're running on the XT terminal, but they're not exactly good citizens in that world. The core issue is Databricks has this wonderful end-to-end tool chain for training, deploying, monitoring, running inference on supervised models. But the paradigm there is the customer builds and trains and deploys each model for each feature or application. In a world of foundation models which are pre-trained and unsupervised, the entire tool chain is different. So it's not like Databricks can junk everything they've done and start over with all their engineers. They have to keep maintaining what they've done in the old world, but they have to build something new that's optimized for the new world. It's a classic technology transition and their mentality appears to be, "Oh, we'll support the new stuff from our old stuff." Which is suboptimal, and as we'll talk about, their biggest patron and the company that put them on the map, Microsoft, really stopped working on their old stuff three years ago so that they could build a new tool chain optimized for this new world. >> Yeah, and so let's sort of close with what we think the options are and decisions that Databricks has for its future architecture. They're smart people. I mean we've had Ali Ghodsi on many times, super impressive. I think they've got to be keenly aware of the limitations, what's going on with foundation models. But at any rate, here in this chart, we lay out sort of three scenarios. One is re-architect the platform by incrementally adopting new technologies. And example might be to layer a graph query engine on top of its stack. They could license key technologies like graph database, they could get aggressive on M&A and buy-in, relational knowledge graphs, semantic technologies, vector database technologies. George, as David Floyer always says, "A lot of ways to skin a cat." We've seen companies like, even think about EMC maintained its relevance through M&A for many, many years. George, give us your thought on each of these strategic options? >> Okay, I find this question the most challenging 'cause remember, I used to be an equity research analyst. I worked for Frank Quattrone, we were one of the top tech shops in the banking industry, although this is 20 years ago. But the M&A team was the top team in the industry and everyone wanted them on their side. And I remember going to meetings with these CEOs, where Frank and the bankers would say, "You want us for your M&A work because we can do better." And they really could do better. But in software, it's not like with EMC in hardware because with hardware, it's easier to connect different boxes. With software, the whole point of a software company is to integrate and architect the components so they fit together and reinforce each other, and that makes M&A harder. You can do it, but it takes a long time to fit the pieces together. Let me give you examples. If they put a graph query engine, let's say something like TinkerPop, on top of, I don't even know if it's possible, but let's say they put it on top of Delta Lake, then you have this graph query engine talking to their storage layer, Delta Lake. But if you want to do analysis, you got to put the data in Photon, which is not really ideal for highly connected data. If you license a graph database, then most of your data is in the Delta Lake and how do you sync it with the graph database? If you do sync it, you've got data in two places, which kind of defeats the purpose of having a unified repository. I find this semantic layer option in number three actually more promising, because that's something that you can layer on top of the storage layer that you have already. You just have to figure out then how to have your query engines talk to that. What I'm trying to highlight is, it's easy as an analyst to say, "You can buy this company or license that technology." But the really hard work is making it all work together and that is where the challenge is. >> Yeah, and well look, I thank you for laying that out. We've seen it, certainly Microsoft and Oracle. I guess you might argue that well, Microsoft had a monopoly in its desktop software and was able to throw off cash for a decade plus while it's stock was going sideways. Oracle had won the database wars and had amazing margins and cash flow to be able to do that. Databricks isn't even gone public yet, but I want to close with some of the players to watch. Alex, if you'd bring that back up, number four here. AWS, we talked about some of their options with S3 and it's not just AWS, it's blob storage, object storage. Microsoft, as you sort of alluded to, was an early go-to market channel for Databricks. We didn't address that really. So maybe in the closing comments we can. Google obviously, Snowflake of course, we're going to dissect their options in future Breaking Analysis. Dbt labs, where do they fit? Bob Muglia's company, Relational.ai, why are these players to watch George, in your opinion? >> So everyone is trying to assemble and integrate the pieces that would make building data applications, data products easy. And the critical part isn't just assembling a bunch of pieces, which is traditionally what AWS did. It's a Unix ethos, which is we give you the tools, you put 'em together, 'cause you then have the maximum choice and maximum power. So what the hyperscalers are doing is they're taking their key value stores, in the case of ASW it's DynamoDB, in the case of Azure it's Cosmos DB, and each are putting a graph query engine on top of those. So they have a unified storage and graph database engine, like all the data would be collected in the key value store. Then you have a graph database, that's how they're going to be presenting a foundation for building these data apps. Dbt labs is putting a semantic layer on top of data lakes and data warehouses and as we'll talk about, I'm sure in the future, that makes it easier to swap out the underlying data platform or swap in new ones for specialized use cases. Snowflake, what they're doing, they're so strong in data management and with their transactional tables, what they're trying to do is take in the operational data that used to be in the province of many state stores like MongoDB and say, "If you manage that data with us, it'll be connected to your analytic data without having to send it through a pipeline." And that's hugely valuable. Relational.ai is the wildcard, 'cause what they're trying to do, it's almost like a holy grail where you're trying to take the expressiveness of connecting all your data in a graph but making it as easy to query as you've always had it in a SQL database or I should say, in a relational database. And if they do that, it's sort of like, it'll be as easy to program these data apps as a spreadsheet was compared to procedural languages, like BASIC or Pascal. That's the implications of Relational.ai. >> Yeah, and again, we talked before, why can't you just throw this all in memory? We're talking in that example of really getting down to differences in how you lay the data out on disk in really, new database architecture, correct? >> Yes. And that's why it's not clear that you could take a data lake or even a Snowflake and why you can't put a relational knowledge graph on those. You could potentially put a graph database, but it'll be compromised because to really do what Relational.ai has done, which is the ease of Relational on top of the power of graph, you actually need to change how you're storing your data on disk or even in memory. So you can't, in other words, it's not like, oh we can add graph support to Snowflake, 'cause if you did that, you'd have to change, or in your data lake, you'd have to change how the data is physically laid out. And then that would break all the tools that talk to that currently. >> What in your estimation, is the timeframe where this becomes critical for a Databricks and potentially Snowflake and others? I mentioned earlier midterm, are we talking three to five years here? Are we talking end of decade? What's your radar say? >> I think something surprising is going on that's going to sort of come up the tailpipe and take everyone by storm. All the hype around business intelligence metrics, which is what we used to put in our dashboards where bookings, billings, revenue, customer, those things, those were the key artifacts that used to live in definitions in your BI tools, and DBT has basically created a standard for defining those so they live in your data pipeline or they're defined in their data pipeline and executed in the data warehouse or data lake in a shared way, so that all tools can use them. This sounds like a digression, it's not. All this stuff about data mesh, data fabric, all that's going on is we need a semantic layer and the business intelligence metrics are defining common semantics for your data. And I think we're going to find by the end of this year, that metrics are how we annotate all our analytic data to start adding common semantics to it. And we're going to find this semantic layer, it's not three to five years off, it's going to be staring us in the face by the end of this year. >> Interesting. And of course SVB today was shut down. We're seeing serious tech headwinds, and oftentimes in these sort of downturns or flat turns, which feels like this could be going on for a while, we emerge with a lot of new players and a lot of new technology. George, we got to leave it there. Thank you to George Gilbert for excellent insights and input for today's episode. I want to thank Alex Myerson who's on production and manages the podcast, of course Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Siliconangle.com, he does some great editing. Remember all these episodes, they're available as podcasts. Wherever you listen, all you got to do is search Breaking Analysis Podcast, we publish each week on wikibon.com and siliconangle.com, or you can email me at David.Vellante@siliconangle.com, or DM me @DVellante. Comment on our LinkedIn post, and please do check out ETR.ai, great survey data, enterprise tech focus, phenomenal. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis.
SUMMARY :
bringing you data-driven core elements of the Databricks portfolio and pervasiveness in the data and that was where you went for data. and Cloudera set out to fix that. the reason you see and the robustness of Databricks and their big challenge and the data locked into in the real world and decisions Yes, and the mission of that is propelling the likes that the way you manage that data, is the fundamental problem because the joins are difficult and slow. and connects the data and the issue with that is the fourth bullet, expressiveness and it spits out the and the threat that may loom. because in the past with Snowflake, Think of that as the refinery So once the data lake was in place, George, the call out threat here But the key point is, in sort of the same context. and the company that put One is re-architect the platform and architect the components some of the players to watch. in the case of ASW it's DynamoDB, and why you can't put a relational and executed in the data and manages the podcast, of
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Dominique Bastos, Persistent Systems | International Women's Day 2023
(gentle upbeat music) >> Hello, everyone, welcome to theCUBE's coverage of International Women's Day. I'm John Furrier host here in Palo Alto, California. theCUBE's second year covering International Women's Day. It's been a great celebration of all the smart leaders in the world who are making a difference from all kinds of backgrounds, from technology to business and everything in between. Today we've got a great guest, Dominique Bastos, who's the senior Vice President of Cloud at Persistent Systems, formerly with AWS. That's where we first met at re:Invent. Dominique, great to have you on the program here for International Women's Day. Thanks for coming on. >> Thank you John, for having me back on theCUBE. This is an honor, especially given the theme. >> Well, I'm excited to have you on, I consider you one of those typecast personas where you've kind of done a lot of things. You're powerful, you've got great business acumen you're technical, and we're in a world where, you know the world's coming completely digital and 50% of the world is women, 51%, some say. So you got mostly male dominated industry and you have a dual engineering background and that's super impressive as well. Again, technical world, male dominated you're in there in the mix. What inspires you to get these engineering degrees? >> I think even it was more so shifted towards males. When I had the inspiration to go to engineering school I was accused as a young girl of being a tomboy and fiddling around with all my brother's toys versus focusing on my dolls and other kind of stereotypical toys that you would give a girl. I really had a curiosity for building, a curiosity for just breaking things apart and putting them back together. I was very lucky in that my I guess you call it primary school, maybe middle school, had a program for, it was like electronics, that was the class electronics. So building circuit boards and things like that. And I really enjoyed that aspect of building. I think it was more actually going into engineering school. Picking that as a discipline was a little bit, my mom's reaction to when I announced that I wanted to do engineering which was, "No, that's for boys." >> Really. >> And that really, you know, I think she, it came from a good place in trying to protect me from what she has experienced herself in terms of how women are received in those spaces. So I kind of shrugged it off and thought "Okay, well I'm definitely now going to do this." >> (laughs) If I was told not to, you're going to do it. >> I was told not to, that's all I needed to hear. And also, I think my passion was to design cars and I figured if I enroll in an industrial engineering program I could focus on ergonomic design and ultimately, you know have a career doing something that I'm passionate about. So yeah, so my inspiration was kind of a little bit of don't do this, a lot of curiosity. I'm also a very analytical person. I've been, and I don't know what the science is around left right brain to be honest, but been told that I'm a very much a logical person versus a feeler. So I don't know if that's good or bad. >> Straight shooter. What were your engineering degrees if you don't mind sharing? >> So I did industrial engineering and so I did a dual degree, industrial engineering and robotics. At the time it was like a manufacturing robotics program. It was very, very cool because we got to, I mean now looking back, the evolution of robotics is just insane. But you, you know, programmed a robotic arm to pick things up. I actually crashed the Civil Engineering School's Concrete Canoe Building Competition where you literally have to design a concrete canoe and do all the load testing and the strength testing of the materials and basically then, you know you go against other universities to race the canoe in a body of water. We did that at, in Alabama and in Georgia. So I was lucky to experience that two times. It was a lot of fun. >> But you knew, so you knew, deep down, you were technical you had a nerd vibe you were geeking out on math, tech, robotics. What happened next? I mean, what were some of the challenges you faced? How did you progress forward? Did you have any blockers and roadblocks in front of you and how did you handle those? >> Yeah, I mean I had, I had a very eye-opening experience with, in my freshman year of engineering school. I kind of went in gung-ho with zero hesitation, all the confidence in the world, 'cause I was always a very big nerd academically, I hate admitting this but myself and somebody else got most intellectual, voted by the students in high school. It's like, you don't want to be voted most intellectual when you're in high school. >> Now it's a big deal. (laughs) >> Yeah, you want to be voted like popular or anything like that? No, I was a nerd, but in engineering school, it's a, it was very humbling. That whole confidence that I had. I experienced prof, ooh, I don't want to name the school. Everybody can google it though, but, so anyway so I had experience with some professors that actually looked at me and said, "You're in the wrong program. This is difficult." I, and I think I've shared this before in other forums where, you know, my thermodynamic teacher basically told me "Cheerleading's down the hall," and it it was a very shocking thing to hear because it really made me wonder like, what am I up against here? Is this what it's going to be like going forward? And I decided not to pay attention to that. I think at the moment when you hear something like that you just, you absorb it and you also don't know how to react. And I decided immediately to just walk right past him and sit down front center in the class. In my head I was cursing him, of course, 'cause I mean, let's be real. And I was like, I'm going to show this bleep bleep. And proceeded to basically set the curve class crushed it and was back to be the teacher's assistant. So I think that was one. >> But you became his teacher assistant after, or another one? >> Yeah, I gave him a mini speech. I said, do not do this. You, you could, you could have broken me and if you would've done this to somebody who wasn't as steadfast in her goals or whatever, I was really focused like I'm doing this, I would've backed out potentially and said, you know this isn't something I want to experience on the daily. So I think that was actually a good experience because it gave me an opportunity to understand what I was up against but also double down in how I was going to deal with it. >> Nice to slay the misogynistic teachers who typecast people. Now you had a very technical career but also you had a great career at AWS on the business side you've handled 'em all of the big accounts, I won't say the names, but like we're talking about monster accounts, sales and now basically it's not really selling, you're managing a big account, it's like a big business. It's a business development thing. Technical to business transition, how do you handle that? Was that something you were natural for? Obviously you, you stared down the naysayers out of the gate in college and then in business, did that continue and how did you drive through that? >> So I think even when I was coming out of university I knew that I wanted to have a balance between the engineering program and business. A lot of my colleagues went on to do their PEs so continue to get their masters basically in engineering or their PhDs in engineering. I didn't really have an interest for that. I did international business and finance as my MBA because I wanted to explore the ability of taking what I had learned in engineering school and applying it to building businesses. I mean, at the time I didn't have it in my head that I would want to do startups but I definitely knew that I wanted to get a feel for what are they learning in business school that I missed out in engineering school. So I think that helped me when I transitioned, well when I applied, I was asked to come apply at AWS and I kind of went, no I'm going to, the DNA is going to be rejected. >> You thought, you thought you'd be rejected from AWS. >> I thought I'd be, yeah, because I have very much a startup founder kind of disruptive personality. And to me, when I first saw AWS at the stage early 2016 I saw it as a corporation. Even though from a techie standpoint, I was like, these people are insane. This is amazing what they're building. But I didn't know what the cultural vibe would feel like. I had been with GE at the beginning of my career for almost three years. So I kind of equated AWS Amazon to GE given the size because in between, I had done startups. So when I went to AWS I think initially, and I do have to kind of shout out, you know Todd Weatherby basically was the worldwide leader for ProServe and it was being built, he built it and I went into ProServe to help from that standpoint. >> John: ProServe, Professional services >> Professional services, right. To help these big enterprise customers. And specifically my first customer was an amazing experience in taking, basically the company revolves around strategic selling, right? It's not like you take a salesperson with a conventional schooling that salespeople would have and plug them into AWS in 2016. It was very much a consultative strategic approach. And for me, having a technical background and loving to solve problems for customers, working with the team, I would say, it was a dream team that I joined. And also the ability to come to the table with a technical background, knowing how to interact with senior executives to help them envision where they want to go, and then to bring a team along with you to make that happen. I mean, that was like magical for me. I loved that experience. >> So you like the culture, I mean, Andy Jassy, I've interviewed many times, always talked about builders and been a builder mentality. You mentioned that earlier at the top of this interview you've always building things, curious and you mentioned potentially your confidence might have been shaken. So you, you had the confidence. So being a builder, you know, being curious and having confidence seems to be what your superpower is. A lot of people talk about the confidence angle. How important is that and how important is that for encouraging more women to get into tech? Because I still hear that all the time. Not that they don't have confidence, but there's so many signals that potentially could shake confidence in industry >> Yeah, that's actually a really good point that you're making. A lot of signals that women get could shake their confidence and that needs to be, I mean, it's easy to say that it should be innate. I mean that's kind of like textbook, "Oh it has to come from within." Of course it does. But also, you know, we need to understand that in a population where 50% of the population is women but only 7% of the positions in tech, and I don't know the most current number in tech leadership, is women, and probably a smaller percentage in the C-suite. When you're looking at a woman who's wanting to go up the trajectory in a tech company and then there's a subconscious understanding that there's a limit to how far you'll go, your confidence, you know, in even subconsciously gets shaken a little bit because despite your best efforts, you're already seeing the cap. I would say that we need to coach girls to speak confidently to navigate conflict versus running away from it, to own your own success and be secure in what you bring to the table. And then I think a very important thing is to celebrate each other and the wins that we see for women in tech, in the industry. >> That's awesome. What's, the, in your opinion, the, you look at that, the challenges for this next generation women, and women in general, what are some of the challenges for them and that they need to overcome today? I mean, obviously the world's changed for the better. Still not there. I mean the numbers one in four women, Rachel Thornton came on, former CMO of AWS, she's at MessageBird now. They had a study where only one in four women go to the executive board level. And so there's still, still numbers are bad and then the numbers still got to get up, up big time. That's, and the industry's working on that, but it's changed. But today, what are some of the challenges for this current generation and the next generation of women and how can we and the industry meet, we being us, women in the industry, be strong role models for them? >> Well, I think the challenge is one of how many women are there in the pipeline and what are we doing to retain them and how are we offering up the opportunities to fill. As you know, as Rachel said and I haven't had an opportunity to see her, in how are we giving them this opportunity to take up those seats in the C-suite right, in these leadership roles. And I think this is a little bit exacerbated with the pandemic in that, you know when everything shut down when people were going back to deal with family and work at the same time, for better or for worse the brunt of it fell on probably, you know the maternal type caregiver within the family unit. You know, I've been, I raised my daughter alone and for me, even without the pandemic it was a struggle constantly to balance the risk that I was willing to take to show up for those positions versus investing even more of that time raising a child, right? Nevermind the unconscious bias or cultural kind of expectations that you get from the male counterparts where there's zero understanding of what a mom might go through at home to then show up to a meeting, you know fully fresh and ready to kind of spit out some wisdom. It's like, you know, your kid just freaking lost their whatever and you know, they, so you have to sort a bunch of things out. I think the challenge that women are still facing and will we have to keep working at it is making sure that there's a good pipeline. A good amount of young ladies of people taking interest in tech. And then as they're, you know, going through the funnel at stages in their career, we're providing the mentoring we're, there's representation, right? To what they're aspiring to. We're celebrating their interest in the field, right? And, and I think also we're doing things to retain them, because again, the pandemic affected everybody. I think women specifically and I don't know the statistics but I was reading something about this were the ones to tend to kind of pull it back and say well now I need to be home with, you know you name how many kids and pets and the aging parents, people that got sick to take on that position. In addition to the career aspirations that they might have. We need to make it easier basically. >> I think that's a great call out and I appreciate you bringing that up about family and being a single mom. And by the way, you're savage warrior to doing that. It's amazing. You got to, I know you have a daughter in computer science at Stanford, I want to get to that in a second. But that empathy and I mentioned Rachel Thornton, who's the CMO MessageBird and former CMO of AWS. Her thing right now to your point is mentoring and sponsorship is very key. And her company and the video that's on the site here people should look at that and reference that. They talk a lot about that empathy of people's situation whether it's a single mom, family life, men and women but mainly women because they're the ones who people aren't having a lot of empathy for in that situation, as you called it out. This is huge. And I think remote work has opened up this whole aperture of everyone has to have a view into how people are coming to the table at work. So, you know, props are bringing that up, and I recommend everyone look at check out Rachel Thornton. So how do you balance that, that home life and talk about your daughter's journey because sounds like she's nerding out at Stanford 'cause you know Stanford's called Nerd Nation, that's their motto, so you must be proud. >> I am so proud, I'm so proud. And I will say, I have to admit, because I did encounter so many obstacles and so many hurdles in my journey, it's almost like I forgot that I should set that aside and not worry about my daughter. My hope for her was for her to kind of be artistic and a painter or go into something more lighthearted and fun because I just wanted to think, I guess my mom had the same idea, right? She, always been very driven. She, I want to say that I got very lucky that she picked me to be her mom. Biologically I'm her mom, but I told her she was like a little star that fell from the sky and I, and ended up with me. I think for me, balancing being a single mom and a career where I'm leading and mentoring and making big decisions that affect people's lives as well. You have to take the best of everything you get from each of those roles. And I think that the best way is play to your strengths, right? So having been kind of a nerd and very organized person and all about, you know, systems for effectiveness, I mean, industrial engineering, parenting for me was, I'm going to make it sound super annoying and horrible, but (laughs) >> It's funny, you know, Dave Vellante and I when we started SiliconANGLE and theCUBE years ago, one of the things we were all like sports lovers. So we liked sports and we are like we looked at the people in tech as tech athletes and except there's no men and women teams, it's one team. It's all one thing. So, you know, I consider you a tech athlete you're hard charging strong and professional and smart and beautiful and brilliant, all those good things. >> Thank you. >> Now this game is changing and okay, and you've done startups, and you've done corporate jobs, now you're in a new role. What's the current tech landscape from a, you know I won't say athletic per standpoint but as people who are smart. You have all kinds of different skill sets. You have the startup warriors, you have the folks who like to be in the middle of the corporate world grow up through corporate, climb the corporate ladder. You have investors, you have, you know, creatives. What have you enjoyed most and where do you see all the action? >> I mean, I think what I've enjoyed the most has been being able to bring all of the things that I feel I'm strong at and bring it together to apply that to whatever the problem is at hand, right? So kind of like, you know if you look at a renaissance man who can kind of pop in anywhere and, oh, he's good at, you know sports and he's good at reading and, or she's good at this or, take all of those strengths and somehow bring them together to deal with the issue at hand, versus breaking up your mindset into this is textbook what I learned and this is how business should be done and I'm going to draw these hard lines between personal life and work life, or between how you do selling and how you do engineering. So I think my, the thing that I loved, really loved about AWS was a lot of leaders saw something in me that I potentially didn't see, which was, yeah you might be great at running that big account but we need help over here doing go to market for a new product launch and boom, there you go. Now I'm in a different org helping solve that problem and getting something launched. And I think if you don't box yourself in to I'm only good at this, or, you know put a label on yourself as being the rockstar in that. It leaves room for opportunities to present themselves but also it leaves room within your own mind to see yourself as somebody capable of doing anything. Right, I don't know if I answered the question accurately. >> No, that's good, no, that's awesome. I love the sharing, Yeah, great, great share there. Question is, what do you see, what do you currently during now you're building a business of Persistent for the cloud, obviously AWS and Persistent's a leader global system integrator around the world, thousands and thousands of customers from what we know and been reporting on theCUBE, what's next for you? Where do you see yourself going? Obviously you're going to knock this out of the park. Where do you see yourself as you kind of look at the continuing journey of your mission, personal, professional what's on your mind? Where do you see yourself going next? >> Well, I think, you know, again, going back to not boxing yourself in. This role is an amazing one where I have an opportunity to take all the pieces of my career in tech and apply them to building a business within a business. And that involves all the goodness of coaching and mentoring and strategizing. And I'm loving it. I'm loving the opportunity to work with such great leaders. Persistent itself is very, very good at providing opportunities, very diverse opportunities. We just had a huge Semicolon; Hackathon. Some of the winners were females. The turnout was amazing in the CTO's office. We have very strong women leading the charge for innovation. I think to answer your question about the future and where I may see myself going next, I think now that my job, well they say the job is never done. But now that Chloe's kind of settled into Stanford and kind of doing her own thing, I have always had a passion to continue leading in a way that brings me to, into the fold a lot more. So maybe, you know, maybe in a VC firm partner mode or another, you know CEO role in a startup, or my own startup. I mean, I never, I don't know right now I'm super happy but you never know, you know where your drive might go. And I also want to be able to very deliberately be in a role where I can continue to mentor and support up and coming women in tech. >> Well, you got the smarts but you got really the building mentality, the curiosity and the confidence really sets you up nicely. Dominique great story, great inspiration. You're a role model for many women, young girls out there and women in tech and in celebration. It's a great day and thank you for sharing that story and all the good nuggets there. Appreciate you coming on theCUBE, and it's been my pleasure. Thanks for coming on. >> Thank you, John. Thank you so much for having me. >> Okay, theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE here in Palo Alto getting all the content, check out the other interviews some amazing stories, lessons learned, and some, you know some funny stories and some serious stories. So have some fun and enjoy the rest of the videos here for International Women's Days, thanks for watching. (gentle inspirational music)
SUMMARY :
Dominique, great to have you on Thank you John, for and 50% of the world is I guess you call it primary And that really, you know, (laughs) If I was told not design and ultimately, you know if you don't mind sharing? and do all the load testing the challenges you faced? I kind of went in gung-ho Now it's a big deal. and you also don't know how to react. and if you would've done this to somebody Was that something you were natural for? and applying it to building businesses. You thought, you thought and I do have to kind And also the ability to come to the table Because I still hear that all the time. and that needs to be, I mean, That's, and the industry's to be home with, you know and I appreciate you bringing that up and all about, you know, It's funny, you know, and where do you see all the action? And I think if you don't box yourself in I love the sharing, Yeah, I think to answer your and all the good nuggets there. Thank you so much for having me. learned, and some, you know
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Closing Panel | Generative AI: Riding the Wave | AWS Startup Showcase S3 E1
(mellow music) >> Hello everyone, welcome to theCUBE's coverage of AWS Startup Showcase. This is the closing panel session on AI machine learning, the top startups generating generative AI on AWS. It's a great panel. This is going to be the experts talking about riding the wave in generative AI. We got Ankur Mehrotra, who's the director and general manager of AI and machine learning at AWS, and Clem Delangue, co-founder and CEO of Hugging Face, and Ori Goshen, who's the co-founder and CEO of AI21 Labs. Ori from Tel Aviv dialing in, and rest coming in here on theCUBE. Appreciate you coming on for this closing session for the Startup Showcase. >> Thanks for having us. >> Thank you for having us. >> Thank you. >> I'm super excited to have you all on. Hugging Face was recently in the news with the AWS relationship, so congratulations. Open source, open science, really driving the machine learning. And we got the AI21 Labs access to the LLMs, generating huge scale live applications, commercial applications, coming to the market, all powered by AWS. So everyone, congratulations on all your success, and thank you for headlining this panel. Let's get right into it. AWS is powering this wave here. We're seeing a lot of push here from applications. Ankur, set the table for us on the AI machine learning. It's not new, it's been goin' on for a while. Past three years have been significant advancements, but there's been a lot of work done in AI machine learning. Now it's released to the public. Everybody's super excited and now says, "Oh, the future's here!" It's kind of been going on for a while and baking. Now it's kind of coming out. What's your view here? Let's get it started. >> Yes, thank you. So, yeah, as you may be aware, Amazon has been in investing in machine learning research and development since quite some time now. And we've used machine learning to innovate and improve user experiences across different Amazon products, whether it's Alexa or Amazon.com. But we've also brought in our expertise to extend what we are doing in the space and add more generative AI technology to our AWS products and services, starting with CodeWhisperer, which is an AWS service that we announced a few months ago, which is, you can think of it as a coding companion as a service, which uses generative AI models underneath. And so this is a service that customers who have no machine learning expertise can just use. And we also are talking to customers, and we see a lot of excitement about generative AI, and customers who want to build these models themselves, who have the talent and the expertise and resources. For them, AWS has a number of different options and capabilities they can leverage, such as our custom silicon, such as Trainium and Inferentia, as well as distributed machine learning capabilities that we offer as part of SageMaker, which is an end-to-end machine learning development service. At the same time, many of our customers tell us that they're interested in not training and building these generative AI models from scratch, given they can be expensive and can require specialized talent and skills to build. And so for those customers, we are also making it super easy to bring in existing generative AI models into their machine learning development environment within SageMaker for them to use. So we recently announced our partnership with Hugging Face, where we are making it super easy for customers to bring in those models into their SageMaker development environment for fine tuning and deployment. And then we are also partnering with other proprietary model providers such as AI21 and others, where we making these generative AI models available within SageMaker for our customers to use. So our approach here is to really provide customers options and choices and help them accelerate their generative AI journey. >> Ankur, thank you for setting the table there. Clem and Ori, I want to get your take, because the riding the waves, the theme of this session, and to me being in California, I imagine the big surf, the big waves, the big talent out there. This is like alpha geeks, alpha coders, developers are really leaning into this. You're seeing massive uptake from the smartest people. Whether they're young or around, they're coming in with their kind of surfboards, (chuckles) if you will. These early adopters, they've been on this for a while; Now the waves are hitting. This is a big wave, everyone sees it. What are some of those early adopter devs doing? What are some of the use cases you're seeing right out of the gate? And what does this mean for the folks that are going to come in and get on this wave? Can you guys share your perspective on this? Because you're seeing the best talent now leaning into this. >> Yeah, absolutely. I mean, from Hugging Face vantage points, it's not even a a wave, it's a tidal wave, or maybe even the tide itself. Because actually what we are seeing is that AI and machine learning is not something that you add to your products. It's very much a new paradigm to do all technology. It's this idea that we had in the past 15, 20 years, one way to build software and to build technology, which was writing a million lines of code, very rule-based, and then you get your product. Now what we are seeing is that every single product, every single feature, every single company is starting to adopt AI to build the next generation of technology. And that works both to make the existing use cases better, if you think of search, if you think of social network, if you think of SaaS, but also it's creating completely new capabilities that weren't possible with the previous paradigm. Now AI can generate text, it can generate image, it can describe your image, it can do so many new things that weren't possible before. >> It's going to really make the developers really productive, right? I mean, you're seeing the developer uptake strong, right? >> Yes, we have over 15,000 companies using Hugging Face now, and it keeps accelerating. I really think that maybe in like three, five years, there's not going to be any company not using AI. It's going to be really kind of the default to build all technology. >> Ori, weigh in on this. APIs, the cloud. Now I'm a developer, I want to have live applications, I want the commercial applications on this. What's your take? Weigh in here. >> Yeah, first, I absolutely agree. I mean, we're in the midst of a technology shift here. I think not a lot of people realize how big this is going to be. Just the number of possibilities is endless, and I think hard to imagine. And I don't think it's just the use cases. I think we can think of it as two separate categories. We'll see companies and products enhancing their offerings with these new AI capabilities, but we'll also see new companies that are AI first, that kind of reimagine certain experiences. They build something that wasn't possible before. And that's why I think it's actually extremely exciting times. And maybe more philosophically, I think now these large language models and large transformer based models are helping us people to express our thoughts and kind of making the bridge from our thinking to a creative digital asset in a speed we've never imagined before. I can write something down and get a piece of text, or an image, or a code. So I'll start by saying it's hard to imagine all the possibilities right now, but it's certainly big. And if I had to bet, I would say it's probably at least as big as the mobile revolution we've seen in the last 20 years. >> Yeah, this is the biggest. I mean, it's been compared to the Enlightenment Age. I saw the Wall Street Journal had a recent story on this. We've been saying that this is probably going to be bigger than all inflection points combined in the tech industry, given what transformation is coming. I guess I want to ask you guys, on the early adopters, we've been hearing on these interviews and throughout the industry that there's already a set of big companies, a set of companies out there that have a lot of data and they're already there, they're kind of tinkering. Kind of reminds me of the old hyper scaler days where they were building their own scale, and they're eatin' glass, spittin' nails out, you know, they're hardcore. Then you got everybody else kind of saying board level, "Hey team, how do I leverage this?" How do you see those two things coming together? You got the fast followers coming in behind the early adopters. What's it like for the second wave coming in? What are those conversations for those developers like? >> I mean, I think for me, the important switch for companies is to change their mindset from being kind of like a traditional software company to being an AI or machine learning company. And that means investing, hiring machine learning engineers, machine learning scientists, infrastructure in members who are working on how to put these models in production, team members who are able to optimize models, specialized models, customized models for the company's specific use cases. So it's really changing this mindset of how you build technology and optimize your company building around that. Things are moving so fast that I think now it's kind of like too late for low hanging fruits or small, small adjustments. I think it's important to realize that if you want to be good at that, and if you really want to surf this wave, you need massive investments. If there are like some surfers listening with this analogy of the wave, right, when there are waves, it's not enough just to stand and make a little bit of adjustments. You need to position yourself aggressively, paddle like crazy, and that's how you get into the waves. So that's what companies, in my opinion, need to do right now. >> Ori, what's your take on the generative models out there? We hear a lot about foundation models. What's your experience running end-to-end applications for large foundation models? Any insights you can share with the app developers out there who are looking to get in? >> Yeah, I think first of all, it's start create an economy, where it probably doesn't make sense for every company to create their own foundation models. You can basically start by using an existing foundation model, either open source or a proprietary one, and start deploying it for your needs. And then comes the second round when you are starting the optimization process. You bootstrap, whether it's a demo, or a small feature, or introducing new capability within your product, and then start collecting data. That data, and particularly the human feedback data, helps you to constantly improve the model, so you create this data flywheel. And I think we're now entering an era where customers have a lot of different choice of how they want to start their generative AI endeavor. And it's a good thing that there's a variety of choices. And the really amazing thing here is that every industry, any company you speak with, it could be something very traditional like industrial or financial, medical, really any company. I think peoples now start to imagine what are the possibilities, and seriously think what's their strategy for adopting this generative AI technology. And I think in that sense, the foundation model actually enabled this to become scalable. So the barrier to entry became lower; Now the adoption could actually accelerate. >> There's a lot of integration aspects here in this new wave that's a little bit different. Before it was like very monolithic, hardcore, very brittle. A lot more integration, you see a lot more data coming together. I have to ask you guys, as developers come in and grow, I mean, when I went to college and you were a software engineer, I mean, I got a degree in computer science, and software engineering, that's all you did was code, (chuckles) you coded. Now, isn't it like everyone's a machine learning engineer at this point? Because that will be ultimately the science. So, (chuckles) you got open source, you got open software, you got the communities. Swami called you guys the GitHub of machine learning, Hugging Face is the GitHub of machine learning, mainly because that's where people are going to code. So this is essentially, machine learning is computer science. What's your reaction to that? >> Yes, my co-founder Julien at Hugging Face have been having this thing for quite a while now, for over three years, which was saying that actually software engineering as we know it today is a subset of machine learning, instead of the other way around. People would call us crazy a few years ago when we're seeing that. But now we are realizing that you can actually code with machine learning. So machine learning is generating code. And we are starting to see that every software engineer can leverage machine learning through open models, through APIs, through different technology stack. So yeah, it's not crazy anymore to think that maybe in a few years, there's going to be more people doing AI and machine learning. However you call it, right? Maybe you'll still call them software engineers, maybe you'll call them machine learning engineers. But there might be more of these people in a couple of years than there is software engineers today. >> I bring this up as more tongue in cheek as well, because Ankur, infrastructure's co is what made Cloud great, right? That's kind of the DevOps movement. But here the shift is so massive, there will be a game-changing philosophy around coding. Machine learning as code, you're starting to see CodeWhisperer, you guys have had coding companions for a while on AWS. So this is a paradigm shift. How is the cloud playing into this for you guys? Because to me, I've been riffing on some interviews where it's like, okay, you got the cloud going next level. This is an example of that, where there is a DevOps-like moment happening with machine learning, whether you call it coding or whatever. It's writing code on its own. Can you guys comment on what this means on top of the cloud? What comes out of the scale? What comes out of the benefit here? >> Absolutely, so- >> Well first- >> Oh, go ahead. >> Yeah, so I think as far as scale is concerned, I think customers are really relying on cloud to make sure that the applications that they build can scale along with the needs of their business. But there's another aspect to it, which is that until a few years ago, John, what we saw was that machine learning was a data scientist heavy activity. They were data scientists who were taking the data and training models. And then as machine learning found its way more and more into production and actual usage, we saw the MLOps become a thing, and MLOps engineers become more involved into the process. And then we now are seeing, as machine learning is being used to solve more business critical problems, we're seeing even legal and compliance teams get involved. We are seeing business stakeholders more engaged. So, more and more machine learning is becoming an activity that's not just performed by data scientists, but is performed by a team and a group of people with different skills. And for them, we as AWS are focused on providing the best tools and services for these different personas to be able to do their job and really complete that end-to-end machine learning story. So that's where, whether it's tools related to MLOps or even for folks who cannot code or don't know any machine learning. For example, we launched SageMaker Canvas as a tool last year, which is a UI-based tool which data analysts and business analysts can use to build machine learning models. So overall, the spectrum in terms of persona and who can get involved in the machine learning process is expanding, and the cloud is playing a big role in that process. >> Ori, Clem, can you guys weigh in too? 'Cause this is just another abstraction layer of scale. What's it mean for you guys as you look forward to your customers and the use cases that you're enabling? >> Yes, I think what's important is that the AI companies and providers and the cloud kind of work together. That's how you make a seamless experience and you actually reduce the barrier to entry for this technology. So that's what we've been super happy to do with AWS for the past few years. We actually announced not too long ago that we are doubling down on our partnership with AWS. We're excited to have many, many customers on our shared product, the Hugging Face deep learning container on SageMaker. And we are working really closely with the Inferentia team and the Trainium team to release some more exciting stuff in the coming weeks and coming months. So I think when you have an ecosystem and a system where the AWS and the AI providers, AI startups can work hand in hand, it's to the benefit of the customers and the companies, because it makes it orders of magnitude easier for them to adopt this new paradigm to build technology AI. >> Ori, this is a scale on reasoning too. The data's out there and making sense out of it, making it reason, getting comprehension, having it make decisions is next, isn't it? And you need scale for that. >> Yes. Just a comment about the infrastructure side. So I think really the purpose is to streamline and make these technologies much more accessible. And I think we'll see, I predict that we'll see in the next few years more and more tooling that make this technology much more simple to consume. And I think it plays a very important role. There's so many aspects, like the monitoring the models and their kind of outputs they produce, and kind of containing and running them in a production environment. There's so much there to build on, the infrastructure side will play a very significant role. >> All right, that's awesome stuff. I'd love to change gears a little bit and get a little philosophy here around AI and how it's going to transform, if you guys don't mind. There's been a lot of conversations around, on theCUBE here as well as in some industry areas, where it's like, okay, all the heavy lifting is automated away with machine learning and AI, the complexity, there's some efficiencies, it's horizontal and scalable across all industries. Ankur, good point there. Everyone's going to use it for something. And a lot of stuff gets brought to the table with large language models and other things. But the key ingredient will be proprietary data or human input, or some sort of AI whisperer kind of role, or prompt engineering, people are saying. So with that being said, some are saying it's automating intelligence. And that creativity will be unleashed from this. If the heavy lifting goes away and AI can fill the void, that shifts the value to the intellect or the input. And so that means data's got to come together, interact, fuse, and understand each other. This is kind of new. I mean, old school AI was, okay, got a big model, I provisioned it long time, very expensive. Now it's all free flowing. Can you guys comment on where you see this going with this freeform, data flowing everywhere, heavy lifting, and then specialization? >> Yeah, I think- >> Go ahead. >> Yeah, I think, so what we are seeing with these large language models or generative models is that they're really good at creating stuff. But I think it's also important to recognize their limitations. They're not as good at reasoning and logic. And I think now we're seeing great enthusiasm, I think, which is justified. And the next phase would be how to make these systems more reliable. How to inject more reasoning capabilities into these models, or augment with other mechanisms that actually perform more reasoning so we can achieve more reliable results. And we can count on these models to perform for critical tasks, whether it's medical tasks, legal tasks. We really want to kind of offload a lot of the intelligence to these systems. And then we'll have to get back, we'll have to make sure these are reliable, we'll have to make sure we get some sort of explainability that we can understand the process behind the generated results that we received. So I think this is kind of the next phase of systems that are based on these generated models. >> Clem, what's your view on this? Obviously you're at open community, open source has been around, it's been a great track record, proven model. I'm assuming creativity's going to come out of the woodwork, and if we can automate open source contribution, and relationships, and onboarding more developers, there's going to be unleashing of creativity. >> Yes, it's been so exciting on the open source front. We all know Bert, Bloom, GPT-J, T5, Stable Diffusion, that work up. The previous or the current generation of open source models that are on Hugging Face. It has been accelerating in the past few months. So I'm super excited about ControlNet right now that is really having a lot of impact, which is kind of like a way to control the generation of images. Super excited about Flan UL2, which is like a new model that has been recently released and is open source. So yeah, it's really fun to see the ecosystem coming together. Open source has been the basis for traditional software, with like open source programming languages, of course, but also all the great open source that we've gotten over the years. So we're happy to see that the same thing is happening for machine learning and AI, and hopefully can help a lot of companies reduce a little bit the barrier to entry. So yeah, it's going to be exciting to see how it evolves in the next few years in that respect. >> I think the developer productivity angle that's been talked about a lot in the industry will be accelerated significantly. I think security will be enhanced by this. I think in general, applications are going to transform at a radical rate, accelerated, incredible rate. So I think it's not a big wave, it's the water, right? I mean, (chuckles) it's the new thing. My final question for you guys, if you don't mind, I'd love to get each of you to answer the question I'm going to ask you, which is, a lot of conversations around data. Data infrastructure's obviously involved in this. And the common thread that I'm hearing is that every company that looks at this is asking themselves, if we don't rebuild our company, start thinking about rebuilding our business model around AI, we might be dinosaurs, we might be extinct. And it reminds me that scene in Moneyball when, at the end, it's like, if we're not building the model around your model, every company will be out of business. What's your advice to companies out there that are having those kind of moments where it's like, okay, this is real, this is next gen, this is happening. I better start thinking and putting into motion plans to refactor my business, 'cause it's happening, business transformation is happening on the cloud. This kind of puts an exclamation point on, with the AI, as a next step function. Big increase in value. So it's an opportunity for leaders. Ankur, we'll start with you. What's your advice for folks out there thinking about this? Do they put their toe in the water? Do they jump right into the deep end? What's your advice? >> Yeah, John, so we talk to a lot of customers, and customers are excited about what's happening in the space, but they often ask us like, "Hey, where do we start?" So we always advise our customers to do a lot of proof of concepts, understand where they can drive the biggest ROI. And then also leverage existing tools and services to move fast and scale, and try and not reinvent the wheel where it doesn't need to be. That's basically our advice to customers. >> Get it. Ori, what's your advice to folks who are scratching their head going, "I better jump in here. "How do I get started?" What's your advice? >> So I actually think that need to think about it really economically. Both on the opportunity side and the challenges. So there's a lot of opportunities for many companies to actually gain revenue upside by building these new generative features and capabilities. On the other hand, of course, this would probably affect the cogs, and incorporating these capabilities could probably affect the cogs. So I think we really need to think carefully about both of these sides, and also understand clearly if this is a project or an F word towards cost reduction, then the ROI is pretty clear, or revenue amplifier, where there's, again, a lot of different opportunities. So I think once you think about this in a structured way, I think, and map the different initiatives, then it's probably a good way to start and a good way to start thinking about these endeavors. >> Awesome. Clem, what's your take on this? What's your advice, folks out there? >> Yes, all of these are very good advice already. Something that you said before, John, that I disagreed a little bit, a lot of people are talking about the data mode and proprietary data. Actually, when you look at some of the organizations that have been building the best models, they don't have specialized or unique access to data. So I'm not sure that's so important today. I think what's important for companies, and it's been the same for the previous generation of technology, is their ability to build better technology faster than others. And in this new paradigm, that means being able to build machine learning faster than others, and better. So that's how, in my opinion, you should approach this. And kind of like how can you evolve your company, your teams, your products, so that you are able in the long run to build machine learning better and faster than your competitors. And if you manage to put yourself in that situation, then that's when you'll be able to differentiate yourself to really kind of be impactful and get results. That's really hard to do. It's something really different, because machine learning and AI is a different paradigm than traditional software. So this is going to be challenging, but I think if you manage to nail that, then the future is going to be very interesting for your company. >> That's a great point. Thanks for calling that out. I think this all reminds me of the cloud days early on. If you went to the cloud early, you took advantage of it when the pandemic hit. If you weren't native in the cloud, you got hamstrung by that, you were flatfooted. So just get in there. (laughs) Get in the cloud, get into AI, you're going to be good. Thanks for for calling that. Final parting comments, what's your most exciting thing going on right now for you guys? Ori, Clem, what's the most exciting thing on your plate right now that you'd like to share with folks? >> I mean, for me it's just the diversity of use cases and really creative ways of companies leveraging this technology. Every day I speak with about two, three customers, and I'm continuously being surprised by the creative ideas. And the future is really exciting of what can be achieved here. And also I'm amazed by the pace that things move in this industry. It's just, there's not at dull moment. So, definitely exciting times. >> Clem, what are you most excited about right now? >> For me, it's all the new open source models that have been released in the past few weeks, and that they'll keep being released in the next few weeks. I'm also super excited about more and more companies getting into this capability of chaining different models and different APIs. I think that's a very, very interesting development, because it creates new capabilities, new possibilities, new functionalities that weren't possible before. You can plug an API with an open source embedding model, with like a no-geo transcription model. So that's also very exciting. This capability of having more interoperable machine learning will also, I think, open a lot of interesting things in the future. >> Clem, congratulations on your success at Hugging Face. Please pass that on to your team. Ori, congratulations on your success, and continue to, just day one. I mean, it's just the beginning. It's not even scratching the service. Ankur, I'll give you the last word. What are you excited for at AWS? More cloud goodness coming here with AI. Give you the final word. >> Yeah, so as both Clem and Ori said, I think the research in the space is moving really, really fast, so we are excited about that. But we are also excited to see the speed at which enterprises and other AWS customers are applying machine learning to solve real business problems, and the kind of results they're seeing. So when they come back to us and tell us the kind of improvement in their business metrics and overall customer experience that they're driving and they're seeing real business results, that's what keeps us going and inspires us to continue inventing on their behalf. >> Gentlemen, thank you so much for this awesome high impact panel. Ankur, Clem, Ori, congratulations on all your success. We'll see you around. Thanks for coming on. Generative AI, riding the wave, it's a tidal wave, it's the water, it's all happening. All great stuff. This is season three, episode one of AWS Startup Showcase closing panel. This is the AI ML episode, the top startups building generative AI on AWS. I'm John Furrier, your host. Thanks for watching. (mellow music)
SUMMARY :
This is the closing panel I'm super excited to have you all on. is to really provide and to me being in California, and then you get your product. kind of the default APIs, the cloud. and kind of making the I saw the Wall Street Journal I think it's important to realize that the app developers out there So the barrier to entry became lower; I have to ask you guys, instead of the other way around. That's kind of the DevOps movement. and the cloud is playing a and the use cases that you're enabling? the barrier to entry And you need scale for that. in the next few years and AI can fill the void, a lot of the intelligence and if we can automate reduce a little bit the barrier to entry. I'd love to get each of you drive the biggest ROI. to folks who are scratching So I think once you think Clem, what's your take on this? and it's been the same of the cloud days early on. And also I'm amazed by the pace in the past few weeks, Please pass that on to your team. and the kind of results they're seeing. This is the AI ML episode,
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Joseph Nelson, Roboflow | AWS Startup Showcase
(chill electronic music) >> Hello everyone, welcome to theCUBE's presentation of the AWS Startups Showcase, AI and machine learning, the top startups building generative AI on AWS. This is the season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talk about AI and machine learning. Can't believe it's three years and season one. I'm your host, John Furrier. Got a great guest today, we're joined by Joseph Nelson, the co-founder and CEO of Roboflow, doing some cutting edge stuff around computer vision and really at the front end of this massive wave coming around, large language models, computer vision. The next gen AI is here, and it's just getting started. We haven't even scratched a service. Thanks for joining us today. >> Thanks for having me. >> So you got to love the large language model, foundation models, really educating the mainstream world. ChatGPT has got everyone in the frenzy. This is educating the world around this next gen AI capabilities, enterprise, image and video data, all a big part of it. I mean the edge of the network, Mobile World Conference is happening right now, this month, and it's just ending up, it's just continue to explode. Video is huge. So take us through the company, do a quick explanation of what you guys are doing, when you were founded. Talk about what the company's mission is, and what's your North Star, why do you exist? >> Yeah, Roboflow exists to really kind of make the world programmable. I like to say make the world be read and write access. And our North Star is enabling developers, predominantly, to build that future. If you look around, anything that you see will have software related to it, and can kind of be turned into software. The limiting reactant though, is how to enable computers and machines to understand things as well as people can. And in a lot of ways, computer vision is that missing element that enables anything that you see to become software. So in the virtue of, if software is eating the world, computer vision kind of makes the aperture infinitely wide. It's something that I kind of like, the way I like to frame it. And the capabilities are there, the open source models are there, the amount of data is there, the computer capabilities are only improving annually, but there's a pretty big dearth of tooling, and an early but promising sign of the explosion of use cases, models, and data sets that companies, developers, hobbyists alike will need to bring these capabilities to bear. So Roboflow is in the game of building the community around that capability, building the use cases that allow developers and enterprises to use computer vision, and providing the tooling for companies and developers to be able to add computer vision, create better data sets, and deploy to production, quickly, easily, safely, invaluably. >> You know, Joseph, the word in production is actually real now. You're seeing a lot more people doing in production activities. That's a real hot one and usually it's slower, but it's gone faster, and I think that's going to be more the same. And I think the parallel between what we're seeing on the large language models coming into computer vision, and as you mentioned, video's data, right? I mean we're doing video right now, we're transcribing it into a transcript, linking up to your linguistics, times and the timestamp, I mean everything's data and that really kind of feeds. So this connection between what we're seeing, the large language and computer vision are coming together kind of cousins, brothers. I mean, how would you compare, how would you explain to someone, because everyone's like on this wave of watching people bang out their homework assignments, and you know, write some hacks on code with some of the open AI technologies, there is a corollary directly related to to the vision side. Can you explain? >> Yeah, the rise of large language models are showing what's possible, especially with text, and I think increasingly will get multimodal as the images and video become ingested. Though there's kind of this still core missing element of basically like understanding. So the rise of large language models kind of create this new area of generative AI, and generative AI in the context of computer vision is a lot of, you know, creating video and image assets and content. There's also this whole surface area to understanding what's already created. Basically digitizing physical, real world things. I mean the Metaverse can't be built if we don't know how to mirror or create or identify the objects that we want to interact with in our everyday lives. And where computer vision comes to play in, especially what we've seen at Roboflow is, you know, a little over a hundred thousand developers now have built with our tools. That's to the tune of a hundred million labeled open source images, over 10,000 pre-trained models. And they've kind of showcased to us all of the ways that computer vision is impacting and bringing the world to life. And these are things that, you know, even before large language models and generative AI, you had pretty impressive capabilities, and when you add the two together, it actually unlocks these kind of new capabilities. So for example, you know, one of our users actually powers the broadcast feeds at Wimbledon. So here we're talking about video, we're streaming, we're doing things live, we've got folks that are cropping and making sure we look good, and audio/visual all plugged in correctly. When you broadcast Wimbledon, you'll notice that the camera controllers need to do things like track the ball, which is moving at extremely high speeds and zoom crop, pan tilt, as well as determine if the ball bounced in or out. The very controversial but critical key to a lot of tennis matches. And a lot of that has been historically done with the trained, but fallible human eye and computer vision is, you know, well suited for this task to say, how do we track, pan, tilt, zoom, and see, track the tennis ball in real time, run at 30 plus frames per second, and do it all on the edge. And those are capabilities that, you know, were kind of like science fiction, maybe even a decade ago, and certainly five years ago. Now the interesting thing, is that with the advent of of generative AI, you can start to do things like create your own training data sets, or kind of create logic around once you have this visual input. And teams at Tesla have actually been speaking about, of course the autopilot team's focused on doing vision tasks, but they've combined large language models to add reasoning and logic. So given that you see, let's say the tennis ball, what do you want to do? And being able to combine the capabilities of what LLM's represent, which is really a lot of basically, core human reasoning and logic, with computer vision for the inputs of what's possible, creates these new capabilities, let alone multimodality, which I'm sure we'll talk more about. >> Yeah, and it's really, I mean it's almost intoxicating. It's amazing that this is so capable because the cloud scales here, you got the edge developing, you can decouple compute power, and let Moore's law and all the new silicone and the processors and the GPUs do their thing, and you got open source booming. You're kind of getting at this next segment I wanted to get into, which is the, how people should be thinking about these advances of the computer vision. So this is now a next wave, it's here. I mean I'd love to have that for baseball because I'm always like, "Oh, it should have been a strike." I'm sure that's going to be coming soon, but what is the computer vision capable of doing today? I guess that's my first question. You hit some of it, unpack that a little bit. What does general AI mean in computer vision? What's the new thing? Because there are old technology's been around, proprietary, bolted onto hardware, but hardware advances at a different pace, but now you got new capabilities, generative AI for vision, what does that mean? >> Yeah, so computer vision, you know, at its core is basically enabling machines, computers, to understand, process, and act on visual data as effective or more effective than people can. Traditionally this has been, you know, task types like classification, which you know, identifying if a given image belongs in a certain category of goods on maybe a retail site, is the shoes or is it clothing? Or object detection, which is, you know, creating bounding boxes, which allows you to do things like count how many things are present, or maybe measure the speed of something, or trigger an alert when something becomes visible in frame that wasn't previously visible in frame, or instant segmentation where you're creating pixel wise segmentations for both instance and semantic segmentation, where you often see these kind of beautiful visuals of the polygon surrounding objects that you see. Then you have key point detection, which is where you see, you know, athletes, and each of their joints are kind of outlined is another more traditional type problem in signal processing and computer vision. With generative AI, you kind of get a whole new class of problem types that are opened up. So in a lot of ways I think about generative AI in computer vision as some of the, you know, problems that you aimed to tackle, might still be better suited for one of the previous task types we were discussing. Some of those problem types may be better suited for using a generative technique, and some are problem types that just previously wouldn't have been possible absent generative AI. And so if you make that kind of Venn diagram in your head, you can think about, okay, you know, visual question answering is a task type where if I give you an image and I say, you know, "How many people are in this image?" We could either build an object detection model that might count all those people, or maybe a visual question answering system would sufficiently answer this type of problem. Let alone generative AI being able to create new training data for old systems. And that's something that we've seen be an increasingly prominent use case for our users, as much as things that we advise our customers and the community writ large to take advantage of. So ultimately those are kind of the traditional task types. I can give you some insight, maybe, into how I think about what's possible today, or five years or ten years as you sort go back. >> Yes, definitely. Let's get into that vision. >> So I kind of think about the types of use cases in terms of what's possible. If you just imagine a very simple bell curve, your normal distribution, for the longest time, the types of things that are in the center of that bell curve are identifying objects that are very common or common objects in context. Microsoft published the COCO Dataset in 2014 of common objects and contexts, of hundreds of thousands of images of chairs, forks, food, person, these sorts of things. And you know, the challenge of the day had always been, how do you identify just those 80 objects? So if we think about the bell curve, that'd be maybe the like dead center of the curve, where there's a lot of those objects present, and it's a very common thing that needs to be identified. But it's a very, very, very small sliver of the distribution. Now if you go out to the way long tail, let's go like deep into the tail of this imagined visual normal distribution, you're going to have a problem like one of our customers, Rivian, in tandem with AWS, is tackling, to do visual quality assurance and manufacturing in production processes. Now only Rivian knows what a Rivian is supposed to look like. Only they know the imagery of what their goods that are going to be produced are. And then between those long tails of proprietary data of highly specific things that need to be understood, in the center of the curve, you have a whole kind of messy middle, type of problems I like to say. The way I think about computer vision advancing, is it's basically you have larger and larger and more capable models that eat from the center out, right? So if you have a model that, you know, understands the 80 classes in COCO, well, pretty soon you have advances like Clip, which was trained on 400 million image text pairs, and has a greater understanding of a wider array of objects than just 80 classes in context. And over time you'll get more and more of these larger models that kind of eat outwards from that center of the distribution. And so the question becomes for companies, when can you rely on maybe a model that just already exists? How do you use your data to get what may be capable off the shelf, so to speak, into something that is usable for you? Or, if you're in those long tails and you have proprietary data, how do you take advantage of the greatest asset you have, which is observed visual information that you want to put to work for your customers, and you're kind of living in the long tails, and you need to adapt state of the art for your capabilities. So my mental model for like how computer vision advances is you have that bell curve, and you have increasingly powerful models that eat outward. And multimodality has a role to play in that, larger models have a role to play in that, more compute, more data generally has a role to play in that. But it will be a messy and I think long condition. >> Well, the thing I want to get, first of all, it's great, great mental model, I appreciate that, 'cause I think that makes a lot of sense. The question is, it seems now more than ever, with the scale and compute that's available, that not only can you eat out to the middle in your example, but there's other models you can integrate with. In the past there was siloed, static, almost bespoke. Now you're looking at larger models eating into the bell curve, as you said, but also integrating in with other stuff. So this seems to be part of that interaction. How does, first of all, is that really happening? Is that true? And then two, what does that mean for companies who want to take advantage of this? Because the old model was operational, you know? I have my cameras, they're watching stuff, whatever, and like now you're in this more of a, distributed computing, computer science mindset, not, you know, put the camera on the wall kind of- I'm oversimplifying, but you know what I'm saying. What's your take on that? >> Well, to the first point of, how are these advances happening? What I was kind of describing was, you know, almost uni-dimensional in that you have like, you're only thinking about vision, but the rise of generative techniques and multi-modality, like Clip is a multi-modal model, it has 400 million image text pairs. That will advance the generalizability at a faster rate than just treating everything as only vision. And that's kind of where LLMs and vision will intersect in a really nice and powerful way. Now in terms of like companies, how should they be thinking about taking advantage of these trends? The biggest thing that, and I think it's different, obviously, on the size of business, if you're an enterprise versus a startup. The biggest thing that I think if you're an enterprise, and you have an established scaled business model that is working for your customers, the question becomes, how do you take advantage of that established data moat, potentially, resource moats, and certainly, of course, establish a way of providing value to an end user. So for example, one of our customers, Walmart, has the advantage of one of the largest inventory and stock of any company in the world. And they also of course have substantial visual data, both from like their online catalogs, or understanding what's in stock or out of stock, or understanding, you know, the quality of things that they're going from the start of their supply chain to making it inside stores, for delivery of fulfillments. All these are are visual challenges. Now they already have a substantial trove of useful imagery to understand and teach and train large models to understand each of the individual SKUs and products that are in their stores. And so if I'm a Walmart, what I'm thinking is, how do I make sure that my petabytes of visual information is utilized in a way where I capture the proprietary benefit of the models that I can train to do tasks like, what item was this? Or maybe I'm going to create AmazonGo-like technology, or maybe I'm going to build like delivery robots, or I want to automatically know what's in and out of stock from visual input fees that I have across my in-store traffic. And that becomes the question and flavor of the day for enterprises. I've got this large amount of data, I've got an established way that I can provide more value to my own customers. How do I ensure I take advantage of the data advantage I'm already sitting on? If you're a startup, I think it's a pretty different question, and I'm happy to talk about. >> Yeah, what's startup angle on this? Because you know, they're going to want to take advantage. It's like cloud startups, cloud native startups, they were born in the cloud, they never had an IT department. So if you're a startup, is there a similar role here? And if I'm a computer vision startup, what's that mean? So can you share your your take on that, because there'll be a lot of people starting up from this. >> So the startup on the opposite advantage and disadvantage, right? Like a startup doesn't have an proven way of delivering repeatable value in the same way that a scaled enterprise does. But it does have the nimbleness to identify and take advantage of techniques that you can start from a blank slate. And I think the thing that startups need to be wary of in the generative AI enlarged language model, in multimodal world, is building what I like to call, kind of like sandcastles. A sandcastle is maybe a business model or a capability that's built on top of an assumption that is going to be pretty quickly wiped away by improving underlying model technology. So almost like if you imagine like the ocean, the waves are coming in, and they're going to wipe away your progress. You don't want to be in the position of building sandcastle business where, you don't want to bet on the fact that models aren't going to get good enough to solve the task type that you might be solving. In other words, don't take a screenshot of what's capable today. Assume that what's capable today is only going to continue to become possible. And so for a startup, what you can do, that like enterprises are quite comparatively less good at, is embedding these capabilities deeply within your products and delivering maybe a vertical based experience, where AI kind of exists in the background. >> Yeah. >> And we might not think of companies as, you know, even AI companies, it's just so embedded in the experience they provide, but that's like the vertical application example of taking AI and making it be immediately usable. Or, of course there's tons of picks and shovels businesses to be built like Roboflow, where you're enabling these enterprises to take advantage of something that they have, whether that's their data sets, their computes, or their intellect. >> Okay, so if I hear that right, by the way, I love, that's horizontally scalable, that's the large language models, go up and build them the apps, hence your developer focus. I'm sure that's probably the reason that the tsunami of developer's action. So you're saying picks and shovels tools, don't try to replicate the platform of what could be the platform. Oh, go to a VC, I'm going to build a platform. No, no, no, no, those are going to get wiped away by the large language models. Is there one large language model that will rule the world, or do you see many coming? >> Yeah, so to be clear, I think there will be useful platforms. I just think a lot of people think that they're building, let's say, you know, if we put this in the cloud context, you're building a specific type of EC2 instance. Well, it turns out that Amazon can offer that type of EC2 instance, and immediately distribute it to all of their customers. So you don't want to be in the position of just providing something that actually ends up looking like a feature, which in the context of AI, might be like a small incremental improvement on the model. If that's all you're doing, you're a sandcastle business. Now there's a lot of platform businesses that need to be built that enable businesses to get to value and do things like, how do I monitor my models? How do I create better models with my given data sets? How do I ensure that my models are doing what I want them to do? How do I find the right models to use? There's all these sorts of platform wide problems that certainly exist for businesses. I just think a lot of startups that I'm seeing right now are making the mistake of assuming the advances we're seeing are not going to accelerate or even get better. >> So if I'm a customer, if I'm a company, say I'm a startup or an enterprise, either one, same question. And I want to stand up, and I have developers working on stuff, I want to start standing up an environment to start doing stuff. Is that a service provider? Is that a managed service? Is that you guys? So how do you guys fit into your customers leaning in? Is it just for developers? Are you targeting with a specific like managed service? What's the product consumption? How do you talk to customers when they come to you? >> The thing that we do is enable, we give developers superpowers to build automated inventory tracking, self-checkout systems, identify if this image is malignant cancer or benign cancer, ensure that these products that I've produced are correct. Make sure that that the defect that might exist on this electric vehicle makes its way back for review. All these sorts of problems are immediately able to be solved and tackled. In terms of the managed services element, we have solutions as integrators that will often build on top of our tools, or we'll have companies that look to us for guidance, but ultimately the company is in control of developing and building and creating these capabilities in house. I really think the distinction is maybe less around managed service and tool, and more around ownership in the era of AI. So for example, if I'm using a managed service, in that managed service, part of their benefit is that they are learning across their customer sets, then it's a very different relationship than using a managed service where I'm developing some amount of proprietary advantages for my data sets. And I think that's a really important thing that companies are becoming attuned to, just the value of the data that they have. And so that's what we do. We tell companies that you have this proprietary, immense treasure trove of data, use that to your advantage, and think about us more like a set of tools that enable you to get value from that capability. You know, the HashiCorp's and GitLab's of the world have proven like what these businesses look like at scale. >> And you're targeting developers. When you go into a company, do you target developers with freemium, is there a paid service? Talk about the business model real quick. >> Sure, yeah. The tools are free to use and get started. When someone signs up for Roboflow, they may elect to make their work open source, in which case we're able to provide even more generous usage limits to basically move the computer vision community forward. If you elect to make your data private, you can use our hosted data set managing, data set training, model deployment, annotation tooling up to some limits. And then usually when someone validates that what they're doing gets them value, they purchase a subscription license to be able to scale up those capabilities. So like most developer centric products, it's free to get started, free to prove, free to poke around, develop what you think is possible. And then once you're getting to value, then we're able to capture the commercial upside in the value that's being provided. >> Love the business model. It's right in line with where the market is. There's kind of no standards bodies these days. The developers are the ones who are deciding kind of what the standards are by their adoption. I think making that easy for developers to get value as the model open sources continuing to grow, you can see more of that. Great perspective Joseph, thanks for sharing that. Put a plug in for the company. What are you guys doing right now? Where are you in your growth? What are you looking for? How should people engage? Give the quick commercial for the company. >> So as I mentioned, Roboflow is I think one of the largest, if not the largest collections of computer vision models and data sets that are open source, available on the web today, and have a private set of tools that over half the Fortune 100 now rely on those tools. So we're at the stage now where we know people want what we're working on, and we're continuing to drive that type of adoption. So companies that are looking to make better models, improve their data sets, train and deploy, often will get a lot of value from our tools, and certainly reach out to talk. I'm sure there's a lot of talented engineers that are tuning in too, we're aggressively hiring. So if you are interested in being a part of making the world programmable, and being at the ground floor of the company that's creating these capabilities to be writ large, we'd love to hear from you. >> Amazing, Joseph, thanks so much for coming on and being part of the AWS Startup Showcase. Man, if I was in my twenties, I'd be knocking on your door, because it's the hottest trend right now, it's super exciting. Generative AI is just the beginning of massive sea change. Congratulations on all your success, and we'll be following you guys. Thanks for spending the time, really appreciate it. >> Thanks for having me. >> Okay, this is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about the hottest things in tech. I'm John Furrier, your host. Thanks for watching. (chill electronic music)
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Adam Wenchel & John Dickerson, Arthur | AWS Startup Showcase S3 E1
(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI Machine Learning Top Startups Building Generative AI on AWS. This is season 3, episode 1 of the ongoing series covering the exciting startup from the AWS ecosystem to talk about AI and machine learning. I'm your host, John Furrier. I'm joined by two great guests here, Adam Wenchel, who's the CEO of Arthur, and Chief Scientist of Arthur, John Dickerson. Talk about how they help people build better LLM AI systems to get them into the market faster. Gentlemen, thank you for coming on. >> Yeah, thanks for having us, John. >> Well, I got to say I got to temper my enthusiasm because the last few months explosion of interest in LLMs with ChatGPT, has opened the eyes to everybody around the reality of that this is going next gen, this is it, this is the moment, this is the the point we're going to look back and say, this is the time where AI really hit the scene for real applications. So, a lot of Large Language Models, also known as LLMs, foundational models, and generative AI is all booming. This is where all the alpha developers are going. This is where everyone's focusing their business model transformations on. This is where developers are seeing action. So it's all happening, the wave is here. So I got to ask you guys, what are you guys seeing right now? You're in the middle of it, it's hitting you guys right on. You're in the front end of this massive wave. >> Yeah, John, I don't think you have to temper your enthusiasm at all. I mean, what we're seeing every single day is, everything from existing enterprise customers coming in with new ways that they're rethinking, like business things that they've been doing for many years that they can now do an entirely different way, as well as all manner of new companies popping up, applying LLMs to everything from generating code and SQL statements to generating health transcripts and just legal briefs. Everything you can imagine. And when you actually sit down and look at these systems and the demos we get of them, the hype is definitely justified. It's pretty amazing what they're going to do. And even just internally, we built, about a month ago in January, we built an Arthur chatbot so customers could ask questions, technical questions from our, rather than read our product documentation, they could just ask this LLM a particular question and get an answer. And at the time it was like state of the art, but then just last week we decided to rebuild it because the tooling has changed so much that we, last week, we've completely rebuilt it. It's now way better, built on an entirely different stack. And the tooling has undergone a full generation worth of change in six weeks, which is crazy. So it just tells you how much energy is going into this and how fast it's evolving right now. >> John, weigh in as a chief scientist. I mean, you must be blown away. Talk about kid in the candy store. I mean, you must be looking like this saying, I mean, she must be super busy to begin with, but the change, the acceleration, can you scope the kind of change you're seeing and be specific around the areas you're seeing movement and highly accelerated change? >> Yeah, definitely. And it is very, very exciting actually, thinking back to when ChatGPT was announced, that was a night our company was throwing an event at NeurIPS, which is maybe the biggest machine learning conference out there. And the hype when that happened was palatable and it was just shocking to see how well that performed. And then obviously over the last few months since then, as LLMs have continued to enter the market, we've seen use cases for them, like Adam mentioned all over the place. And so, some things I'm excited about in this space are the use of LLMs and more generally, foundation models to redesign traditional operations, research style problems, logistics problems, like auctions, decisioning problems. So moving beyond the already amazing news cases, like creating marketing content into more core integration and a lot of the bread and butter companies and tasks that drive the American ecosystem. And I think we're just starting to see some of that. And in the next 12 months, I think we're going to see a lot more. If I had to make other predictions, I think we're going to continue seeing a lot of work being done on managing like inference time costs via shrinking models or distillation. And I don't know how to make this prediction, but at some point we're going to be seeing lots of these very large scale models operating on the edge as well. So the time scales are extremely compressed, like Adam mentioned, 12 months from now, hard to say. >> We were talking on theCUBE prior to this session here. We had theCUBE conversation here and then the Wall Street Journal just picked up on the same theme, which is the printing press moment created the enlightenment stage of the history. Here we're in the whole nother automating intellect efficiency, doing heavy lifting, the creative class coming back, a whole nother level of reality around the corner that's being hyped up. The question is, is this justified? Is there really a breakthrough here or is this just another result of continued progress with AI? Can you guys weigh in, because there's two schools of thought. There's the, "Oh my God, we're entering a new enlightenment tech phase, of the equivalent of the printing press in all areas. Then there's, Ah, it's just AI (indistinct) inch by inch. What's your guys' opinion? >> Yeah, I think on the one hand when you're down in the weeds of building AI systems all day, every day, like we are, it's easy to look at this as an incremental progress. Like we have customers who've been building on foundation models since we started the company four years ago, particular in computer vision for classification tasks, starting with pre-trained models, things like that. So that part of it doesn't feel real new, but what does feel new is just when you apply these things to language with all the breakthroughs and computational efficiency, algorithmic improvements, things like that, when you actually sit down and interact with ChatGPT or one of the other systems that's out there that's building on top of LLMs, it really is breathtaking, like, the level of understanding that they have and how quickly you can accelerate your development efforts and get an actual working system in place that solves a really important real world problem and makes people way faster, way more efficient. So I do think there's definitely something there. It's more than just incremental improvement. This feels like a real trajectory inflection point for the adoption of AI. >> John, what's your take on this? As people come into the field, I'm seeing a lot of people move from, hey, I've been coding in Python, I've been doing some development, I've been a software engineer, I'm a computer science student. I'm coding in C++ old school, OG systems person. Where do they come in? Where's the focus, where's the action? Where are the breakthroughs? Where are people jumping in and rolling up their sleeves and getting dirty with this stuff? >> Yeah, all over the place. And it's funny you mentioned students in a different life. I wore a university professor hat and so I'm very, very familiar with the teaching aspects of this. And I will say toward Adam's point, this really is a leap forward in that techniques like in a co-pilot for example, everybody's using them right now and they really do accelerate the way that we develop. When I think about the areas where people are really, really focusing right now, tooling is certainly one of them. Like you and I were chatting about LangChain right before this interview started, two or three people can sit down and create an amazing set of pipes that connect different aspects of the LLM ecosystem. Two, I would say is in engineering. So like distributed training might be one, or just understanding better ways to even be able to train large models, understanding better ways to then distill them or run them. So like this heavy interaction now between engineering and what I might call traditional machine learning from 10 years ago where you had to know a lot of math, you had to know calculus very well, things like that. Now you also need to be, again, a very strong engineer, which is exciting. >> I interviewed Swami when he talked about the news. He's ahead of Amazon's machine learning and AI when they announced Hugging Face announcement. And I reminded him how Amazon was easy to get into if you were developing a startup back in 2007,8, and that the language models had that similar problem. It's step up a lot of content and a lot of expense to get provisioned up, now it's easy. So this is the next wave of innovation. So how do you guys see that from where we are right now? Are we at that point where it's that moment where it's that cloud-like experience for LLMs and large language models? >> Yeah, go ahead John. >> I think the answer is yes. We see a number of large companies that are training these and serving these, some of which are being co-interviewed in this episode. I think we're at that. Like, you can hit one of these with a simple, single line of Python, hitting an API, you can boot this up in seconds if you want. It's easy. >> Got it. >> So I (audio cuts out). >> Well let's take a step back and talk about the company. You guys being featured here on the Showcase. Arthur, what drove you to start the company? How'd this all come together? What's the origination story? Obviously you got a big customers, how'd get started? What are you guys doing? How do you make money? Give a quick overview. >> Yeah, I think John and I come at it from slightly different angles, but for myself, I have been a part of a number of technology companies. I joined Capital One, they acquired my last company and shortly after I joined, they asked me to start their AI team. And so even though I've been doing AI for a long time, I started my career back in DARPA. It was the first time I was really working at scale in AI at an organization where there were hundreds of millions of dollars in revenue at stake with the operation of these models and that they were impacting millions of people's financial livelihoods. And so it just got me hyper-focused on these issues around making sure that your AI worked well and it worked well for your company and it worked well for the people who were being affected by it. At the time when I was doing this 2016, 2017, 2018, there just wasn't any tooling out there to support this production management model monitoring life phase of the life cycle. And so we basically left to start the company that I wanted. And John has a his own story. I'll let let you share that one, John. >> Go ahead John, you're up. >> Yeah, so I'm coming at this from a different world. So I'm on leave now from a tenured role in academia where I was leading a large lab focusing on the intersection of machine learning and economics. And so questions like fairness or the response to the dynamism on the underlying environment have been around for quite a long time in that space. And so I've been thinking very deeply about some of those more like R and D style questions as well as having deployed some automation code across a couple of different industries, some in online advertising, some in the healthcare space and so on, where concerns of, again, fairness come to bear. And so Adam and I connected to understand the space of what that might look like in the 2018 20 19 realm from a quantitative and from a human-centered point of view. And so booted things up from there. >> Yeah, bring that applied engineering R and D into the Capital One, DNA that he had at scale. I could see that fit. I got to ask you now, next step, as you guys move out and think about LLMs and the recent AI news around the generative models and the foundational models like ChatGPT, how should we be looking at that news and everyone watching might be thinking the same thing. I know at the board level companies like, we should refactor our business, this is the future. It's that kind of moment, and the tech team's like, okay, boss, how do we do this again? Or are they prepared? How should we be thinking? How should people watching be thinking about LLMs? >> Yeah, I think they really are transformative. And so, I mean, we're seeing companies all over the place. Everything from large tech companies to a lot of our large enterprise customers are launching significant projects at core parts of their business. And so, yeah, I would be surprised, if you're serious about becoming an AI native company, which most leading companies are, then this is a trend that you need to be taking seriously. And we're seeing the adoption rate. It's funny, I would say the AI adoption in the broader business world really started, let's call it four or five years ago, and it was a relatively slow adoption rate, but I think all that kind of investment in and scaling the maturity curve has paid off because the rate at which people are adopting and deploying systems based on this is tremendous. I mean, this has all just happened in the few months and we're already seeing people get systems into production. So, now there's a lot of things you have to guarantee in order to put these in production in a way that basically is added into your business and doesn't cause more headaches than it solves. And so that's where we help customers is where how do you put these out there in a way that they're going to represent your company well, they're going to perform well, they're going to do their job and do it properly. >> So in the use case, as a customer, as I think about this, there's workflows. They might have had an ML AI ops team that's around IT. Their inference engines are out there. They probably don't have a visibility on say how much it costs, they're kicking the tires. When you look at the deployment, there's a cost piece, there's a workflow piece, there's fairness you mentioned John, what should be, I should be thinking about if I'm going to be deploying stuff into production, I got to think about those things. What's your opinion? >> Yeah, I'm happy to dive in on that one. So monitoring in general is extremely important once you have one of these LLMs in production, and there have been some changes versus traditional monitoring that we can dive deeper into that LLMs are really accelerated. But a lot of that bread and butter style of things you should be looking out for remain just as important as they are for what you might call traditional machine learning models. So the underlying environment of data streams, the way users interact with these models, these are all changing over time. And so any performance metrics that you care about, traditional ones like an accuracy, if you can define that for an LLM, ones around, for example, fairness or bias. If that is a concern for your particular use case and so on. Those need to be tracked. Now there are some interesting changes that LLMs are bringing along as well. So most ML models in production that we see are relatively static in the sense that they're not getting flipped in more than maybe once a day or once a week or they're just set once and then not changed ever again. With LLMs, there's this ongoing value alignment or collection of preferences from users that is often constantly updating the model. And so that opens up all sorts of vectors for, I won't say attack, but for problems to arise in production. Like users might learn to use your system in a different way and thus change the way those preferences are getting collected and thus change your system in ways that you never intended. So maybe that went through governance already internally at the company and now it's totally, totally changed and it's through no fault of your own, but you need to be watching over that for sure. >> Talk about the reinforced learnings from human feedback. How's that factoring in to the LLMs? Is that part of it? Should people be thinking about that? Is that a component that's important? >> It certainly is, yeah. So this is one of the big tweaks that happened with InstructGPT, which is the basis model behind ChatGPT and has since gone on to be used all over the place. So value alignment I think is through RLHF like you mentioned is a very interesting space to get into and it's one that you need to watch over. Like, you're asking humans for feedback over outputs from a model and then you're updating the model with respect to that human feedback. And now you've thrown humans into the loop here in a way that is just going to complicate things. And it certainly helps in many ways. You can ask humans to, let's say that you're deploying an internal chat bot at an enterprise, you could ask humans to align that LLM behind the chatbot to, say company values. And so you're listening feedback about these company values and that's going to scoot that chatbot that you're running internally more toward the kind of language that you'd like to use internally on like a Slack channel or something like that. Watching over that model I think in that specific case, that's a compliance and HR issue as well. So while it is part of the greater LLM stack, you can also view that as an independent bit to watch over. >> Got it, and these are important factors. When people see the Bing news, they freak out how it's doing great. Then it goes off the rails, it goes big, fails big. (laughing) So these models people see that, is that human interaction or is that feedback, is that not accepting it or how do people understand how to take that input in and how to build the right apps around LLMs? This is a tough question. >> Yeah, for sure. So some of the examples that you'll see online where these chatbots go off the rails are obviously humans trying to break the system, but some of them clearly aren't. And that's because these are large statistical models and we don't know what's going to pop out of them all the time. And even if you're doing as much in-house testing at the big companies like the Go-HERE's and the OpenAI's of the world, to try to prevent things like toxicity or racism or other sorts of bad content that might lead to bad pr, you're never going to catch all of these possible holes in the model itself. And so, again, it's very, very important to keep watching over that while it's in production. >> On the business model side, how are you guys doing? What's the approach? How do you guys engage with customers? Take a minute to explain the customer engagement. What do they need? What do you need? How's that work? >> Yeah, I can talk a little bit about that. So it's really easy to get started. It's literally a matter of like just handing out an API key and people can get started. And so we also offer alternative, we also offer versions that can be installed on-prem for models that, we find a lot of our customers have models that deal with very sensitive data. So you can run it in your cloud account or use our cloud version. And so yeah, it's pretty easy to get started with this stuff. We find people start using it a lot of times during the validation phase 'cause that way they can start baselining performance models, they can do champion challenger, they can really kind of baseline the performance of, maybe they're considering different foundation models. And so it's a really helpful tool for understanding differences in the way these models perform. And then from there they can just flow that into their production inferencing, so that as these systems are out there, you have really kind of real time monitoring for anomalies and for all sorts of weird behaviors as well as that continuous feedback loop that helps you make make your product get better and observability and you can run all sorts of aggregated reports to really understand what's going on with these models when they're out there deciding. I should also add that we just today have another way to adopt Arthur and that is we are in the AWS marketplace, and so we are available there just to make it that much easier to use your cloud credits, skip the procurement process, and get up and running really quickly. >> And that's great 'cause Amazon's got SageMaker, which handles a lot of privacy stuff, all kinds of cool things, or you can get down and dirty. So I got to ask on the next one, production is a big deal, getting stuff into production. What have you guys learned that you could share to folks watching? Is there a cost issue? I got to monitor, obviously you brought that up, we talked about the even reinforcement issues, all these things are happening. What is the big learnings that you could share for people that are going to put these into production to watch out for, to plan for, or be prepared for, hope for the best plan for the worst? What's your advice? >> I can give a couple opinions there and I'm sure Adam has. Well, yeah, the big one from my side is, again, I had mentioned this earlier, it's just the input data streams because humans are also exploring how they can use these systems to begin with. It's really, really hard to predict the type of inputs you're going to be seeing in production. Especially, we always talk about chatbots, but then any generative text tasks like this, let's say you're taking in news articles and summarizing them or something like that, it's very hard to get a good sampling even of the set of news articles in such a way that you can really predict what's going to pop out of that model. So to me, it's, adversarial maybe isn't the word that I would use, but it's an unnatural shifting input distribution of like prompts that you might see for these models. That's certainly one. And then the second one that I would talk about is, it can be hard to understand the costs, the inference time costs behind these LLMs. So the pricing on these is always changing as the models change size, it might go up, it might go down based on model size, based on energy cost and so on, but your pricing per token or per a thousand tokens and that I think can be difficult for some clients to wrap their head around. Again, you don't know how these systems are going to be used after all so it can be tough. And so again that's another metric that really should be tracked. >> Yeah, and there's a lot of trade off choices in there with like, how many tokens do you want at each step and in the sequence and based on, you have (indistinct) and you reject these tokens and so based on how your system's operating, that can make the cost highly variable. And that's if you're using like an API version that you're paying per token. A lot of people also choose to run these internally and as John mentioned, the inference time on these is significantly higher than a traditional classifi, even NLP classification model or tabular data model, like orders of magnitude higher. And so you really need to understand how that, as you're constantly iterating on these models and putting out new versions and new features in these models, how that's affecting the overall scale of that inference cost because you can use a lot of computing power very quickly with these profits. >> Yeah, scale, performance, price all come together. I got to ask while we're here on the secret sauce of the company, if you had to describe to people out there watching, what's the secret sauce of the company? What's the key to your success? >> Yeah, so John leads our research team and they've had a number of really cool, I think AI as much as it's been hyped for a while, it's still commercial AI at least is really in its infancy. And so the way we're able to pioneer new ways to think about performance for computer vision NLP LLMs is probably the thing that I'm proudest about. John and his team publish papers all the time at Navs and other places. But I think it's really being able to define what performance means for basically any kind of model type and give people really powerful tools to understand that on an ongoing basis. >> John, secret sauce, how would you describe it? You got all the action happening all around you. >> Yeah, well I going to appreciate Adam talking me up like that. No, I. (all laughing) >> Furrier: Robs to you. >> I would also say a couple of other things here. So we have a very strong engineering team and so I think some early hires there really set the standard at a very high bar that we've maintained as we've grown. And I think that's really paid dividends as scalabilities become even more of a challenge in these spaces, right? And so that's not just scalability when it comes to LLMs, that's scalability when it comes to millions of inferences per day, that kind of thing as well in traditional ML models. And I think that's compared to potential competitors, that's really... Well, it's made us able to just operate more efficiently and pass that along to the client. >> Yeah, and I think the infancy comment is really important because it's the beginning. You really is a long journey ahead. A lot of change coming, like I said, it's a huge wave. So I'm sure you guys got a lot of plannings at the foundation even for your own company, so I appreciate the candid response there. Final question for you guys is, what should the top things be for a company in 2023? If I'm going to set the agenda and I'm a customer moving forward, putting the pedal to the metal, so to speak, what are the top things I should be prioritizing or I need to do to be successful with AI in 2023? >> Yeah, I think, so number one, as we talked about, we've been talking about this entire episode, the things are changing so quickly and the opportunities for business transformation and really disrupting different applications, different use cases, is almost, I don't think we've even fully comprehended how big it is. And so really digging in to your business and understanding where I can apply these new sets of foundation models is, that's a top priority. The interesting thing is I think there's another force at play, which is the macroeconomic conditions and a lot of places are, they're having to work harder to justify budgets. So in the past, couple years ago maybe, they had a blank check to spend on AI and AI development at a lot of large enterprises that was limited primarily by the amount of talent they could scoop up. Nowadays these expenditures are getting scrutinized more. And so one of the things that we really help our customers with is like really calculating the ROI on these things. And so if you have models out there performing and you have a new version that you can put out that lifts the performance by 3%, how many tens of millions of dollars does that mean in business benefit? Or if I want to go to get approval from the CFO to spend a few million dollars on this new project, how can I bake in from the beginning the tools to really show the ROI along the way? Because I think in these systems when done well for a software project, the ROI can be like pretty spectacular. Like we see over a hundred percent ROI in the first year on some of these projects. And so, I think in 2023, you just need to be able to show what you're getting for that spend. >> It's a needle moving moment. You see it all the time with some of these aha moments or like, whoa, blown away. John, I want to get your thoughts on this because one of the things that comes up a lot for companies that I talked to, that are on my second wave, I would say coming in, maybe not, maybe the front wave of adopters is talent and team building. You mentioned some of the hires you got were game changing for you guys and set the bar high. As you move the needle, new developers going to need to come in. What's your advice given that you've been a professor, you've seen students, I know a lot of computer science people want to shift, they might not be yet skilled in AI, but they're proficient in programming, is that's going to be another opportunity with open source when things are happening. How do you talk to that next level of talent that wants to come in to this market to supplement teams and be on teams, lead teams? Any advice you have for people who want to build their teams and people who are out there and want to be a coder in AI? >> Yeah, I've advice, and this actually works for what it would take to be a successful AI company in 2023 as well, which is, just don't be afraid to iterate really quickly with these tools. The space is still being explored on what they can be used for. A lot of the tasks that they're used for now right? like creating marketing content using a machine learning is not a new thing to do. It just works really well now. And so I'm excited to see what the next year brings in terms of folks from outside of core computer science who are, other engineers or physicists or chemists or whatever who are learning how to use these increasingly easy to use tools to leverage LLMs for tasks that I think none of us have really thought about before. So that's really, really exciting. And so toward that I would say iterate quickly. Build things on your own, build demos, show them the friends, host them online and you'll learn along the way and you'll have somebody to show for it. And also you'll help us explore that space. >> Guys, congratulations with Arthur. Great company, great picks and shovels opportunities out there for everybody. Iterate fast, get in quickly and don't be afraid to iterate. Great advice and thank you for coming on and being part of the AWS showcase, thanks. >> Yeah, thanks for having us on John. Always a pleasure. >> Yeah, great stuff. Adam Wenchel, John Dickerson with Arthur. Thanks for coming on theCUBE. I'm John Furrier, your host. Generative AI and AWS. Keep it right there for more action with theCUBE. Thanks for watching. (upbeat music)
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Robert Nishihara, Anyscale | AWS Startup Showcase S3 E1
(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)
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episode one of the ongoing and you guys really had and other resources in the Cloud. and particular the large language and what you want to achieve. and the Cloud did that with data centers. the point, and you know, if you don't mind explaining and managing the infrastructure and you guys are positioning is that the amount of compute needed to do But John, I'm curious what you think. because that's the platform So you got tools in the platform. and being the best way to of the computer industry, Did you have an inside prompt and the large language models and tell it what you want it to do. So I have to ask you and you can lower your So I want to get into that, you know, and you know, your big cluster is back up So I think, you know, the on-demand instances. and what you were showing me that demo and run the stuff out of the box. I know you got a couple websites. and the opportunity is there, right. and you got growth ahead
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Opening Panel | Generative AI: Hype or Reality | AWS Startup Showcase S3 E1
(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)
SUMMARY :
of the AWS Startup Showcase, of the behind the ropes, and something that, you know, and build out, you know, Aidan, let's get into what you guys do. and it's trained on, you know, it helps me, you know, the ability to use tools, to use APIs? I call that the people and you know, making sure the first group of adopters We got the language coming in. Tom, on your side, what do you see the- and you know, everything into the models. they want to get into what you guys see and you know, you pick for our customers. then you know, you again, All right, I love the example. and make the most of our models. And so the ability to And so the barrier is coming down- and it's exciting to see. So I have to ask you guys and ensuring that all of the robustness and directly to bring in new and it's the first time in human history the consumers have to win. and it's just the beginning. I'm John Furrier, your host.
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