>> Voiceover: From Denver, Colorado, it's theCUBE covering Super Computing '17, brought to you by Intel. Hey, welcome back, everybody. Jeff Frick, here, with theCUBE. We're in Denver, Colorado at Super Computing 2017. It's all things heavy lifting, big iron, 12,000 people. I think it's the 20th anniversary of the conference. A lot of academics, really talking about big iron, doin' big computing. And we're excited to have our next guest, talking about speed, he's Stephane Monoboisset. Did I get that right? That's right. He's a director of marketing and partnerships for Accelize. Welcome. Thank you. So, for folks that aren't familiar with Accelize, give them kind of the quick overview. Okay, so Accelize is a French startup. Actually, a spinoff for a company called PLDA that has been around for 20 years, doing PCI express IP. And about a few years ago, we started initiative to basically bring FPGA acceleration to the cloud industry. So what we say is, we basically enable FPGA acceleration as a service. So did it not exist in cloud service providers before that, or what was kind of the opportunity that you saw there? So, FPGAs have been used in data centers in many different ways. They're starting to make their way into, as a service type of approach. But one of the thing that the industry, one of the buzzword that the industry's using, is FPGA as a service. And the industry usually refers to it as the way to bring FPGA to the end users. But when you think about it, end users don't really want FPGA as a service. Most of the cloud end users are not FPGA experts. So they couldn't care less whether it's an FPGA or something else. What they really want is the acceleration benefits. Hence the term, FPGA acceleration as a service. So, in order to do that, instead of just going and offering an FPGA platform, and giving them the tools, even if they are easy to use and develop the FPGAs, our objective is to propose to provide a marketplace of accelerators that they can use as a service, without even thinking that it's an FPGA on the background. So that's a really interesting concept. Because that also leverages an ecosystem. And one thing we know that's important, if you have any kind of a platform playing, you need an ecosystem that brings a much broader breadth of applications, and solution suites, and there's a lot of talk about solutions. So that was pretty insightful, 'cause now you open it up to this much broader set of applications. Well, absolutely. The ecosystem is the essential part of the offering because obviously, as a company, we cannot be expert in every single domain. And to a certain extent, even FPGA designers, they are what, about maybe 10, 15,000 FPGA designers in the world. They are not really expert in the end application. So one of the challenges that we're trying to address is how do we make application developers, the people who are already playing in the cloud, the ISVs, for example, who have the expertise of what the end user wants, being able to develop something that is efficient to the end user in FPGAs. And this is why we've created a tool called Quick Play, which basically enables what we call the accelerator function developers, the guys who have the application expertise, to leverage an ecosystem of IP providers in the FPGA space that have built efficient building blocks, like encryption, compression, video transcoding. Right. These sort of things. So what you have is an ecosystem of cloud service providers. You have an ecosystem of IP providers. And we have this growing ecosystem of accelerator developers that develop all these accelerators that are sold as a service. And that really opens up the number of people that are qualified to play in the space. 'Cause you're kind of hiding the complexity into the hardcore, harder engineers and really making it more kind of a traditional software application space. Is that right? Yeah, you're absolutely right. And we're doing that on the technical front, but we're also doing that on the business model front. Because one thing with FPGAs is that FPGAs has relied heavily over the years on the IP industry. And the IP industry for FPGAs, and it's the same for ASIGs, have been also relying on the business model, which is based on very high up-front cost. So let me give you an example. Let's say I want to develop an accelerator, right, for database. And what I need to do is to get the stream of data coming in. It's most likely encrypted, so I need to decrypt this data, then I want to do some search algorithm on it to extract certain functions. I'm going to do some processing on it, and maybe the last thing I want to do is, I want to compress because I want to store the result of that data. If I'm doing that with a traditional IP business model, what I need to do is basically go and talk to every single one of those IP providers and ask them to sell me the IP. In the traditional IP business model, I'm looking at somewhere between 200,000 to 500,000 up front cost. And I want to sell this accelerator for maybe a couple of dollars on one of the marketplace. There's something that doesn't play out. So what we've done, also, is we've introduced a pay-per-use business model that allows us to track those IPs that are being used by the accelerators so we can propagate the as-a-service business model throughout the industry, the supply chain. Which is huge, right? 'Cause as much as cloud is about flexibility and extensibility, it's about the business model as well. About paying what you use when you use it, turning it on, turning it off. So that's a pretty critical success factor. Absolutely, I mean, you can imagine that there's, I don't know, millions of users in the cloud. There's maybe hundreds of thousands of different type of ways they're processing their data. So we also need a very agile ecosystem that can develop very quickly. And we also need them to do it in a way that doesn't cost too much money, right? Think about it, and think about the app store when it was launched, right? Right. When Apple launched the iPhone back about 10 years ago, right, they didn't have much application. And they didn't, I don't think they quite knew, exactly, how it was going to be used. But what they did, which completely changed the industry, is they opened up the SDK that they sold for very small amount of money and enabled a huge community to come up with a lot of a lot of application. And now you go there and you can find application that really meats your need. That's kind of the similar concept that we're trying to develop here. Right. So how's been the uptake? I mean, so where are you, kind of, in the life cycle of this project? 'Cause it's a relatively new spinout of the larger company? Yes, so it's relatively new. We did the spinout because we really want to give that product its own life. Right, right. Right? But we are still at the beginning. So we started a developing partnership with cloud service providers. The two ones that we've announced is Amazon Web Services and OVH, the cloud service provider in France. And we have recruited, I think, about a dozen IP partners. And now we're also working with accelerator developer, accelerator functions developers. Okay. So it's a work in progress. And our main goal right now is to, really to evangelize, and to show them how much money they can do and how they can serve this market of FPGA acceleration as a service. The cloud providers, or the application providers? Who do you really have to convince the most? So the one we have to convince today are really the application developers. Okay, okay. Because without content, your marketplace doesn't mean much. So this is the main thing we're focusing on right now. Okay, great. So, 2017's coming to an end, which is hard to believe. So as you look forward to 2018, of those things you just outlined, kind of what are some of the top priorities for 2018? So, top priorities will be to strengthen our relationship with the key cloud service providers we work with. We have a couple of other discussions ongoing to try to offer a platform on more cloud service providers. We also want to strengthen our relationship with Intel. And we'll continue the evangelization to really onboard all the IP providers and the accelerator developers so that the marketplace becomes filled with valuable accelerators that people can use. And that's going to be a long process, but we are focusing right now on key application space that we know people can leverage in the application. Exciting times. Oh yeah, it is. You know, it's 10 years since the app store launched, I think, so I look at acceleration as a service in cloud service providers, this sounds like a terrific opportunity. It is, it is a huge opportunity. Everybody's talking about it. We just need to materialize it now. All right, well, congratulations and thanks for taking a couple minutes out of your day. Oh, thanks for your time. All right, he's Stephane, I'm Jeff Frick. You're watching theCUBE from Super Computing 2017. Thanks for watching. (upbeat music)

>>Thanks. Hey, everyone, welcome back to the cubes. Continuing coverage of snowflakes. Summit 22 live from Caesars Forum in Las Vegas. Lisa Martin here. I have three guests here with me. We're gonna be talking about Snowflake Ventures and the snowflakes start up Challenge. That's in its second year. I've got Harry Glaser with me. Co founder and CEO of Model Bit Start Up Challenge finalist Damon Bryan joins us as well. The CTO and co founder of Hyper Affinity. Also a startup Challenge Finalists. And Stephane Williams to my left here, VP of Corporate development and snowflake Ventures. Guys, great to have you all on this little mini panel this morning. >>Thank you. >>Thank you. >>Let's go ahead, Harry, and we'll start with you. Talk to the audience about model. But what do you guys do? And then we'll kind of unpack the snowflake. The Snowflakes challenge >>Model bit is the easiest way for data scientists to deploy machine learning models directly into Snowflake. We make use of the latest snowflake functionality called Snow Park for python that allows those models to run adjacent to the data so that machine learning models can be much more efficient and much more powerful than they were before. >>Awesome. Damon. Give us an overview of hyper affinity. >>Yes, so hyper affinity were Decision Intelligence platform. So we helped. Specifically retailers and brands make intelligent decisions through the use of their own customer, data their product data and put data science in a I into the heart of the decision makers across their business. >>Nice Step seven. Tell us about the startup challenge. We talked a little bit about it yesterday with CMO Denise Pearson, but I know it's in its second year. Give us the idea of the impetus for it, what it's all about and what these companies embody. >>Yeah, so we This is the second year that we've done it. Um, we it was really out of, um Well, it starts with snowflake Ventures when we started to invest in companies, and we quickly realised that there's there's a massive opportunity for companies to be building on top of the Lego blocks, uh, of snowflake. And so, um, open up the competition. Last year it was the inaugural competition overlay analytics one, Um, and since then, you've seen a number of different functionalities and features as part of snowflakes snow part. Being one of them native applications is a really exciting one going forward. Um, the companies can really use to accelerate their ability to kind of deliver best in class applications using best in class technology to deliver real customer outcomes and value. Um, so we've we've seen tremendous traction across the globe, 250 applicants across 50. I think 70 countries was mentioned today, so truly global in nature. And it's really exciting to see how some of the start ups are taking snowflake to to to new and interesting use cases and new personas and new industries. >>So you had 200 over 250 software companies applied for this. How did you did you narrow it down to three? >>We did. Yeah, >>you do that. >>So, behind the scenes, we had a sub judging panel, the ones you didn't see up on stage, which I was luckily part of. We had kind of very distinct evaluation criteria that we were evaluating every company across. Um and we kind of took in tranches, right? We we took the first big garden, and we kind of try to get that down to a top 50 and top 50. Then we really went into the details and we kind of across, um, myself in ventures with some of my venture partners. Um, some of the market teams, some of the product and engineering team, all kind of came together and evaluated all of these different companies to get to the top 10, which was our semifinalists and then the semi finalists, or had a chance to present in front of the group. So we get. We got to meet over Zoom along the way where they did a pitch, a five minute pitch followed by a Q and A in a similar former, I guess, to what we just went through the startup challenge live, um, to get to the top three. And then here we are today, just coming out of the competition with with With folks here on the table. >>Wow, Harry talked to us about How did you just still down what model bit is doing into five minutes over Zoom and then five minutes this morning in person? >>I think it was really fun to have that pressure test where, you know, we've only been doing this for a short time. In fact model. It's only been a company for four or five months now, and to have this process where we pitch and pitch again and pitch again and pitch again really helped us nail the one sentence value proposition, which we hadn't done previously. So in that way, very grateful to step on in the team for giving us that opportunity. >>That helps tremendously. I can imagine being a 4 to 5 months young start up and really trying to figure out I've worked with those young start ups before. Messaging is challenging the narrative. Who are we? What do we do? How are we changing or chasing the market? What are our customers saying we are? That's challenging. So this was a good opportunity for you, Damon. Would you say the same as well for hyper affinity? >>Yeah, definitely conquer. It's really helped us to shape our our value proposition early and how we speak about that. It's quite complicated stuff, data science when you're trying to get across what you do, especially in retail, that we work in. So part of what our platform does is to help them make sense of data science and Ai and implement that into commercial decisions. So you have to be really kind of snappy with how you position things. And it's really helped us to do that. We're a little bit further down the line than than these guys we've been going for three years. So we've had the benefit of working with a lot of retailers to this point to actually identify what their problems are and shape our product and our proposition towards. >>Are you primarily working with the retail industry? >>Yes, Retail and CPG? Our primary use case. We have seen any kind of consumer related industries. >>Got it. Massive changes right in retail and CPG the last couple of years, the rise of consumer expectations. It's not going to go back down, right? We're impatient. We want brands to know who we are. I want you to deliver relevant content to me that if I if I bought a tent, go back on your website, don't show me more tense. Show me things that go with that. We have this expectation. You >>just explain the whole business. But >>it's so challenging because the brothers brands have to respond to that. How do you what is the value for retailers working with hyper affinity and snowflake together. What's that powerhouse? >>Yeah, exactly. So you're exactly right. The retail landscape is changing massively. There's inflation everywhere. The pandemic really impacted what consumers really value out of shopping with retailers. And those decisions are even harder for retailers to make. So that's kind of what our platform does. It helps them to make those decisions quickly, get the power of data science or democratise it into the hands of those decision makers. Um, so our platform helps to do that. And Snowflake really underpins that. You know, the scalability of snowflake means that we can scale the data and the capability that platform in tangent with that and snowflake have been innovating a lot of things like Snow Park and then the new announcements, announcements, uni store and a native APP framework really helping us to make developments to our product as quick as snowflakes are doing it. So it's really beneficial. >>You get kind of that tailwind from snowflakes acceleration. It sounds like >>exactly that. Yeah. So as soon as we hear about new things were like, Can we use it? You know, and Snow Park in particular was music to our ears, and we actually part of private preview for that. So we've been using that while and again some of the new developments will be. I'm on the phone to my guys saying, Can we use this? Get it, get it implemented pretty quickly. So yeah, >>fantastic. Sounds like a great aligned partnership there, Harry. Talk to us a little bit about model bit and how it's enabling customers. Maybe you've got a favourite customer example at model bit plus snowflake, the power that delivers to the end user customer? >>Absolutely. I mean, as I said, it allows you to deploy the M L model directly into snowflake. But sometimes you need to use the exact same machine learning model in multiple endpoints simultaneously. For example, one of our customers uses model bit to train and deploy a lead scoring model. So you know when somebody comes into your website and they fill out the form like they want to talk to a sales person, is this gonna be a really good customer? Do we think or maybe not so great? Maybe they won't pay quite as much, and that lead scoring model actually runs on the website using model bit so that you can deploy display a custom experience to that customer we know right away. If this is an A, B, C or D lead, and therefore do we show them a salesperson contact form? Do we just put them in the marketing funnel? Based on that lead score simultaneously, the business needs to know in the back office the score of the lead so that they can do things like routed to the appropriate salesperson or update their sales forecasts for the end of the quarter. That same model also runs in the in the snowflake warehouse so that those back office systems can be powered directly off of snowflake. The fact that they're able to train and deploy one model into two production environment simultaneously and manage all that is something they can only do with bottled it. >>Lead scoring has been traditionally challenging for businesses in every industry, but it's so incredibly important, especially as consumers get pickier and pickier with. I don't want I don't want to be measured. I want to opt out. What sounds like what model but is enabling is especially alignment between sales and marketing within companies, which is That's also a big challenge at many companies face for >>us. It starts with the data scientist, right? The fact that sales and marketing may not be aligned might be an issue with the source of truth. And do we have a source of truth at this company? And so the idea that we can empower these data scientists who are creating this value in the company by giving them best in class tools and resources That's our dream. That's our mission. >>Talk to me a little bit, Harry. You said you're only 4 to 5 months old. What were the gaps in the market that you and your co founders saw and said, Guys, we've got to solve this. And Snowflake is the right partner to help us do it. >>Absolutely. We This is actually our second start up, and we started previously a data Analytics company that was somewhat successful, and it got caught up in this big wave of migration of cloud tools. So all of data tools moved and are moving from on premise tools to cloud based tools. This is really a migration. That snowflake catalyst Snowflake, of course, is the ultimate in cloud based data platforms, moving customers from on premise data warehouses to modern cloud based data clouds that dragged and pulled the rest of the industry along with it. Data Science is one of the last pieces of the data industry that really hasn't moved to the cloud yet. We were almost surprised when we got done with our last start up. We were thinking about what to do next. The data scientists were still using Jupiter notebooks locally on their laptops, and we thought, This is a big market opportunity and we're We're almost surprised it hasn't been captured yet, and we're going to get in there. >>The other thing. I think it's really interesting on your business that we haven't talked about is just the the flow of data, right? So that the data scientist is usually taking data out of a of a of a day like something like Smoke like a data platform and the security kind of breaks down because then it's one. It's two, it's three, it's five, it's 20. Its, you know, big companies just gets really big. And so I think the really interesting thing with what you guys are doing is enabling the data to stay where it's at, not copping out keeping that security, that that highly governed environment that big companies want but allowing the data science community to really unlock that value from the data, which is really, really >>cool. Wonderful for small startups like Model Bit. Because you talk to a big company, you want them to become a customer. You want them to use your data science technology. They want to see your fed ramp certification. They want to talk to your C. So we're two guys in Silicon Valley with a dream. But if we can tell them the data is staying in snowflake and you have that conversation with Snowflake all the time and you trust them were just built on top. That is an easy and very smooth way to have that conversation with the customer. >>Would you both say that there's credibility like you got street cred, especially being so so early in this stage? Harry, with the partnership with With Snowflake Damon, we'll start with you. >>Yeah, absolutely. We've been using Snowflake from day one. We leave from when we started our company, and it was a little bit of an unknown, I guess maybe 23 years ago, especially in retail. A lot of retailers using all the legacy kind of enterprise software, are really starting to adopt the cloud now with what they're doing and obviously snowflake really innovating in that area. So what we're finding is we use Snowflake to host our platform and our infrastructure. We're finding a lot of retailers doing that as well, which makes it great for when they wanted to use products like ours because of the whole data share thing. It just becomes really easy. And it really simplifies it'll and data transformation and data sharing. >>Stephane, talk about the startup challenge, the innovation that you guys have seen, and only the second year I can. I can just hear it from the two of you. And I know that the winner is back in India, but tremendous amount of of potential, like to me the last 2.5 days, the flywheel that is snowflake is getting faster and faster and more and more powerful. What are some of the things that excite you about working on the start up challenge and some of the vision going forward that it's driving. >>I think the incredible thing about Snowflake is that we really focus as a company on the data infrastructure and and we're hyper focused on enabling and incubating and encouraging partners to kind of stand on top of a best of breed platform, um, unlocked value across the different, either personas within I T organisations or industries like hypothermia is doing. And so it's it's it's really incredible to see kind of domain knowledge and subject matter expertise, able to kind of plug into best of breed underlying data infrastructure and really divide, drive, drive real meaningful outcomes for for for our customers in the community. Um, it's just been incredible to see. I mean, we just saw three today. Um, there was 250 incredible applications that past the initial. Like, do they check all the boxes and then actually, wow, they just take you to these completely different areas. You never thought that the technology would go and solve. And yet here we are talking about, you know, really interesting use cases that have partners are taking us to two >>150. Did that surprise you? And what was it last year. >>I think it was actually close to close to 2 to 40 to 50 as well, and I think it was above to 50 this year. I think that's the number that is in my head from last year, but I think it's actually above that. But the momentum is, Yeah, it's there and and again, we're gonna be back next year with the full competition, too. So >>awesome. Harry, what is what are some of the things that are next for model bed as it progresses through its early stages? >>You know, one thing I've learned and I think probably everyone at this table has internalised this lesson. Product market fit really is everything for a start up. And so for us, it's We're fortunate to have a set of early design partners who will become our customers, who we work with every day to build features, get their feedback, make sure they love the product, and the most exciting thing that happened to me here this week was one of our early design partner. Customers wanted us to completely rethink how we integrate with gets so that they can use their CI CD workflows their continuous integration that they have in their own get platform, which is advanced. They've built it over many years, and so can they back, all of model, but with their get. And it was it was one of those conversations. I know this is getting a little bit in the weeds, but it was one of those conversations that, as a founder, makes your head explode. If we can have a critical mass of those conversations and get to that product market fit, then the flywheel starts. Then the investment money comes. Then you're hiring a big team and you're off to the races. >>Awesome. Sounds like there's a lot of potential and momentum there. Damon. Last question for you is what's next for hyper affinity. Obviously you've got we talked about the street cred. >>Yeah, what's >>next for the business? >>Well, so yeah, we we've got a lot of exciting times coming up, so we're about to really fully launch our products. So we've been trading for three years with consultancy in retail analytics and data science and actually using our product before it was fully ready to launch. So we have the kind of main launch of our product and we actually starting to onboard some clients now as we speak. Um, I think the climate with regards to trying to find data, science, resources, you know, a problem across the globe. So it really helps companies like ours that allow, you know, allow retailers or whoever is to democratise the use of data science. And perhaps, you know, really help them in this current climate where they're struggling to get world class resource to enable them to do that >>right so critical stuff and take us home with your overall summary of snowflake summit. Fourth annual, nearly 10,000 people here. Huge increase from the last time we were all in person. What's your bumper sticker takeaway from Summit 22 the Startup Challenge? >>Uh, that's a big closing statement for me. It's been just the energy. It's been incredible energy, incredible excitement. I feel the the products that have been unveiled just unlock a tonne, more value and a tonne, more interesting things for companies like the model bit I profanity and all the other startups here. And to go and think about so there's there's just this incredible energy, incredible excitement, both internally, our product and engineering teams, the partners that we have spoke. I've spoken here with the event, the portfolio companies that we've invested in. And so there's there's there's just this. Yeah, incredible momentum and excitement around what we're able to do with data in today's world, powered by underlying platform, like snowflakes. >>Right? And we've heard that energy, I think, through l 30 plus guests we've had on the show since Tuesday and certainly from the two of you as well. Congratulations on being finalist. We wish you the best of luck. You have to come back next year and talk about some of the great things. More great >>things hopefully will be exhibited next year. >>Yeah, that's a good thing to look for. Guys really appreciate your time and your insights. Congratulations on another successful start up challenge. >>Thank you so much >>for Harry, Damon and Stefan. I'm Lisa Martin. You're watching the cubes. Continuing coverage of snowflakes. Summit 22 live from Vegas. Stick around. We'll be right back with a volonte and our final guest of the day. Mhm, mhm