>>Okay, welcome back. Everyone's Cube's coverage here in Las Vegas for Amazon re Mars machine learning, automation, robotics, and space out. John fir host of the queue. Got a great set of guests here talking about AI, Jason Montgomery CTO and co-founder man and Ryans CEO, founder guys. Thanks for coming on. We're just chatting, lost my train of thought. Cuz we were chatting about something else, your history with DataRobot and, and your backgrounds entrepreneurs. Welcome to the queue. Thanks >>Tur. Thanks for having >>Us. So first, before we get into the conversation, tell me about the company. You guys have a history together, multiple startups, multiple exits. What are you guys working on? Obviously AI is hot here as part of the show. M is Mars machine learning, which we all know is the basis for AI. What's the story. >>Yeah, really. We're we're here for two of the letters and Mars. We're here for the machine learning and the automation part. So at the high level, man is a no code AI application development platform. And basically anybody could log in and start making AI applications. It could be anything from just texting it with the Twilio integration to tell you that you're doing great or that you need to exercise more to integrating with zenes to get support tickets classified. >>So Jason, we were talking too about before he came on camera about the cloud and how you can spin up resources. The data world is coming together and I, and I like to see two flash points. The, I call it the 2010 big data era that began and then failed Hadoop crashed and burned. Yeah. Then out of the, out of the woodwork came data robots and the data stacks and the snowflakes >>Data break snowflake. >>And now you have that world coming back at scale. So we're now seeing a huge era of, I need to stand up infrastructure and platform to do all this heavy lifting. I don't have time to do. Right. That sounds like what you guys are doing. Is that kind of the case? >>That's absolutely correct. Yeah. Typically you would have to hire a whole team. It would take you months to sort of get the infrastructure automation in place, the dev ops DevOps pipelines together. And to do the automation to spin up, spin down, scale up scale down requires a lot of special expertise with, you know, Kubernetes. Yeah. And a lot of the other data pipelines and a lot of the AWS technologies. So we automate a lot of that. So >>If, if DevOps did what they did, infrastructure has code. Yeah. Data has code. This is kind of like that. It's not data ops per se. Is there a category? How do you see this? Cuz it's you could say data ops, but that's also it's DevOps dev. It's a lot going on. Oh yeah. It's not just seeing AI ops, right? There's a lot more, what, what would you call this? >>It's a good question. I don't know if we've quite come up with the name. I know >>It's not data ops. It's not >>Like we call it AI process automation >>SSPA instead of RPA, >>What RPA promised to be. Yes, >>Exactly. But what's the challenge. The number one problem is it's I would say not, not so much all on ever on undifferent heavy lifting. It's a lot of heavy lifting that for sure. Yes. What's involved. What's the consequences of not going this way. If I want to do it myself, can you take me through the, the pros and cons of what the scale scope, the scale of without you guys? >>Yeah. Historically you needed to curate all your data, bring it together and have some sort of data lake or something like that. And then you had to do really a lot of feature engineering and a lot of other sort of data science on the back end and automate the whole thing and deploy it and get it out there. It's a, it's a pretty rigorous and, and challenging problem that, you know, we there's a lot of automation platforms for, but they typically focus on data scientists with these large language models we're using they're pre-trained. So you've sort of taken out that whole first step of all that data collection to start out and you can basically start prototyping almost instantly because they've already got like 6 billion parameters, 10 billion parameters in them. They understand the human language really well. And a lot of other problems. I dunno if you have anything you wanna add to that, Ryan, but >>Yeah, I think the other part is we deal with a lot of organizations that don't have big it teams. Yeah. And it would be impossible quite frankly, for them to ever do something like deploy text, track as an example. Yeah. They're just not gonna do it, but now they can come to us. They know the problem they want solved. They know that they have all these invoices as an example and they wanna run it through a text track. And now with us they can just drag and drop and say, yeah, we want tech extract. Then we wanted to go through this. This is what we >>Want. Expertise is a huge problem. And the fact that it's changing too, right? Yeah. Put that out there. You guys say, you know, cybersecurity challenges. We guys do have a background on that. So you know, all the cutting edge. So this just seems to be this it, I hate to say transformation. Cause I not the word I'm looking for, I'd say stuck in the mud kind of scenario where they can't, they have to get bigger, faster. Yeah. And the scale is bigger and they don't have the people to do it. So you're seeing the rise of managed service. You mentioned Kubernetes, right? I know this young 21 year old kid, he's got a great business. He runs a managed service. Yep. Just for Kubernetes. Why? Because no, one's there to stand up the clusters. >>Yeah. >>It's a big gap. >>So this, you have these sets of services coming in now, where, where do you guys fit into that conversation? If I'm the customer? My problem is what, what is my, what is my problem that I need you guys for? What does it look like to describe my problem? >>Typically you actually, you, you kind of know that your employees are spending a lot of time, a lot of hours. So I'll just give you a real example. We have a customer that they were spending 60 hours a week just reviewing these accounts, payable, invoices, 60 hours a week on that. And they knew there had to be a better way. So manual review manual, like when we got their data, they were showing us these invoices and they had to have their people circle the total on the invoice, highlight the customer name, the >>Person who quit the next day. Right? >>No like they, they, Hey, you know, they had four people doing this, I think. And the point is, is they come to us and we say, well, you know, AI can, can just basically using something like text track can just do this. And then we can enrich those outputs from text track with the AI. So that's where the transformers come in. And when we showed them that and got them up and running in about 30 minutes, they were mind blown. Yeah. And now this is a company that doesn't have a big it department. So the >>Kind, and they had the ability to quantify the problem >>They knew. And, and in this case it was actually a business user. It was not a technical >>In is our she consequence technical it's hours. She consequences that's wasted. Manual, labor wasted. >>Exactly. Yeah. And, and to their point, it was look, we have way more high, valuable tasks that our people could be doing yeah. Than doing this AP thing. It takes 60 hours. And I think that's really important to remember about AI. What're I don't think it's gonna automate away people's jobs. Yeah. What it's going to do is it's going to free us up to focus on what really matters and focus on the high value stuff. And that's what people should >>Be doing. I know it's a cliche. I'm gonna say it again. Cause I keep saying, cause I keep saying for people to listen, the bank teller argument always was the big thing. Oh yeah. They're gonna get killed by the ATM machine. No, they're opening up more branches. That's right. That's right. So it's like, come on. People let's get, get over that. So I, I definitely agree with that. Then the question, next question is what's your secret sauce? I'm the customer I'm gonna like that value proposition. You make something go away. It's a pain relief. Then there's the growth side. Okay. You can solve from problems. Now I want this, the, the vitamin you got aspirin. And I want the vitamin. What's the growth angle for you guys with your customers. What's the big learnings. Once they get the beach head with problem solving. >>I think it, it, it it's the big one is let's say that we start with the account payable thing because it's so our platform's so approachable. They go in and then they start tinkering with the initial, we'll call it a template. So they might say, Hey, you know what, actually, in this edge case, I'm gonna play with this. And not only do I want it to go to our accounting system, but if it's this edge case, I want it to email me. So they'll just drag and drop an email block into our canvas. And now they're making it >>Their own. There is the no code, low code's situation. They're essentially building a notification engine under the covers. They have no idea what they're doing. That's >>Right. They get the, they just know that, Hey, you know what? When, when like the amount's over $10,000, I want an email. They know that's what they want. They don't, they don't know that's the notification engine. Of >>Course that's value email. Exactly. I get what I wanted. All right. So tell me about the secret sauce. What's under the covers. What's the big, big, big scale, valuable, valuable, secret sauce. >>I would say part of it. And, and honestly, the reason that we're able to do this now is transformer architecture. When the transformer papers came out and then of course the attention is all you need paper, those kind of unlocked it and made this all possible. Beyond that. I think the other secret sauce we've been doing this a long time. >>So we kind of, we know we're in the paid points. We went to those band points. Cause we weren't data scientists or ML people. >>Yeah. >>Yeah. You, you walked the snow and no shoes on in the winter. That's right. These kids now got boots on. They're all happy. You've installed machines. You've loaded OSS on, on top of rack switches. Yeah. I mean, it's unbelievable how awesome it's right now to be a developer and now a business user's doing the low code. Yep. If you have the system architecture set up, so back to the data engineering side, you guys had the experience got you here. This is a big discussion right now. We're having in, in, on the cube and many conversations like the server market, you had that go away through Amazon and Google was one of the first, obviously the board, but the idea that servers could be everywhere. So the SRE role came out the site reliability engineer, right. Which was one guy or gal and zillions of servers. Now you're seeing the same kind of role with data engineering. And then there's not a lot of people that fit the requirement of being a data engineer. It's like, yeah, it's very unique. Cause you're dealing with a system architecture, not data science. So start to see the role of this, this, this new persona, because they're taking on all the manual challenges of doing that. You guys are kind of replaced that I think. Well, do you agree with it about the data engineer? First of all? >>I think, yeah. Well and it's different cuz there's the older data engineer and then there's sort of the newer cloud aware one who knows how to use all the cloud technologies. And so when you're trying, we've tried to hire some of those and it's like, okay, you're really familiar with old database technology, but can you orchestrate that in a serverless environment with a lot of AWS technology for instance. And it's, and that's hard though. They don't, they don't, there's not a lot of people who know that space, >>So there's no real curriculum out there. That's gonna teach you how to handle, you know, ETL. And also like I got I'm on stream data from this source. Right. I'm using sequel I'm I got put all together. >>Yeah. So it's yeah, it's a lot of just not >>Data science. It's >>Figure that out. So its a large language models too. We don't have to worry about some of the data there too. It's it's already, you know, codified in the model. And then as we collect data, as people use our platform, they can then curate data. They want to annotate or enrich the model with so that it works better as it goes. So we're kind of curating, collecting the data as it's used. So as it evolves, it just gets better. >>Well, you guys obviously have a lot of experience together and congratulations on the venture. Thank you. What's going on here at re Mars. Why are you here? What's the pitch. What's the story. Where's your, you got two letters. You got the, you got the M for the machine learning and AI and you got the, a for automation. What's the ecosystem here for you? What are you doing? >>Well, I mean, I think you, you kind of said it right. We're here because the machine learning and the automation part, >>But >>More, more widely than that. I mean we work very, very closely with Amazon on a number of front things like text track, transcribe Alexa, basically all these AWS services are just integrations within our system. So you might want to hook up your AI to an Alexa so that you could say, Hey Alexa, tell me updates about my LinkedIn feed. I don't know, whatever, whatever your hearts content >>Is. Well what about this cube transcription? >>Yeah, exactly. A hundred percent. >>Yeah. We could do that. You know, feed all this in there and then we could do summarization of everything >>Here, >>Q and a extraction >>And say, Hey, these guys are >>Technicals. Yeah, >>There you go. No, they mentioned Kubernetes. We didn't say serverless chef puppet. Those are words straight, you know, and no linguistics matters right into that's a service that no one's ever gonna build. >>Well, and actually on that point, really interesting. We work with some healthcare companies and when you're basically, when people call in and they call into the insurance, they have a question about their, what like is this gonna be covered? And what they want to key in on are things like I just went to my doctor and got a cancer diagnosis. So the, the, the relevant thing here is they just got this diagnosis. And why is that important? Well, because if you just got a diagnosis, they want to start a certain triage to make you successful with your treatments. Because obviously there's an >>Incentive to do time. That time series matters and, and data exactly. And machine learning reacts to it. But also it could be fed back old data. It used to be time series to store it. Yeah. But now you could reuse it to see how to make the machine learning better. Are you guys doing anything, anything around that, how to make that machine learning smarter, look doing look backs or maybe not the right word, but because you have data, I might as well look back at it's happened. >>So part of, part of our platform and part of what we do is as people use these applications, to your point, there's lots of data that's getting generated, but we capture all that. And that becomes now a labeled data set within our platform. And you can take that label data set and do something called fine tuning, which just makes the underlying model more and more yours. It's proprietary. The more you do it. And it's more accurate. Usually the more you do it. >>So yeah, we keep all that. I wanna ask your reaction on this is a good point. The competitive advantage in the intellectual property is gonna be the workflows. And so the data is the IP. If this refinement happens, that becomes intellectual property. Yeah. That's kind of not software. It's the data modeling. It's the data itself is worth something. Are you guys seeing that? >>Yeah. And actually how we position the company is man team is a control plane and you retain ownership of the data plane. So it is your intellectual property. Yeah. It's in your system, it's in your AWS environment. >>That's not what everyone else is doing. Everyone wants to be the control plane and the data plan. We >>Don't wanna own your data. We don't, it's a compliance and security nightmare. Yeah. >>Let's be, Real's the question. What do you optimize for? Great. And I think that's a fair, a fair bet. Given the fact that clients want to be more agile with their data anyway, and the more restrictions you put on them, why would that this only gets you in trouble? Yeah. I could see that being a and plus lock. In's gonna be a huge factor. Yeah. I think this is coming fast and no one's talking about it in the press, but everyone's like run to silos, be a silo and that's not how data works. No. So the question is how do you create siloing of data for say domain specific applications while maintaining a horizontally scalable data plan or control plan that seems to be kind of disconnected everyone to lock in their data. What do you guys think about that? This industry transition we're in now because it seems people are reverting back to fourth grade, right. And to, you know, back to silos. >>Yeah. I think, well, I think the companies probably want their silo of data, their IP. And so as they refine their models and, and we give them the ability to deploy it in their own stage maker and their own VPC, they, they retain and own it. They can actually get rid of us and they still have that model. Now they may have to build, you know, a lot of pipelines and other technology to support it. But well, >>Your lock in is usability. Exactly. And value. Yeah. Value proposition is the lock in bingo. That's not counterintuitive. Exactly. Yeah. You say, Hey, more value. How do I wanna get rid of it? Valuable. I'll pay for it. Right. As long as you have multiple value, step up. And that's what cloud does. I mean, think that's the thing about cloud. That's gonna make all this work. In my opinion, the value enablement is much higher. Yeah. So good business model. Anything else here at the show that you observed that you like, that you think people would be interested in? What's the most important story coming out of the, the holistic, if you zoom up and look at re Mars, what's, what's coming out of the vibe. >>You know, one thing that I think about a lot is we're, you know, we have Artis here, humanity hopefully soon gonna be going to Mars. And I think that's really, really exciting. And I also think when we go to Mars, we're probably not gonna send a bunch of software engineers up there. >>Right. So like robots will do break fix now. So, you know, we're good. It's gone. So services are gonna be easy. >>Yeah. But I, oh, >>I left that device back at earth. I just think that's not gonna be good. Just >>Replicated it in one. I think there's like an eight >>Minute, the first monopoly on next day delivery in space. >>They'll just have a spaceship that sends out drones to Barss. Yeah. But I think that when we start going back to the moon and we go to Mars, people are gonna think, Hey, I need this application now to solve this problem that I didn't anticipate having. And in science fiction, we kind of saw this with like how, right? Like you had this AI on this computer or this, on this spaceship that could do all this stuff. We need that. And I haven't seen that here yet. >>No, it's not >>Here yet. And >>It's right now I think getting the hardware right first. Yep. But we did a lot of reporting on this with the D O D and the tactile edge, you know, military applications. It's a fundamental, I won't say it's a tech, religious argument. Like, do you believe in agile realtime data or do you believe in democratizing multi-vendor, you know, capability? I think, I think the interesting needs to sort itself out because sometimes multi vendor multi-cloud might not work for an application that needs this database or this application at the edge. >>Right. >>You know, so if you're in space, the back haul, it matters. >>It really does. Yeah. >>Yeah. Not a good time to go back and get that highly available data. You mean highly, is it highly available or there's two terms highly available, which means real time and available. Yeah. Available means it's on a dis, right? >>Yeah. >>So that's a big challenge. Well guys, thanks for coming on. Plug for the company. What are you guys up to? How much funding do you have? How old are you staff hiring? What's some of the details. >>We're about 45 people right now. We are a globally distributed team. So we hire every like from every country, pretty much we are fully remote. So if you're looking for that, hit us up, definitely always look for engineers, looking for more data scientists. We're very, very well funded as well. And yeah. So >>You guys headquarters out, you guys headquartered. >>So a lot of us live in Columbus, Ohio that's technically HQ, but like I said, we we're in pretty much every continent except in Antarctica. So >>You're for all virtual. >>Yeah. A hundred percent virtual, a hundred percent. >>Got it. Well, congratulations and love to hear that Datadog story at another time >>Or DataBot >>Yeah. I mean data, DataBot sorry. Let's get, get all confused >>Data dog data company. >>Well, thanks for coming on and congratulations for your success and thanks for sharing. Yeah. >>Thanks for having us for having >>Pleasure to be here. It's a cube here at rebars. I'm John furier host. Thanks for watching more coming back after this short break.