>>Welcome back to the cubes coverage here in Seattle for AWS marketplace seller conference, the combination of the Amazon partner network, combined with the marketplace from the AWS partner organization, the APO and John Forer host of the queue, bringing you all the action and what it all means. Our next guest is Trisha kata, Malva, chief strategy officer at DataRobot. Great to have you. Thanks for coming on. >>Thank you, John. Great to be here. >>So DataRobot obviously in the big data business data is the big theme here. A lot of companies are in the marketplace selling data solutions. I just ran into snowflake person. I ran into another data analyst company, lot of, lot of data everywhere. You're seeing security. You're seeing insights a lot more going on with data than ever before. It's one of the most popular categories in the marketplace. Talk about DataRobot what you guys are doing. What's your product in there? Yeah, >>Absolutely. John. So we are an artificial intelligence machine learning platform company. We have been around for 10 years. This is this year marks our 10th anniversary and we provide a platform for data scientists and also citizen data scientists. So essentially wanna be data scientists on the business side to rapidly experiment with data and to get insights and then productionize ML models. So the 100% workflow that goes into identifying the data that you need for machine learning and then building models on top of that and operationalizing a, >>How big is the company, roughly employee count? What's the number in >>General general, about a thousand employees. And we have customers all over the world. Our biggest verticals are financial services, insurance, manufacturing, healthcare pharma, all the highly regulated, as well as our tech presence is also growing. And we have people spread across multiple geographies and I can't disclose a customer number, but needless to say, we have hundreds of customers across the >>World. A lot of customers. Yeah, yeah. You guys are well known in the industry have been following some of the recent news lately as well. Yeah. Obviously data's exploding. What in the marketplace are you guys offering? What's the pitch, someone hits the marketplace that wants to buy DataRobot what's the pitch. >>The pitch is if you're looking to get real value from your data science, personal investments and your data, then you have DataRobot that you can download from your AWS marketplace. You can do a free trial and essentially get from, get value from data in a matter of minutes and not months or quarters, that's generally associated with IML. And after that, if you want to purchase you, it's a private offer on, in the marketplace. So you need to call DataRobot representative, but AWS marketplace offers a fantastic distribution channel for us. >>Yeah. I mean, one of the things I heard Chris say, who's now heading up the marketplace and the partner network was the streamlining, a lot of the benefits for the sellers and for the buyers to have a great experience buyers. Clearly we see this as a macro trend, that's gonna only get stronger in terms of self-service buying bundling, having the console on AWS for low level services like infrastructure. But now you've got other business applications that like analytics applies to. You're seeing that work. Now he said things like than the keynote, I wanna get your reaction to like, we're gonna make this more like a C I C D pipeline. We're gonna have more native services built into AWS. What that means to me is that sounds like, oh, if I have a solution, like DataRobot, that can be more native into AWS level services. How do you see that working out for you guys is that play well for your strategy and your customers? What's the, what's the what's resonating with the >>Customers. It plays extremely well with the strategy. So I call this as a win, win, win strategy, win for DataRobot win for customers and win for AWS, which is our partner. And it's a win for DataRobot because the amount of people, the number of eyeballs that look at AWS marketplace, a significantly higher than, than the doors that we can go knock on. So it's a distribution multiplier for us. And the integration into AWS services that you're talking about. It is very important because in this day and age, we need to be interoperable with cloud player services that they offer, whether it is with SageMaker or Redshift, we support all of those. And it's a win for customers because customers, it is a very important growing buyer persona for DataRobot. Yeah. And they already have pre-committed spend with AWS and they can use the, those spend dollars for DataRobot to procure DataRobot. So it eases their procurement life cycle as >>Well. It's a forced multiplier on, on the revenue side, correct? I mean, as well as, as on the business front cost of sales, go down the cost of order dollar. Correct. This is good. Goodness. >>It's it's definitely sorry, just to finish my thought on the win for the partner for AWS. It's great win for them because they're getting the consumption from the partner side, to your point on the force multiplier. Absolutely. It is a force multiplier on the revenue side, and it's great for customers and us, because for us, we have seen that the deal size increases when there is the cloud commit that we can draw down for, for our customers, the procurement cycle shortens. And also we have multiple constituencies within the customers working together in a very seamless fashion. >>How has the procurement going through AWS helped your customers? What specific things are you seeing that are popping out as benefits to the customer? >>So from a procurement standpoint, we, we are early in our marketplace journey. We got listed about a year ago, but the amount of revenue that has gone through marketplace is pretty significant at DataRobot. We experienced like just in, by, I think this quarter until this quarter, we got like about 20 to 30 transactions that went through AWS marketplace. And that is significant within just a year of us operating on the marketplace. And the procurement becomes easier for our customers. Yeah. Because they trust AWS and we can put our legal paperwork through the AWS machine as well, which we haven't done yet. But if we do that, that'll be a further force multiplier because that's the, the less friction there is. >>I like how you say that it's a machine. Yeah. And if you think about the benefits too, like one of the things that I see happening, and I love to get your thoughts because I think this is what's happening here. Infrastructure services, I get that IAS done hardware I'm oversimplifying, but all the, all the goodness, but as customers have business apps and vertical market solutions, you got more AI involved. You need more data that's specialized for that use case. Or you need a business application. Those, you don't hear words like let's provision that app. I mean, your provision hardware and, and infrastructure, but the, the new net cloud native is that you provision turn on the apps. So you're seeing the wave of building apps are composing Lego blocks, if you will. So it seems like the customers are starting to assemble the solution, almost like deploying a service, correct. And just pressing a button. And it happens. This seems to be where the, the business apps are going. >>Yeah, absolutely. You agree for us? We are, we are a data science platform and for us being very close to the data that the customers have is very important. And where if, if the customer's data is in Redshift, we are close to there. So being very close to the hyperscale or ecosystem in that entire C I C D pipeline, and also the data platform pipeline is very important. >>You know, what's interesting is, is the data is such a big part of, I mean, DevOps infrastructure has code has been the movement for decade. Yeah. So throw security in there. It's dev SecOps. Yeah. That is the developer now. Yeah. They're running essentially what used to be it now the new ops is security and data. Yeah. You see, in those teams really level up to be highly high velocity data meshes, semantic layer. These are words I'm hearing in the industry around the big waves of data, having this mesh. Yeah. Having it connected. So you're starting to see data availability become more pervasive. And, and we see this as a way that's powering this next gen data science revolution where it's like the business person is now the data science person. >>That's exactly. That is, that is what DataRobot does the best. We were founded with the vision that we wanted to democratize the access to AI within enterprises. It shouldn't be restricted to a small group of people don't get me wrong. Data scientists also love DataRobot. They use DataRobot. But the mission is to enhance many, many hundreds of people within an organization to use data science, like how you use Tableau on a regular basis, how you use Microsoft Excel on a regular basis. We want to democratize AI. And when you want to democratize AI, you need to democratize access to data, which is, which could be stored in data marketplaces, which could be stored in data warehouses and push all the intelligence that we grab from that data into the E R P into the apps layer. Because at the end of the day, business users, customers consume predictions through applications layer. >>You know, it's interesting, you mentioned that comment about, you know, trying not to, to offend data scientists, it's actually a rising tide that the tsunami of data is actually making that population bigger too. Right. So correct. You also have data engineering, which has come out of the woodwork. We covered a lot on the cube, which is, you know, we call data as code. So infrastructure as code kind of a spoof on that. But the reality is that there's a lot more data engineering. I call that the smallest population. Those are the, those are the alphas, the alpha geeks. Yeah. Hardcore data operating systems, kind of education, data science, big pool growing. And then the users yeah. Are the new data science practitioners. Correct? Exactly. So kind of a, the landscape is you see that picture too, right? >>For sure. I mean, we, we have presence in all of those, right? Like data engineers are very important. Data scientists. Those are core users of DataRobot like, how can you develop thousands and hundreds of thousands of models without having to hand code? If you have to hand code, it takes months and years to solve one problem for one customer in one location. I mean, see how fast the microeconomic conditions are moving. And data engineers are very important because at the end of the day, yes, you do. You create the model, but you need to operationalize that model. You need to monitor that model for data drift. You need to monitor how the model is performing and you need to productionize the insights that you gain. And for that engineering effort is very important behind the scenes. Yeah. And the users at the end of the day, they are the ones who consume the predictions. >>Yeah. I mean the volume and, and the scale and scope of the data requires a lot of automation as well. Correct. Cause you had that on top of it. You gotta have a platform that's gonna do the heavy lifting. >>Correct. Exactly. The platform is we call it as an augmented platform. It augments data scientists by eliminating the tedious work that they don't want to do in their everyday life, which some of which is like feature engineering, right? It's a very high value add work. However, it takes like multiple iterations to understand which features in your data actually impact the outcome. >>This is where the SAS platform is a service is evolved and we call that super cloud, right. This new model where people can scale it out and up. So horizontally, scalable cloud, but vertically integrated into the applications. It's an integrator dilemma. Not so much correct innovators dilemma, as we say in the queue. Yeah. So I have to ask you, I'm a, I'm a buyer I'm gonna come to the marketplace. I want DataRobot why should they buy DataRobot what's in it for them? What's the key features of DataRobot for a company to hit the subscribe, buy button. >>Absolutely. Do you want to scale your data science to multiple projects? Do you want to be ahead of your competition? Do you want to make AI real? That is our pitch. We are not about doing data science for the sake of data science. We are about generating business value out of data science. And we have done it for hundreds of customers in multiple different verticals across the world, whether it is investment banks or regional banks or insurance companies or healthcare companies, we have provided real value out of data for them. And we have the knowhow in how to solve, whether it is your supply chain, forecasting, problem, demand, forecasting problem, whether it is your foreign exchange training problem, how to solve all these use cases with AI, with DataRobot. So if you want to be in the business of using your data and being ahead of your competitors, DataRobot is your tool log choice. >>Sure. Great to have you on the cube as a strategy officer, you gotta look at the chess board, right. And we're kind of in the mid game, I call it the cloud opening game was, you know, happened. Now we're in the mid game of cloud computing where you're seeing a lot of refactoring of opportunities where technologies and data is the key to success, being things secure and operationally, scalable, etcetera, et cetera. What's the key right now for the ecosystem as a strategy, look at the chessboard for data robots. Obviously marketplace is important strategy. Yeah. And bet for, for DataRobot. What else do you see for your company to be successful? And you could share with, with customers watching. >>Yeah. For us, we are in the intelligence layer, the data, the layer below us is the data layer. The layer about us is the applications and the engagement layer. DataRobot I mean, interoperability and ecosystem is important for every company, but for DataRobot it's extra important because we are in that middle of middle layer of intelligence. And we, we have to integrate with all different data warehouses out there enable our customers to pull the data out in a very, very faster way and then showcase all the predictions into, into their tool of choice. And from a chessboard perspective, I like your phrase of we are in the mid cycle of the cloud revolution. Yeah. And every cloud player has a data science platform, whether it is simple one or more complex one, or whether it has been around for quite some time or it's been latent features. And it is important for us that we have complimentary value proposition with all of them, because at the end of the day, we want to maximize our customer's choice. And DataRobot wants to be a neutral platform in supporting all the different vendors out there from a complementary standpoint, because you don't want to have a vendor lock in for your customers. So you create models in SageMaker. For example, you monitor those in DataRobot or you create models in DataRobot and monitor those in AWS so that you have to provide like a very flexible >>That's a solution architecture. >>Correct? Exactly. You have to provide a very flexible tech stack for your customers. >>Yeah. That's the choice. That's the choice. It's all good. Thank you for coming on the cube, sharing the data robot. So I really appreciate it. Thank >>You for coming. Thank you very much for the opportunity. >>Okay. Breaking it all down with the partners here, the marketplace, it's the future, obviously where people are gonna buy the buyers and sellers coming together, the partner network and marketplace, the big news here at 80 seller conference. I'm John ferry with the cube will be right back with more coverage after this short break.