 Welcome back to theCUBE's 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, John Furrier, host of theCUBE, bringing you all the action and what it all means. Our next guest is Shreya Kada Malakava, 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. A 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 year marks our 10th anniversary. And we provide a platform for data scientists and also citizen data scientists. So essentially want to 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 AI. How big is the company, roughly, employee count? What's the number? We are about 1,000 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 disclose a customer number, but needless to say we have hundreds of customers across the world. A lot of customers, yeah. You guys are well known in the industry, have been following some of the recent news lately as well. I'll see data is exploding. What in the marketplace are you guys offering? What's the pitch? Someone hits the marketplace, someone wants to buy, data robot, 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 data robot that you can download from your AWS marketplace. You can do a free trial and essentially get value from data in a matter of minutes and not months or quarters that's generally associated with AIML. And after that, if you want to purchase you, it's a private offer in the marketplace. So you need to call a data robot 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 going to only get stronger in terms of self-service, buying, bundling, having the console on AWS for low level services like infrastructure. But now you got other business applications that like analysts applies to. You're seeing that work. Now he said things like that in the keynote I want to get your reaction to like, we're going to make this more like a CICD pipeline. We're going to have more native services built into AWS. What that means to me is that sounds like, oh, if I have a solution like data robot that can be more native into AWS level services. How do you see that working out for you guys? Does that play well for your strategy and your customers? 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 data robot, win for customers and win for AWS, which is our partner. And it's a win for data robot because the amount of people, the number of eyeballs that look at AWS marketplace is significantly higher 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 data robot. And they already have pre-committed spend with AWS and they can use those spend dollars for data robot to procure data robot. So it eases their procurement lifecycle as well. It's a force multiplier on the revenue side. Correct. I mean, as well as on the business front. Cost of sales go down, the cost per order dollar. Correct. This is good, goodness. 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 are 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 a cloud commit that we can draw down for our customers. The procurement cycle shortens and also we have multiple constituencies within the customers working together in a very seamless fashion. How is the procurement going through AWS help your customers? What specific things are you seeing that are popping out as benefits to the customer? So from a procurement standpoint, 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 data robot. 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 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 will be a further force multiplier because that's the less fraction there is. I like how you say that's a machine. 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. IaaS, done, hardware, I'm oversimplifying but 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 prison hardware and infrastructure but the new cloud native is that, your provision turn on the app. So you're seeing a wave of building apps or composing Lego blocks if you will. So it seems like the customers are starting to assemble the solution almost like deploying a service and just pressing a button and it happens. This seems to be where the business apps are going. Yeah, absolutely. For us we are a data science platform and for us being very close to the data that the customers have is very important and if the customer's data is in redshift we are close to there. So being very close to the hyperscalary ecosystem in that entire CSED pipeline and also the data platform pipeline is very important. You know what's interesting is the data is such a big part of, I mean DevOps infrastructure as code has been the movement for a decade. So if you throw security in there, it's DevSecOps. That is the developer now. They're running essentially what used to be IT. Now the new ops is security and data. 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 when they're connected. So they're starting to see data availability become more pervasive. 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 what data robot 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 data robot, they use data robot, 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 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 ERP, into the apps layer because at the end of the day, business users, customers consume predictions through applications layer. You know, it's interesting how you mentioned that comment about trying not to offend data scientists. It's actually a rising tide. The tsunami of data is actually making that population bigger too, right? So you also have data engineering which has come out of the woodwork. We covered a lot on theCUBE, 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 alphas, the alpha geeks, hardcore data operating systems kind of education, data science, big pool growing, and then the users are the new data science practitioners. So kind of the landscape is, you see that picture too, right? For sure. I mean, we have presence in all of those, right? Like data engineers are very important. Data scientists, those are core users of data robot. 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. And the users, at the end of the day, they are the ones who consume the predictions. Yeah, I mean, the volume and the scale and scope of the data requires a lot of automation as well. Because you add that on top of it. You got to have a platform that's going to 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 at work. However, it takes multiple iterations to understand which features in your data actually impact the outcome. This is where the SaaS platform as a service has evolved when we call that super cloud. This new model where people can scale out and up. So horizontally scalable cloud, but vertically integrated into the applications. It's an integrator's dilemma, not so much an innovator's dilemma, as we say in the queue. So I have to ask you, I'm a buyer. I'm going to 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? 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 in Muslim banks or regional banks or insurance companies or healthcare companies. We have provided real value out of data for them. And we have the know-how in how to solve whether it is your supply chain forecasting problem, demand forecasting problem, whether it is your foreign exchange trading 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 of choice. Sheesh, it's great to have you on theCUBE as a strategy officer. You got to 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. We're seeing a lot of refactoring of opportunities where technologies and data is the key to success. Being things secure and operationally scalable, et cetera, et cetera. What's the key right now for the ecosystem? As a strategy, look at the chess board for DataRobots. Obviously marketplace is important strategy and bet for DataRobot. What else do you see for your company to be successful that you could share with customers watching? Yeah, for us, we are in the intelligence layer. The layer below us is the data layer. The layer above us is the applications and the engagement layer. DataRobot, I mean, interoperability in ecosystem is important for every company, but for DataRobot it's extra important because we are in that middle layer of intelligence. And 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 their tool of choice. And from a chess board perspective, I like your phrase of you're in the mid-cycle of the cloud revolution. And every cloud player has a data science platform, whether it is a 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 complementary 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 complementarity standpoint because you don't want to have a vendor locking 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 you have to provide like a very flexible- It's the 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. Correct. It's all good. Thank you for coming on theCUBE to share in the DataRobot. So I really appreciate it. Thank you for coming. Thank you very much for the opportunity to share. Okay, breaking it all down with the partners here. The Marketplace is the future obviously where people can buy the buyers and sellers coming together. The partner network and Marketplace, the big news here at AWS Seller Conference. I'm John Furrier with theCUBE. We'll be right back with more coverage after this short break.