 Hello, and welcome back to theCUBE's continuous coverage of AWS 2021. We're here live, real people, and we're pleased to bring you this hybrid event, the most important hybrid event of the year to wrap up really 2021 and kick off next year. We're going to dig into the intersection of machine learning and business intelligence. Business intelligence, Inamar and Corian is here as the senior vice president of technology alliances at CLIC and Costi Vasilekakis is the head of product growth for low-code, no-code machine learning at AWS gentlemen. Welcome to theCUBE. Thanks for having us. Inamar, I think the first time you were on a re-invent, it's definitely early last decade. I had black hair, maybe 2013, I want to say, so it's a big point of run. It has, and it's definitely been a privilege. I had a chance to attend pretty much all re-invents from the first one with much fewer people and see this grow a few year over year. And what's just amazing about it is beyond the scale how much it grows in a number of people. It's just the face of innovation keeps accelerating and it's just phenomenal. We're lucky that we chose data as sort of our business passion, but speaking of data, what are you hearing from customers about what they want to do with their data and bringing together business intelligence and machine learning, it's being injected in, but what are they telling you that they want, that they need? What's the opportunity that you're hearing? So I think, first of all, this is a fascinating topic because we're talking kind of about the intersection of what everybody wants to look to do is a next frontier of data with predictive data because descriptive analytics have been around for a long time, but how can we use predictive analytics, prescriptive analytics to enrich what we've had with descriptive analytics but in the end of the day, improve the business and what I love talking to people around here and just listening to customers express their needs is how can they get more value out of data? So they have the data, they don't use a lot of the data or enough of it and they want to use it in more ways and that's what's exciting to discuss those new ways they want to bring it together. Can I say anything you'd add to that from AWS's perspective? I'll tell you what we don't hear from our customers and we've stopped hearing what is AI and machine learning and on the contrary we're hearing how can we make the teams that already AI and ML a lot more productive and make a lot more of it? For example, how can they iterate a lot faster across the ML workflow? How they can train and build really large state-of-the-art natural language processing models like GDPT-3, how can we help customers build, train and tune customer-specific models for older customers to bring in hyper-personalization to their products? And the other thing we're hearing is how can we help the teams that are not tapping into AI and ML get the most power of it? In a way how could you actually potentially either democratize the building and development of machine learning models? Or how can you in another way expose machine learning into applications that analytics users are already using? Yeah, so in a way when we first met success was measured and I got the Hadoop cluster to work technically, right, but to your point customers want to get more value out of that data now. And so they want to operationalize machine intelligence. Is that what active intelligence is? So active intelligence is something that you have here click start to talk about but we believe it really represents what customers are trying to achieve. And the reason we use the word active intelligence is if you kind of think about active not being passive. So traditional BI kind of relied on preconfigured historical data sets which were great for what they did but today they're kind of out of gas in terms of supporting real-time decisioning and action. So what active intelligence is all about is really enabling customers to make it take informed informed action not just informed decision informed action in the moment. So when that action needs to happen. So in order to accommodate that, again the difference between active and passive is that active intelligence is all about innovations that bring real-time data. So it's not just historical data, I need real-time data that's relevant to what's happening now. I need a way to get an intelligent data pipeline an analytics data pipeline that makes that real-time data available in the form, in the structure that allows me to make a decision or to take action. And finally it's really to be designed to drive action. So whether it's a manual action or whether it's even completely automated but it's intelligence is informed. So that's what active intelligence is all about that by the way predictive data fits really well into that entire paradigm. Right I mean we've been talking for years about real-time and it's like okay what is real-time? Well real-time is before you lose the customer, before you lose the patient, before the machine explodes, right? To your point about predictive. Now you guys made an announcement yesterday, ADA which stands for AI for data analytics. What's that all about? Well ADA aims to address the very point I mentioned before, our customers that are asking us how can we give access to our business themes to a lot more business themes to machine learning? And AI for data analytics is a set of partner solutions that are ML powered and they're focusing across the spectrum of analytics from data warehousing, business intelligence, business process automation and other business application. And the idea is to help our partners bring to our customers a lot of those more ways. For example, we've built integrations with click, tableau, snowflake, workato, Pegasystems and through those, those usually take two flavors. Either we help our customer partners build ML and embed it into their applications and in a way make them more intelligent as it Amar mentioned. Or we help our partners expose machine learning capability from AWS right within the UI. So for example yesterday we launched a snowflake integration with SageMaker. Now snowflake user can use the same user experience and trigger the same user SQL query that they love and trigger an auto ML process in SageMaker right from the same UI and get ML into the same UI. And I'm quite excited to also discuss today about the integration we announced today with click. SageMaker integration or? Tell us about it. No other. Yeah. So I think- What a setup. Yeah exactly. As Costi mentioned, customers want to create more machine learning. They want to build faster, more machine learning capabilities, which is where by the way the no code, low code comes into mind. How can you use Autopilot, which is a SageMaker product for enabling faster creation of models. So we want to create models faster. They also want to be able to use models in a sense monetize them, turn them into value to make them available to more users, where their users are. So BI environments or experiences that as we start to think about them, such as what we provide with click, again with our active intelligence platform, is all about weaving the data into the applications, into the environments, into the analytic workflows that users have. So we introduced and we're super excited. We've announced two integrations, so very robust integration, between ClickCloud and Amazon SageMaker. That includes both our new analytic connector for Amazon SageMaker, and our integration with Amazon SageMaker Autopilot. So with integration with SageMaker, we now have click sense, interacting directly and seamlessly with any model deployed within SageMaker. So again, very much like Costi mentioned, in your experience as a user, seamlessly you now also have predictive data. So as you work in an application, as you're interacting with your data, dynamically data is interchanged between Click and SageMaker, and reaching your decision-making, your actions with predictive data sets. And that's what's so cool about it. So again, the click environment, we bring real-time data in, prepare it for analytics, and then feed that real-time data to SageMaker to get the real-time prediction back in the same experience for the user. So we're really, really excited about that. Translate what that means for customers, it's just that everything happens faster, does it unlock new capabilities? Can we unpack that a little bit? Absolutely. So we're in a way bridging the cousin between the data science world and the business teams. So the data science teams are building machine learning models to make predictions. And now with the first integration that Mara mentioned, we actually expose those machine learning models in an application that the business team uses, Click. And with the same dashboards that they are very familiar with, can now trigger those machine learning models and get real-time predictions in the dashboards themselves powered by machine learning. So in a way, this cousin between the two worlds of data science and business users is completely bridged. And the second integration we built with autopilot helps data engineers use completely their own machine learning technology powered by AWS SageMajor. So data engineers creating different pipelines and through those pipelines, they can now with a building block add AutoML capabilities in that pipeline without them really knowing machine learning. So we bridge the gap of the business teams getting access to the data science teams and also bringing the skills at gap for the data engineers to tap into machine learning. You mentioned monetization before. So this to me is key because who's going to do with you in the monetization? It's the business lines that are going to do that. Not the data scientists, they're going to enable that. But ultimately it's those data consumers that are building those, I call them data products that they can ultimately monetize. And that's, I'm interested in low-code, no-code because it's in your title too. So that all plays in, doesn't it? It does, it does. And we're heavily invested into the whole space. So for example, today, we just launched SageMajor Canvas that is a low-code capability for analysts and business users. But we realize we don't need to only innovate on the technology side. We need to also innovate on the partnerships that we built. And those integrations help expose our technology to wherever our customers want to be, they want to be in click. So be it, let them use the machine learning technology that we are innovating on exactly where they want it to be. In the market, you give us some customer examples, use cases, maybe make it real for us. For sure, and I think as you think about these use cases, one of the other things I wanted to kind of envision is the fact that all this predictive data and all this integration that we're talking about is not, can actually express itself in a lot of different experiences for the user. It can be a dashboard. It can also be conversational analytics which is part of what we offer in ClickCloud. So you can actually, you know, you can write and interact with the data. You don't have to actually look at it. It can be alerts that actually look automatically and inform you that you need to take action. So you don't actually look at it. The data will come to you when it needs you including based on predictive data. So there's a lot of options about how you're going to do it. And let me give you, let me give you an example. Let me try and maybe pick one that is intuitive, I think for many people, sales, right? So we have sales, you have a lot of orders. You want to try to close in a quarter. You have a forecast, the deals you expect to close. And then you can use machine learning, for example, to forecast or to try to project which deals you're going to lose. So now, again, that can look at a lot of different aspects of the deal, the timing, the volume, the amount, a lot of other parameters, right? And predict if you're going to lose a deal. So now if there's a deal that my sales person is telling me he's going to win, but the model is telling me he may lose, well, I probably want to double click on the debt one, right? So I can now bring that information in the moment either to the seller or to the management so they can identify it and take action. Now, not only can I bring it to them, but I can also, from the machine learning know what is the likely reason that they lose? And if I know the likely reason, it also become prescriptive. I now can know what to do to try and fix it, right? So I can either do it again manually or I can also integrate it, again, click cloud, we also click application automation, which is again also kind of a low-code, no-code environment to orchestrate processes. I can now take that automatically also update the Salesforce or the CRM, okay? So the order management system gets updated. So again, it's an example, exactly an example of active intelligence. It allows me to take informed action in the now, in the moment about making the better business. It's a great example and if sales person, maybe I prioritize and the machine's helping me, you know, direct my resources. Is this available today? Is it in general availability? Available right now. Today anyone can go start it right now and click cloud. Great, awesome, congratulations. Last question, so what's the future hold for this partnership? Where are you guys headed? Give us a little direction. First of all, we'd love to scale those integrations. So if you're a customer of Click, please go ahead and test them and do serve the feedback. And second for us, we really want to learn from our customers and improve those integrations we bring to them. We really want to hear what technologies they want to expose to a lot more users and we're aspiring to build that partnership and get a lot more tight aligned with Click. Do that. And thank you, Costi. And we see tremendous additional opportunities. I think Amazon tells it, we always say we're in day one, that that's how we kind of feel about it. There's already so much we put into it, but the market is so dynamic, there's so many new needs that are coming up. So we kind of think about it that way. So first of all, we want a journey to expand Click Cloud adding more services. It's actually a platform where we bring both data services, data integration, data management, everything related to the analytic pipeline and of course the analytic services. So it all comes together in one environment that makes it more agile, faster to build these new, modern, active intelligence type experiences. So as we do that, we're going to be adding more services, creating more opportunities to integrate with more services from the AWS side. So I'm really excited to look at that. And just like Costi mentioned with Canvas, Amazon keeps coming up with new services and new capabilities. So there's going to be a lot of more opportunity. We're going to keep, again, within Spirited for a partnership where we want to jump first, innovate quickly and create this integration that's value to customers. Up on the flywheel, that's, I love it. Great to have you guys, awesome to reconnect. All right, appreciate it. See you again. Thank you for watching. This is theCUBE and we're covering AWS re-invent 2021. We're the leader in high tech coverage. Right back.