>>Hello, and welcome back to the cubes. 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 20, 21 and kick off next year, we're going to dig into the intersection of machine learning and business intelligence, business intelligence, Innomar, and Corian is here as the senior vice president of technology alliances at click and costy Wasilla caucus is the head of product growth for low code, no code machine learning at AWS gentlemen. Welcome to the >>Cube. Thanks for having us. >>I think the first time you were on at reinvent Sev definitely early last decade of >>My life. I >>Had black hair and it was maybe a 2013, I want to say. So it's been quite a run >>And it's definitely been a, been a privilege. I had a, had a chance to attend pretty much all all reinvents from the first one, eh, with a much fewer people and say this growth year over year. And what's amazing about it. This is beyond the scale, how much you grow, the number of people. It's just the face of innovation. Keeps, keeps accelerating as an it's, just this phenomenal. >>We're lucky that we chose data as sort of a, our business passion. But, um, so 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, that they want, that they need? What's the opportunity that you're hearing now? >>So, uh, I think first of all, this is a fascinating, fascinating topic because we're talking kind of about the intersection of, uh, what everybody wants to look to do as the next frontier of, uh, of data with predictive data, because descriptive analytics have been around for a long time, but what coconut use predictive analytics, prescriptive analytics to enrich what we've had with descriptive analytics to be the end of the day, improve the business and what, what I love talking to people around here and just listening to customers, express the, you know, 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 are in Applegate and they want to use it in more ways. And that's what exciting to discuss those new ways. They want to bring it together >>Because anything you'd add to that from AWS perspective, >>I'll tell you what we don't hear from our customers and that we've stopped hearing what is AI and machine learning. And on the contrary we are 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 DDB DBT three, how can we help customers build, train and tune customer specific models for all their, to be able 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 my, when we first met success was measured in, yeah, I got the Hadoop cluster, the work technically, but to your point, they 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? >>Um, so active intelligence is something that you have here click started 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're going to think about active, not being passive. So, uh, traditional BI, uh, kind of relied on pre-configured 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 needs to happen. So in order to accommodate that again, this is really the difference between active and passive. Is it active intelligence is all about innovations to bring real-time data. So it's all 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. And I lead this data pipeline that makes it real-time data available in the forum and the structure that allows me to make a decision or to take action. And finally, it's really to be designed to drive action, right? So whether it's a manual action or whether it's even completely automated, but it's intelligent, it's informed. So that's, 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, it's real time is before you lose the customer before you lose the patient before the machine explodes. Right? So your point about predictive. Yeah. Now you guys made an announcement yesterday, uh, ADA, which stands for AI, for data analytics, what what's that all about? Well, >>Ate them tries to aims to address the very point I mentioned before our customers that are asking us, how can we give access to our business teams? There are a lot more business needs to machine learning. An 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. And for example, we've built integrations with clique Tableau, snowflake, Workato Pegasystems. And through those, those usually take two flavors. Either we help our partners build a mail and embedded into their applications and in a way, make them more intelligent as Mr. Wright mentioned, or we help our partners expose machine learning capability from AWS, right within the UI. >>So for example, yes, they will launch snowflake integration with SageMaker. Now snowflake user can use the same user experience in three-year the same use, the SQL query that they love and trigger an auto ML process insights maker, 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 that was about it. No, no, no other, so I think, um, what a setups, yeah. You mentioned customers want to create more machine learning. They, they want to build faster, new, more machine learning capabilities, which is whereby the way the, the, uh, no code local, you know, comes into mind. How can you use the autopilot, which is a SageMaker product for enabling faster creation of models. So I 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 they're you there's users are. >>Eh, so, you know, BI environments or experiences like as we started to think about him. So I says, well, be provided with Gleevec. And again, with our active intelligence platform is all about weaving the data into the applications, into the environments, either the analytic workflows that, uh, that users have. So we introduced and are super excited. Uh, we've announced, uh, two integrations. So very robust integration between cloud and Amazon SageMaker. And that includes both our new analytic connector for, uh, uh, Amazon SageMaker and our integration with Amazon SageMaker autopilot. So with integration with SageMaker, we now have ClixSense interacting directly and seamlessly with any model deployed within SageMaker. So again, very much like cost dimension in your experience as a user seamlessly, you now also have predictive predictive data. So as you working in application, as you're interacting with your data, dynamically data is interchanged between click and SageMaker in reaching your decision, making your actions with predictive datasets. And that's, what's so cool about it. So again, the clinic 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. So >>Translate what that means for customers is that everything happens faster. Is it unlocked new capabilities? Can we unpack >>A little bit? Absolutely. So aware in a way, bridging the chasm 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 Myra 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 chasm between the two worlds of data science and business users is completely bruised. And the second integration we built with autopilot, she helps data engineers use completely their own machine learning technology powered by AWS pacemaker. So a data engineers creating different pipelines and through those pipelines, they can now with a building block, add auto ML 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 skillset gap for the data engineers to tap into machine learning. You mentioned >>Monitor monetization before. So this to me is key because who's going to do with doing the monetization. It's the business lines that are going to do that, not the data scientists data 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 who sits in your title too, so that all plays in doesn't it? >>Yeah, you guys, and we're heavily invested into that whole space. So for example, today we just launched SageMaker canvas. That is a low-code no-code capability for analysts and business users, but we realized 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 those, our technology to wherever our customers want to be the one to be in clique. So be it, let them use the machine learning technology that we are innovating on exactly where they wanted to be. >>Can you give us some customer examples and use cases, maybe make it real for us, >>Uh, for sure. And I, and I think as you, as you think about these use cases, one of the other things I want to do 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 a conversation analytics, which is part of what we offer in the cloud. So you can actually, he can arrive 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 the data. The data will come to you when it, when it needs you including base on, on predictive data. So there's a lot of, uh, a lot of options about how you're going to do it. >>Then give me, let me give you, let me give you an example. I'll let me try and maybe pick one that is intuitive. I think for, for many, for many people sales, right? So you have sales, you have a lot of orders. You want to try to close to closing a quarter, you have a forecast, the deals you expect to close. Uh, and then you can use machine learning for example, to forecast or to try to project which, 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 folder, the volume, the amounts, a lot of other parameters, right. Then predict if you're going to lose a deal. So now, if there's a deal that I, that my sales person is telling me, he's going to win, but the mall is telling me you may lose, well, I probably want to double click on that one. >>Right? So I cannot bring that information right in again, in the moment it is 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, you know, from the machine learning, you 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 it can also integrate it, uh, again, you know, click cloud. We also also click on application automation, which is again, also kind of a low-code no-code environment to orchestrate processes. I can also take that automatically, also update back Salesforce or the CRM. Okay. So that the metadata management system gets updated. So you got an example, exactly. The example of active intelligence. It allows me to take informed action in the now in the moment about making the best example. >>And if Salesforce salesperson, maybe I prioritize and the machines helping me direct my resources. Is this available today? Is it in general availability >>Available right now? Right? Anyone can go start it right now and click LA >>Congratulations. Um, last question. So what's the future hold for this partnership? Where are you guys headed? Give us a little >>Direction. First of all, would love to scale those integrations. So if you're a customer of Blake, please go ahead and test them and do sir, 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 are aspiring to build that partnership and get a lot more tight aligned with, uh, with Glick. And, uh, thank you costly. And, uh, we, we see tremendous additional opportunities. I think Amazon tells it where I would say is, well, we're in day one. That that's how we kind of feel about it. There's only 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 to journey to expand Lee cloud, adding more services. It's actually a platform where we're bringing both data services. They integration data management, everything related to the analytics 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 we're really excited to look at that and just like close to, you mentioned with canvas, you know, Amazon keeps coming up with new new services and new capabilities. So there's gonna be a lot of more opportunity. Eh, we're gonna keep, uh, again, within spirit of our partnership where we want to, you know, jump first innovate quickly and, uh, you know, create is integration, adds value to customer >>Often the flywheel that's. I love it. Great. Great to have you guys awesome to reconnect. All right. Appreciate it. Thank you for watching. This is the queue and we're covering AWS reinvent 2021. We're the leader in high tech coverage, right back