>>from around the globe. It's the cue with digital coverage of out systems. Next step 2020. Brought to you by Out systems. Welcome back. I'm stupid, man. This is the Cube at Out systems. Next step course. When we gather at the events, one of the big things to talk about is the community and out system show is no exception. Lots of developers, lots of community engagement. A Z they're building and sharing what they have. So to dig into that topic, happen it. Welcome to the program. 10 Lopez. She is the senior director of Community and Advocacy without systems. Again. Thank you so much for joining us. >>Hi. Thanks so much for having me. >>Well, Jen, you know they're there. So much discussion in the industry right now is like OK, what does that the current moment in time with the global pandemic mean for events? What does it mean for communities? The term I've heard used so much is, you know, how do we bring ourselves together even while we're apart? But if you could, you know, give us You know what does the community on help systems look like? You know, you've had this event before. If this was 2019 you know, what did the community activity in the community engagement looked like? >>Yeah, we're definitely in a different world right now, right? So in 2019 gathering the community together, you know, whether it was at at Max step or another in person events that we often had. Um you know that that is such a huge part of building community is getting people together and being able to have those conversations. And, um, sometimes it's just ah, whether it's meeting at, you know, you're getting some coffee and you meet someone. All of those in person things, um, are hard to do online. But we're really working hard this year at, you know, finding those ways to connect in a bunch of different ways with the community. Um, we have our regular technical talks and that sort of thing that we're doing. But we also have a chat where you can come in tow and chat with other community members. We're gonna have ah, 24. You know, we have this 24 hour zoom going on. So you could you know, we're fine trying to find his many ways as possible. Teoh sort of at least get those conversations and have the ability for the community to connect with each other. >>I'm wondering if you can, you know, people look at communities and especially in the developer community There's so many different pieces of that. Uh, when I talkto Gonzalo he was talking about how do we enable the next? You know, 10 million developers? When I talk to help communities, it looks like the app Dev is obviously a big piece of ah of what you're addressing. But you characterize if you could And if you have any staff but loved, understand, you know, the community, the growth of community. You know where the engagement activity is. >>Yeah, thanks. So the community growth of the out systems community has been phenomenal. Um, last year we saw are just for this year with on 90% growth since last year. Uh, we have 22,000 developers on a monthly basis who are actively doing things in the community. Um, that's anywhere from between building APS and asking questions in the forum and, um, using downloading forge components which are reusable APS attending user groups. There's all these things right. We have this activity level that we've seen that has just been through the roof. And, um, Cove in for the community has actually been, You know, we've seen a huge birth specifically march in April, we saw a great increase in new members coming on. And then what happened is our other members jumped in answering way more questions than we've ever had in the forums offering to help in different ways so that between the increase in gross, the growth and increasing activity, uh, the community itself has jumped in to really help out other people. >>Well, if you look at the development community and the tools they use and how they engage there, really, the work from home, you know, movement probably hit them a little bit less that than the average knowledge worker because they're used to being online there. Used to engaging in these environments. Often it is a distributed community, so it sounds like it makes sense. What what else? From a covert standpoint, You know, I've talked to some of the out systems customers and the ability it baked into the five former, something that they're take advantage of. Do you have any interesting stories around. You know how the community is rallying, you know, specifically with Kobe going on? >>Yeah. So, actually, that that Brown was a huge thing for us. We had at both internal and external. We were getting a lot of folks coming to us and saying, you know, everybody wanted to help, right? Especially in the beginning off when it kind of hit globally, everybody wanted to help. So what we did is we launched a program that we called the community the coveted 19 community response. Orban, um and we weren't quite sure exactly how people might react. But what ended up happening is we had thousands of people give ideas. And with those ideas, we had teams of people who were working on building these acts and actually launching the abs to help different communities all around the world with various issues. Whether it waas, you know, um, on an uber like up that was created to help people in a certain community, you know, find somebody who could go to the store for them. Um, there were, you know, these different acts were being created by the community. The ideas were coming from the community and people just really rallied around it because everybody wanted to help and they wanted to participate and be a part of something. And they were able to get these APS out in, you know, record time. Um, I would see other folks. Everyone was was trying to rely on technology at the time. And I would see other folks saying, Oh, you know, we had a team of five people spent 22 weeks building out. You know, our first M v p of this happened at out systems we were seeing people in, you know, two people in one week having, like, awfully blown, flushed out, being created. Um, So we were able to you not just help with the technology simply but help really quickly when it was needed right away. >>You know what? One of the themes I've been hearing a lot at the show is How do we close that? That Helen skill gap? Um, I have to imagine with your community engagements, the advocacy. You've got some visibility in tow. You know what things is out system engaged with when it comes toe educating the next generation, helping people take advantage of some of the new technologies adoption of the new AI features. It gives a little viewpoint as those changing dynamics in the community and specifically for developer. >>Yeah, I think it's it's really interesting. So, um, we have a number of programs with our between our education program and, um, low code schools and various programs where we're getting not just new developers coming in and burning out systems right away. But but actually getting developers who were coming from other programming languages who were ready to learn something new, who are like, Hey, I'm hearing a lot about, you know, uh, these these different ways to be innovative and I, you know, build an act quickly and it's still secure and stable and robust. And all of this. And so we have a lot of people on, you know, coming in in different ways. We're also really excited about a new partnership that we've just launched with women who code. We're you know, we're working really hard at going beyond just sort of those regular ways of people coming in. We want to help bring people from, you know normally, who may be underrepresented intact at the moment because we want to help bring that new generation in and that generations coming from all walks of life. And, um, you know, coming up with working on lots of different ways, Teoh, educate and and bring them in and keep them intact. >>Yeah. You know, Jen, such an important topic. I'm so glad that you brought it up, you know, diversity. Um, you know what? One of the things when when I think about we're lowering the bar. Ah, and you know what necessary skills? You have to get started to be a coder so often it's I have to have this degree. I need to understand these languages. So, you know, do do you feel this general movement is making it more accessible? Are we in a You know, what are we doing? What we doom or to be able to reach out, find some new pools of talent that can help us close this gap. And you know, then, as you said, keep them in tech. >>Yeah, and I think that will be key, is keeping them in tech. But, um, there are right now it's a It's a strange thing to say this is an opportunity, right? But with lots of people. Um, and specifically here in the US, where I am, Obviously we have a lot of folks who have lost jobs, right? People are looking for ways to get into something new. What's great about being able to learn? Ah, out systems is that that you're going to have a a different kind of job, right? You're going to have one of those jobs with an enterprise organization, um, or or or, you know, one of our partnerships. And it's going to level up your career and it totally differently. And there are, um, uh, lots of organizations right now who are also looking to find those those ways online there, like we have all these members in our community. But we're trying to get trained and Intertek and in different ways, and they're reaching out to us as well and saying, Hey, we're hearing a lot about you know, all of these innovative things out systems is doing. How do we work together? And so it's been really exciting to see that it's not just us going out and reaching out its people saying, Oh, I see these really cool things that you're doing and you know, we want to help get our members, um, learning and into this as well. >>All right, let's look a little forward. If we could, Jen, you know, tell us. You know what? What do you What do you see in the future? You know what feedback you're getting from community? What things should we be looking at? Going forward, >>Going forward. I think that, um, development is really going to be focused on on being able to be creative and innovative and finding new ways to do things. We don't have to do things the same old way anymore, right? We can, uh, build a robust application, uh, quickly and likely saw with co bid. Um, you know, we had big issues, and people were able Teoh, uh, figure out a way Thio Thio use technology to actually help fix these issues or solve a problem really quickly. And I see that very much that people it lights something in people's minds of Oh, being a developer doesn't have to just mean sitting and coding all day. It means, you know, doing really but robust things that I can do to help people and use technology in a totally different, innovative way >>Wonderful. Don't want to give you the final word when we talk about out systems bringing the community together. What do you want? People toe understand and connect with on this community? >>Thedc, um, unity itself is very generous and giving and one thing, but I have really, really loved about being a part of out systems is the community itself because they are working really hard to help bring new developers in help train them, give them mentor ship. So there's a There's a big feeling of, you know, it's not just every person out for themselves. They really want to help lift each other up. I think it's really important for, you know, feeding that technology, that new generation and that innovation that that is coming from it. All right, >>well, 10 Lopez thank you. So so much for helping us dig inside the community. Definitely looking at the engagement opportunities this week. And ah, thank you for all of the information that you share. >>Thanks, Dio appreciate it. >>Stay tuned for more cover jumps to minimum. And thank you for watching the Cube

>>from around the globe. It's the cue with digital coverage of out systems. Next Step 2020 Brought to you by out systems. I'm stupid, man. And welcome back to the cubes Coverage of out systems Next step course. One of the items that we've been talking a lot in the industry is about how artificial intelligence, machine learning or helping people is. We go beyond what really human scale can do and we need to be ableto do things more machine scale. Help us really dig into this topic. Happy to welcome to the program First time guest Antonio Alegria. He is the head of artificial intelligence at out systems. Tonio, thanks so much for joining us. >>Thank you. So I'm really happy to be here and and really talk a little bit about what? We're doing it out systems to help our customers and our leverage eai to get to those goals. >>Wonderful. So I I saw ahead of the event a short video that you did and talked about extreme agility with no limits. So, you know, before we drink, dig into the product itself. Maybe if you could just how should we be thinking about a I you know, there's broad spectrum. Is that machine learning that there's various components in there? Listen to the big analyst firms. You know, the journey. It's big steps and something that that is pretty broad. So when we're talking about A I, you know, what does that mean to you? What does that mean to your customers? >>Eso So AI out systems really speaks to division and the core strategy we have for our product, which is, you know, if you saw the keynote, no, we talk about no, really enabling every company, even those that you know, that existed for decades, perhaps have a lot of legacy to become. You know, leading elite cloud software development companies and really can develop digital solutions at scale really easily. But one thing we see and then this is a big statistic. One of the things that limits limits CEOs the most nowadays is really the lack of town lack of engineering, a softer engineering, you know, ability and people that that that could do that. And there's a statistic that was reported by The Wall Street Journal. I saw it recently, perhaps last year, that said that according to federal jobs dating the U. S. By the end of 2. 2020 there would be about a million unfilled I E. T s after development jobs available. Right? So there's this big problem All of these companies really need to scale, really need to invest in digital systems and so horribly fed out systems. We've already been abstracting and we've been focusing automating as much as possible the softer development tools and applications that use. We've already seen amazing stories of people coming from different backgrounds really starting to develop, really leading edge applications. And we want to take this to the next level. And we believe that artificial intelligence with machine learning but also with other AI technologies that were also taking advantage of can really help us get to a next stage of productivity. So from 10 x productivity to 100 x productivity and we believe AI plays a rolling three ways. We believe II by learning from all of this data that we not collect in terms of, you know, projects are being developed. We're essentially trying to embed a tech lead, so to speak, inside a product and attack Lee that can help developers by guiding them got in the most junior ones by automating some of the boring, repetitive tasks were by validating their work. Making sure that they're using the best practice is making sure that it helps them as they scale to re factor on their code to automatically designed architectures. Things like that >>Wonderful. Antonio Gonzalo stated it quite clearly in the interview that I had with him. It's really about enabling that next you know, 10 million developers. We know that there is that skill gap, as you said, and you know everybody right now how can I do more? How can I react faster? Eso that's where you know, the machine learning artificial intelligence should be able to help. So bring us inside. I know the platform itself has had, you know, guidance and and the whole movement. You know, what we used to call low code was about simplifying things and allowing people to, you know, build faster. So bring us inside the product. You know what? The enhancements? One of the new pieces. Some of the key key items, >>Yes, So 11 interesting thing. And I think one thing that I think out system is really proud of being able to achieve is if you look at how out system has been using a AI within the platform. We started with introducing AI assistance within the Our Software Development Environment Service studio. Right? And so this capability, we've been generating it a lot. We've been evolving it, and now it's really able to accelerate significantly and guide novices, but also help pros dealing through software development process and coding by essentially trying to infer understanding their context and trying to infer their intent and then automating the steps afterwards. And we do this by suggesting you the most likely let's say function or or code p sexual one you need. But then, at the next step, which we're introducing this year, even better, which is we're trying to auto fill most of them. Let's see the variables and all of that in the data flow that you need to collect. And so you get a very delightful frictionless experience as you are coating, so you're closer to the business value even more than before. Now this is the This was just the first step, what you're seeing now and what we're announcing, and we're showing up at this next step that we show that the keynote is that we're trying to fuse starting to fuse AI across the out systems products and across this after development life cycle. So he took this core technology that we used to guide developers and assistant automate their work. Um, and we use the same capability to help developers. Tech leads an architect's to analyze the code, learning from the bad patterns that exist, learning from and receiving runtime information about crashes and performance and inside the product recall architecture, dashboard were really able to give recommendations to these architects and tech leads. Where should they evolve and improve their code? And we're using AI refusing AI in this product into very specific ways. Now that we're releasing today, which is one is to automatically collect and design and defined the architecture. So we call this automated architecture discovery. So if you have a very large factory, you can imagine, you know have lots of different modules, lots of different applications, and if you need to go and manually have to label everything so this is ah, front, and this is the back end. That would take a lot of time. So we use machine learning, learning from what architects have already done in the past, classifying their architecture. And we can map out your architecture completely automatically, which is really powerful. Then we also use our AI engine to analyze your factory and weaken detect the best opportunities for re factoring. Sorry. Factoring is one of the top problems in the top smells and technical depth problems that large factories have. Right, So we can completely identify and pinpoint. What are these opportunities for re factory and we guide you through it, which held you okay, all of these hundreds of functions and logic patterns that we see in your code Could you re factor this into a single function and you can save a lots and lots of code because, as you know, the best code the fastest coast easiest to maintain is the Cody. Don't ride. You don't have. So we're trying to really eliminate Kurt from these factories with these kids ability. >>Well, it's fascinating. You're absolutely right. I'm curious. You know, I think back to some of the earliest interactions I had with things that give you guys spell checkers. Grammar check. How much does the AI that you work on. Does it learn what specific for my organization in my preferences? Is there any community learning over time? Because there are industry breast pack that best practices out there that are super valuable. But, you know, we saw in the SAS wave when I can customize things myself were learned over time. So how does that play into kind of today in the road map for a I that you're building >>that? That's a good question. So our AI let's say technology that we use it actually uses to two different big kinds of AI. So we use machine learning definitely to learn from the community. What are the best practices and what are the most common pattern that people use? So we use that to guide developers, but also to validate and analyze their code. But then we also use automated reasoning. So this is more logic based reasoning based AI and repair these two technologies to really create a system that is able to learn from data but also be able to reason at a higher order about what are good practices and kind of reach conclusions from there and learn new things from there now. We started by applying these technologies to more of the community data and kind of standard best practices. But our vision is to more and more start learning specifically and allowing tech leads an architect even in the future. To Taylor. These engines of AI, perhaps to suggest these are the best practices for my factory. These patterns perhaps, are good best practices in general. But in my factory, I do not want to use them because I have some specificities for compliance or something like that. And our vision is that architects and techniques can just provide just a few examples of what they like and what they don't like in the engine just automatically learns and gets tailor to their own environment. >>So important that you're able to, uh, you know, have the customers move things forward in the direction that makes sense on their end. I'm also curious. You talk about, um, you know what what partnerships out systems has out there, you know, being able to tie into things like what the public cloud is doing. Lots of industry collaboration. So how does health system fit into the kind of the broader ai ecosystem. >>Yes. So one thing I did not mention and to your point is eso were have kind of to, um Teoh Complementary visions and strategies for a I. So one of them is we really want to improve our own product, improve the automation in the product in the abstraction by using AI together with great user experience and the best programming language for software on automation. Right, So that's one. That's what we generally call AI assisted development. And if using AI across this software development life cycle, the other one is We also believe that you know, true elite cloud software companies that create frictionless experiences. One of the things that they used to really be super competitive and create this frictionless experiences is that they can themselves use AI and machine learning to to automate processes created really, really delightful experiences. So we're also investing and we've shown and we're launching, announcing that next step we just showed this at at the keynote one tool that we call the machine learning builder ml builder. So this essentially speaks to the fact that you know, a lot of companies do not have access to data science talent. They really struggle to adopt machine learning. Like just one out of 10 companies are able to go and put a I in production. So we're essentially abstracting also that were also increasing the productivity for you for customers to implement an AI and machine learning we use. We use partners behind the scenes and cloud providers for the core technology with automated machine learning and all of that. But we abstract all of the experience so developers can essentially just pick of the data they have already in the inside the all systems platform, and they want to just select. I want to trade this machine learning model to predict this field, just quickly click and it runs dozens of experiments, selects the best algorithms, transforms that the data for you without you needing to have a lot of data science experience. And then you can just drag and drop in the platform integrating your application. And you're good to go. >>Well, it sounds comes Ah, you know, phenomenal. You mentioned data scientists. We talked about that. The skill gap. Do you have any statistics? You know? Is this helping people you know? Higher, Faster. Lower the bar the entry for people to get on board, you know, increased productivity. What kind of hero numbers do your customers typically, you know, how do they measure success? >>Yes, So we know that in for machine learning adoption at cos we know that. Sorry, This is one of the top challenges that they have, right? So companies do not. It's not only that they do not have the expertise to implement machine learning at in their products in their applications. They don't even have a good understanding of what are the use cases in or out of the technology opportunities for them to apply. Right? So this has been listed by lots of different surveys that this is the top problem. These other 22 of the top problems that companies have to adopt a ice has access to skilled. They decided skill, understanding of the use case. And that's exactly what we're trying to kind of package up in a very easy to use product where you can see the use cases you have available, we just select your data, you just click train. You do not need to know that many greedy details and for us, a measure of success is that we've seen customers that are starting to experiment with ML Builder is that in just a day or a few days that can iterating over several machine learning models and put them in production. We have customers that have, you know, no machine learning models and production ever, and they just now have to, and they're starting to automate processes. They're starting to innovate with business. And that, for us, is we've seen it's kind of the measure of success for businesses initially, what they want to do is they want to do. POC is and they want to experiment and they want to get to production stopped. Getting to field for it and generate from >>a product standpoint, is the A. I just infused in or there's there additional licensing, how to customers, you know to take advantage of it. What's the impact on that from the relationship without systems? >>Yes. So for for for a I in machine learning that is fused into our product and for automation, validation and guidance, there's no extra charge is just part of the product. It's what we believe is kind of a core building block in a course service for everything we do in our product for machine learning services and components that customers can use to in their own applications. We allow you to integrate with cloud providers, and the building is is done separately on. That's something that that we're working towards and building great technical partnerships and exploring other avenues for deeper integration so that developers and customers do not really have to worry about those things. Well, >>it's it's It's such a great way to really democratize the use of this technology platform that they're used to. They start doing it. What's general feedback from your customers? Did they just like, Oh, it's there. I start playing with it. It's super easy. It makes it better there any concerns or push back. Have we gotten beyond that? What? What? What do you hear any any good customer examples you can share us toe general adoption? >>Yes. So, as I said, as we re reduce the friction for adopting these technologies, we've seen one thing that's very interesting. So we have a few customers that are present more in the logistics site of industry and vertical, and so they they have a more conservative management, like take time to adopt and more of a laggard in adopting these kinds of technologies, the businesses more skeptical. But I want to spend a lot of time playing around right and whence they saw. Once they saw what they could do with a platform, they quickly did a proof of concept. They show to the business and the business had lots of ideas. So they just started interacting a lot more with I t, which is something we see without systems platform not just for a I machine learning, but generally in the jib. Digital transformation is when the I teak and can start really being very agile in iterating and innovating, and they start collaborating a lot with the business. And so what we see is customers asking us for even more so customers want more use cases to be supported like this. Customers also the ones that are more mature than already, have their centers of excellence and they have their data scientists, for example. They want to understand how they can also bring in perhaps their use of very specialized tool talking in it. Integrate that into the platform so that you know, for certain use cases. Developer scan very quickly trained their own models. But so specialized data science teams can also bring in. And developers can integrate their models easily and put them into production, which is one of the big barriers we see in a lot of companies people working on yearlong projects. They develop the models that they struggle to get them to production. And so we really want to focus on the whole into in journey. Either you're building everything within the octopus platform or you're bringing it from a specialized pro tool. We want to make that whole journey frictionless in school. >>And Tony a final question I have for you. Of course, this space we're seeing maturing, you know, rapid Ah, new technologies out there gives a little look forward. What should we be expecting to see from out systems or things even a little broader? If you look at your your partner ecosystem over kind of the next 6, 12 18 months, >>Yes. So, um, what you're going to continues to see a trend, I think, from from the closer providers of democratization of the AI services. So this is during that just starting to advanced and accelerate as these providers started packaging. It's like what out systems also doing, starting to packaging Cem some specific, well defined use cases and then making the journey for training these models and deploying Super super simple. That's one thing that's continued to ramp up, and we're going to move from A I services more focused on cognitive, pre trained models, right, that which is kind of the status quo to custom ai models based on your data. That's kind of the train we're going to start seeing in that out systems also pushing forward generally from the AI and machine learning application and technology side of thing. I think one thing that we're leading leading on is that you know, machine learning and deep learning is definitely one of the big drivers for the innovation that we're seeing in a I. But you're start seeing more and more what is called hybrid I, which is taking machine learning and data based artificial intelligence with more logic based automated reasoning techniques, impairing these two to really create systems that are able to operate at a really higher level, higher cognitive level of which is what out systems investing internally in terms of research and development and with partnerships with institutions like Carnegie Mellon University and >>rely Antonio, who doesn't want, you know, a tech experts sitting next to them helping get rid of some of the repetitive, boring things or challenges. Thank you so much for sharing the update. Congratulations. Definitely Look forward to hearing war in the future. >>Thank you. Do have a good day >>Stay tuned for more from out systems. Next step is to minimum and thank you for watching.