 Welcome back everyone to theCUBE's live coverage here at SaaS Innovate 2024. I'm John Furrier, host of theCUBE, my co-founder and co-host Dave Vellante, extracting the signal from the noise, reporting on all the top stories. Go to SiliconANGLE.com, check out all the stories. Of course theCUBE.net for all the viewers, as we're in day two of two days of coverage. We've got Jared Peterson, senior VP of R&D, platform engineer at SaaS. He was on stage where he did the demo and the announcement of the general availability of SaaS via Workbench. Welcome back to theCUBE. Yeah, I'm happy to be here. I mean, you'd be hard-pressed to find something I'm more excited to talk about than via Workbench, so. You know, one of the things I love about the product and just where it's going is it's really kind of teed up perfectly for both the AI wave and developer needs. If you look at the market, and we report this on SiliconANGLE and cover it on theCUBE with other experts, the rise of open source in the AI world on the model side is merging in, almost connecting with the proprietary models from size and functionality. And also the requirements to run them are getting easier, seeing some models running on an iPhone. So we just talked to Intel about cost of ownership, so the developer community is booming. That's right, yeah. So having a Workbench is a pretty big deal. Yeah, Workbench, you talk about the developer community and the rise of open source, and you can really see Workbench as us kind of trying to reach back with our kind of deep legacy in these areas, but then kind of bring some of that forward and say, okay, can we bring a tool to the market that blends both the world of SaaS, SaaS programming, but also if you want to just do pure open source or you want to use open source languages like Python, but use SaaS underneath the covers, Workbench just kind of brings this all together in this kind of amazing package. And I think, I mean, we're really proud of it just because of so many, all the things that's able to cover. What's the big highlights? What's the big take on people watching right now, they're SaaS customers, or prospect, first of all, analytics products well-known in the decades of success. What's the big deal? What's the high-order bit of the new? Yeah, so I mean, I would go through a couple things. First, I think about just Workbench is this kind of cloud native, cloud scalable, rapidly accessible development environment. And then so once you have a development environment open, it brings kind of the best of breed tools from kind of the open source community to your kind of palette, so to speak. So you've got Visual Studio Code. If you want to go that angle, you've got JupyterLab if you want to go that angle. And so we're bringing those kind of IDEs to the table. So cloud scalable, cloud native, got these IDEs. But then we're bringing kind of SaaS analytics into all of that. So if you're a SaaS coder, the SaaS language works beautifully in both of those editors. If you're a pure open source developer, Python is there underneath the covers, ready to go right when you start up. You can use it in both of those editors as well. But then if you want to say, okay, I'm interested in the SaaS analytics, this kind of the reliable analytics that SaaS has been known for for years, but I want that in Python. We've now got native Python libraries that are also there in Workbench. And then the last thing I would say for the SaaS coder is in Workbench, we've taken all of these analytical investments we've made in the last few years in Viya that are in what we call our CAS server via what we call actions. Things like advanced computer vision algorithms, advanced natural language processing, neural networks, those kind of things, deep learning. And now all of that's being exposed as in the form of Prox. And Prox are kind of the unit of value in the SaaS language. So all that new capability that's been in Viya is now in the SaaS language. So a SaaS nine coder can kind of come into Workbench. They know the SaaS language, they get all this new goodness, and it's all there in this environment that's just lightning fast and starts up in there. Workbench fills a nice hole in the portfolio. And I'm interested in how it can be used as a lever to add incremental value to the roadmap. So what can we expect now that that hole is filled? And what kind of flywheel effect can you get in the roadmap? Yeah, yeah, so when we look at the roadmap and you see us doing some things in Workbench that are absolutely kind of hints as the things that are coming in the future. And so one of the things that we like to think about in Workbench is it's like this leanest, meanest rendering of the Viya platform as possible. So we've taken out anything and everything that you don't absolutely need for this kind of pure developer persona, this pure kind of SaaS and Python coder. And so we've just kind of leaned it out. And so you'll see us do more of that as we kind of have brought people to Workbench. They're then going to expect that kind of experience of man, this thing starts up fast. It's blazing fast when I'm executing code. So the way we got that experience is because we leaned all those things out. So now let's say, okay, maybe they want some visualization capabilities on top of that. Maybe they want model management on top of that. Well, we've got to make sure that we also render those things out in the same way we've done Workbench so that you get all that, the same kind of, it starts up in seconds. It runs super fast. And so, yeah, it is that bridge. It's kind of like, okay, let's bring you in. Let's get you connected to all these things, all this goodness, and then let's bring the rest of Viya to the table. And you got to keep it lightweight though. That's right. Yeah, yeah. So architecturally, how do you do that? Well, the way you do that is you only start up. You only bring to the table and kind of from a compute and resource perspective, the things that the user absolutely wants, the things that they're demanding. The things aren't just running all the time in the background. And so maybe they start with code, kind of the pure Workbench experience now. And maybe they're using code for ETL. They're getting data in, they're transforming that data via SAS or Python. But then at some point, maybe they're ready to do some exploratory analysis of that data. And so at that point in the future, it's like, okay, as soon as I have that thought, I want to do this. That's when it's like, okay, let's now just go spin up the containers behind the scenes that are necessary for that experience and only that experience so that they then get that same snappiness and performing. What are some of the customer early adopters saying about the product? Obviously, I mean, I was really, I like the UI. You guys nailed that. Great job on that. I mean, it looked easy to use. I mean, I haven't used it, but I demoed, you guys gave, I was blown away by that. So user interface check. I'm assuming they like it. What's some of the other feedback? Well, so on the people like it question, the first thing to note is, it happens over and over again, both internally at SAS, but then externally as we interface with customers. The first time people interact with Workbench, before we even get to feature function, they just smile. Like it's just this like delightful experience. And so that's, I mean, as a developer inside of an R&D organization, that's always just fun. And you know, you've got something when you just see this kind of joy, kind of, you know, wash over people. But then beyond that, the thing that we hear people tell us is, you see this light bulb come on of, wait a second. Okay, in the same platform, I can enable SAS code development. I can enable pure open source development. I can enable open source languages using SAS math. And in the future, other additional languages like R, when they see that they can do that, all that in one tool, that's when they're like, oh wow, okay, this is different. And so that's the biggest thing we hear is people realizing they can kind of bring all that underneath one offering. And so when that light bulb comes on, people get really excited. And you said R is coming, yes? Yes, absolutely, yeah, later this year, yep. You know, one of the things we see is, you guys always had been talking about democratization. Now that you have a real developer product, and so I was intrigued by the slide that said, tool for the data science developer. The data word, data science developer. Now, in this world, the population needed for data sciences doesn't need to be big. That's right, yeah. I won't say shrinking, but you don't need to have these alpha nerd, geeky, data scientist, jaming all the time as a table stakes. You can hire one or even use a managed service. Now you have the more the user base coming on. So you have, which opens up to the developer, classic app developer. Right, right. Yeah, no, exactly. So generative AI and coming into tools like Workbench, whether it's through kind of co-pilots and code assistants and those kind of things is going to enable people maybe coming for more of just a developer background to then dabble in data science, right? You know, I mean, and so that's one way to go. And it'll be interesting to see how generative AI kind of across the board allows developers, data scientists to level up in kind of whatever dimension they're interested in. And Jared, one of the things that came up in our analyst round table this morning, this afternoon was the recognition that the old days, oh, the tech genius builds an algorithm, they come in as public, they have core technology. Okay, new technology is ubiquitous. It's now the glue that holds things together. And everything around is the new IP. And of course we've been saying in theCUBE, workflows and data is the new intellectual property. Yeah. Just tie it into your theme. Right. So to open up the democratization to developers to code with Workbench, the rock star, the superstar, the person could be a business user. Absolutely. So it's like the, now it's shifting to not getting a little anything for the team award or a little pat on the back or a little bump in promotion or raise. Someone in the world could be a game changer on the business model for the company. Oh, for sure. This is a huge power dynamic. Absolutely. So one of the things I like to remind people of internally at SAS is whether it's with tools like Workbench or just kind of what you're seeing happen across the space with generative AI is that, so yeah, doesn't have to be a developer. It might be a business user. But I like to remind people in the future there will, we will be competing with companies. We don't even know exist today because these tools enable this wave of productivity that we've just never seen and never imagined. And so teams of one, teams of two, teams of three are going to be able to do things that we've never thought possible years ago. Yeah. And I like the intelligence being built in the device, another one I like. Another announcement that I personally had that the models selling lightweight models, that's a home run, by the way. I think that's going to be a standard. We're excited about it. The prompt saving concept is interesting because that kind of is a nuanced point, but it brings out the directional, direction of saving stuff. Yeah. And using it later. That's right. And promptless potentially around the corner. Sure. A promptless environment. Hey, it just doesn't for me. Right, right. Yeah, no, we're really, so we're really excited about the prompt catalog work and one of the reasons we're so excited about it is it allows us to kind of blend both, okay, new generative AI, which of course is dependent on prompts. That's kind of the way you interact with that system, right? But a prompt catalog allows you to bring what you might consider to be more traditional NLP task and technologies into the fold that are still amazingly effective, but they're now being used in new ways you would have never imagined before generative AI came on the scenes to make that better, right? And there are very few companies that can kind of blend all those things together like we can just because Viya has all this stuff kind of in the toolbox. Being able to persist them and then reuse them, that's huge. I mean, that's, I mean, we all need that. You know, it's funny, Dave, we've always talked about it from theCUBE I'm over the past decade. You know, when I broke into the business in the A's at a computer science degree, it was the careers called software engineering. Yeah, yeah. Engineering was in the word. That's right. Then it became software developer. Yeah. Then it became coder. Yeah. Then all kinds of other stuff started happening. Chunk and words, yeah. Like as the personas started exploding. The aperture gets bigger, but we're kind of coming back to software engineering, but you don't have to be a software engineer to do it. You're engineering the system. So you guys have to think about, and this is what I want to get your reaction of, how to enable that to be better for the user, now the engineer, the business users, now the engineering systems, they're putting stuff together. That's right. And so you have to be smart about knowing what they might want to need without being a coder. I think that's the trick. Can you share your vision on how that evolves? Yeah. I see this happening with this product. No, for sure. So, I mean, first of all, where I'd start is, I like to remind people, I talk to customers about this all the time, it's kind of this question of like, what do you want to be when you grow up? Right? And most of our customers, they don't necessarily wake up every day wanting to be a professional software development organization, right? They're in financial services or health and life sciences or manufacturing, right? And so SaaS, we have to take that on, right? We've got to bring the professional software development, software engineering, architecture and those kind of things to bear. And so what we've got to make sure is, as we bring tools like Workbench into the Viya ecosystem, as we continue to evolve the Viya ecosystem, that we're providing kind of the world-class architecture for things like, how do you do generative AI at scale, right? How do you bring things like a prompt catalog into a workflow to make the generative AI experience better? And I mean, I feel like take a lot of pride in the fact that we can do that because of SaaS kind of being around. And how do you customize it from my specific use case, from my industry, all the stuff that we've been talking about for months and over a year now. I brought up this line of questioning because your title is platform engineering. Now that word is used a lot in cloud native, Rob Stretcher, our lead analyst in this area, who's here, we're talking to some of your folks about this. That platform engineering in the, quote, cloud native Kubernetes serverless container world is very important. Yeah, absolutely. However, it's not trivial. No. So the skills gap comes in. So I want to get your thoughts on how Viya and now AI could elevate, I won't say let's just say level one engineer or IT person, level three super platform engineering. It's hard to get that training or find people to do that, but we're seeing AI turn level one talent into level three talent across the board from DevSecOps all the way over to business analytics. Yeah. No, for sure. So yeah, we, and we see that internally, right? As we're, even as we're adopting generative AI tools in our software development process. So yes, absolutely, especially in areas where, whether it's in, you know, software development in DevSecOps, anything where you've got some amount of boilerplate code and those kind of things that are part of the process, generative AI is excellent as far as injecting into that and kind of leveling up. And so from the platform engineering perspective, it's our job at the SaaS side to make sure we're bringing the kind of services, the tools, the kind of capabilities inside of Viya that help people access those things. Now, I got to tell you, and I'll make, I won't, it's not a confession, it's more of an observation. So when I saw the demo and the presentation, I kind of had a throwback to like 2008, where I first used AWS. Yeah. When I first used AWS, I was working on a startup idea, didn't want to buy servers. Right. That would have cost me about 10,000 and ends up stuff, all that stuff, and I had to host it. The interface, there was no custom domain support at that time. The URL is easy. There was an easy, easy URL. Matt Garmin laughed because when I was talking to him about the URL, I was like, yeah, I had to go to RightScale to do that. Anyway, but you guys have that all in one package. I can spin up resources. There's no friction. No, exactly. And I'm imagining that you guys must be thinking about the enablement there, knowing your customer base, knowing what that's going to do. It's going to have that Amazon web service effect where the first time you use it, well, the alternative was provision, grunt work. That's right. All this heavy lifting. Install all of that, yeah. So you got this dynamic going on. What are you seeing as proof points? Is it going that way one? Because it seems like it is. I'm sure you agree. But what are you seeing that's validating that? And how do you see it evolving for your customers? Because you know more about your customers than I do. I'm imagining it be huge. Yeah, no, I mean, I think so frictionless is a great phrase. You know, as we talk to our customers that are trying, they want to be doing modern data science, right? They want to access, you know, whether it's generative AI or just kind of, you know, modern algorithmic techniques. But the barriers that's in the way is whether they're even open source, whether they're SaaS is like, how do I provision? How do I deploy? How do I configure? How do I tune? How do I monitor all of those things in the cloud? You've got to have all those skills in house if you want to do all of those things. Kind of step, walk those steps. And so workbench is like, hey, let's just clear all that noise, right? Get out of the way. And if you're a developer, you know, you don't need an IT shop setting this step up behind you. Like just, you know, log in, click a button, it's there, start coding, run, you're good. And you can just turn up and start playing with data. Yeah. I mean, there's no, I mean, and then that's where the discovery comes in. What I'd be curious to see is, and again, just me riffing in real time here and this is that because it's self-service, what accidental by on purpose things happen? Like what, like, okay, I'm going to play around. Oh my God, discovery because we're seeing what's in the eye of that. If you do a good prompt, you get to a quick idea or value faster that then another prompt can follow so you're in a time series of discovery. That's right. The innovation cycle just kind of keeps turning. Yeah. I mean, well, you guys know this, but like from a software development perspective, when you, when you're building a product, when you give somebody a tool and that type of thing happens on the other side of it, there's nothing more rewarding, right? Than just seeing like, oh man, okay, we gave them this thing, quick startup, generative AI, it's all those tools are in the box and now they're just cycling, cycling, cycling. It's like, oh, I never thought that would happen. Prop papa. Yeah, exactly, yeah. All right, so bottom line, what's the bumper sticker for the product benefits the customers, people watching via workbench is an on-demand self-service compute, self-service on-demand compute for architecture, our product that you can explore data quickly, I think I summed it up, okay. Yeah, you did a pretty good job. What is the bottom line value proposition to the customer? Yeah, quick access to a cloud-native, cloud-scalable development environment using modern IDEs, VS code, JupyterLab, SAS code is there if that's what you want. Pure open source is in the box if that's what you want. If you want to blend those worlds, you can do that too and we'll just keep adding things like R and other goodness into the box in the future. What is the benefit and the impact of the customer? Rapid access to actually doing real data science, delivering value in their organizations, getting from questions to answers rapidly. Sure, thanks for coming on theCUBE, really appreciate you. Congratulations, great product. Thank you very much. It's going to be big, I predict it's pretty obvious to predict that. Hope so. Thanks for coming on. Okay, I'm John Furrier with Dave Vellante. Are you watching theCUBE? We'll be right back. Day two, we're on day two of walk-to-walk, two days of coverage, we'll be right back. Thanks for watching.