77. Moving Enterprise AI Applications From Experiments to Production with NetApp – Tech Field Day Podcast

Running enterprise applications in production is a lot different from the AI experiments many of us have been involved with so far. This episode of the Tech Field Day podcast recorded prior to NetApp Insight 2025 features Ingo Fuchs from NetApp, along with Gina Rosenthal and Glen Decker, and myself, Steven FoST, talking about how enterprises are bringing AI applications to production. Welcome to the Tech Field Day podcast, where we bring together a group of IT technical experts to discuss a single idea about key concepts in the industry.

This podcast features a variety of perspectives from members of the Tech Field Day community, including delegates and presenters. And this episode is recorded in association with our attendance at NetApp Insight 2025, which is on October 14th. Tech Field Day is part of the Futurum Group, and this podcast is also published by our sister company Techstrong tv.

In this episode, we look ahead to NetApp Insight and our discussing the many ways that companies are moving enterprise AI applications from experiments to production. But before we start that conversation, let's meet who's on the panel today. Hi Steven and team, very nice to be, uh, here with you today as we're getting ready for the amazing insight event.

My name is Ingo F and I'm the Chief technologist for AI at NetApp. And I'm Glenn Deck Haer. I am a global principal technologist at Equinix.

Uh, I kind of oversee pre-sales, uh, strategy for our, uh, enterprise storage and, uh, data strategy, uh, for our, our customers. Um, and, uh, look forward to the conversation. Hey there, I'm Gina Rosenthal and I'm a fractional product marketing, um, expert.

And I work with lots of B2B companies that are doing a i for their customers. And I am Steven Foskett, the organizer of Tech Field Day, and I am thrilled to be attending NetApp Insight once again with all of the folks on this panel. And, uh, also of course, this is a topic that's near and dear to me.

Uh, we recently launched our utilizing tech, uh, season nine, which is focused on agen AI applications. Uh, we've been talking about this on the, uh, tech Field Day podcast, and of course on the rundown in the Textron gang as well. And I think that the topic of today's conversation is something that we've all been talking around quite a lot, and that is that as we move from playing around and experimenting with AI to actual AI powered applications, we are opening up a whole new world of requirements in terms of data protection, uh, data classification, making sure that things are really production ready.

Ino, I wanna start with you because I know that you work with your clients on this exact topic on a regular basis. What are the real production enterprise requirements for AI applications? Yeah, I think I, I always like to start by talking about data, right?

'cause data fuels AI very obviously, right? So you need electricity and you need data. Um, so to drive your AI data pipelines and, and build your AI factories.

And so when it comes to data, there're really five questions that I always like to ask, um, when, when we are having these conversations. One is, do you know where your data is that you want to use for AI workflows? That's where it all starts, right?

And that could be on premises in many different locations. That can be in the cloud. That can be hosted clouds, that can be sovereign clouds, that can be neo clouds, which are these new GPU clouds.

So data can be all over the place, including shadow ai, right? So a lot of customers that start their AI experiments, they built little silo over on the side. They put like a singular workflow on there.

They have a data scientist work on it, maybe data engineer or two, but it's relatively small in scale. And things that work in the silo may not work the same once you go global. So where is your data is kind of the first question.

The second question that I always like to ask is, what is in your data? So do you actually know what you have in that data? Can you classify that data?

Is there personally identifiable information? Then there is there credit card data in there? And you know, some critical data that you absolutely cannot let move through the data pipeline to an agent or to a chat bot.

Because if you don't let the customer credit data, credit card information even get to the agent, then you don't have to worry about restricting the agent from giving that information out. So you, if you stop that data from getting into the pipeline, that's the right time when you want to stop that data that you don't want to get out. So where is your data?

What is your data? There's a lot of conversation about structured versus unstructured data because in ai, most of the data that flows into AI workloads is unstructured data. So now you would need to put a structure over that data, which is really what vectorization and embeddings are about.

And then you end up with this vector database load, you might need 10 x the capacity of your original data just to build the structure just to do your embeddings and vectorization. So that's critical. That's number three.

Number four is your data current. A lot of customers have data all over the place. And to find out whether or not this data has been updated is a complex and costly project.

And you may only wanna do this once a day, once a week, once a month, once a quarter, depending on the complexity of your environment and how much compute and networking and other capabilities you have. But now, if you have, let's say a customer service chat bot that's operating on data that's a month old, it's not gonna be very useful. And then finally, and maybe most importantly, is the data appropriate and ethical for your workflows.

So it may be legal, but it is, is it ethical? Is it in line with how you want your organization to be seen by your clients and by your customers? So you have, you may have a lot of really deep insights into your customers, but if you start make, making pricing decisions or other decisions based on that, is that gonna cause some negative perception in your customer base?

So it may be completely legal to use certain data, but is it ethical? Is it appropriate? Is it in line with the kind of morals that you wanna put out there about your organization?

So those are kind of the five questions that always start the conversation with, which has nothing to do with infrastructure, but infrastructure helps tremendously in solving some of these challenges. Yeah, I, I would look at these five questions and you know, I I, I, after digesting them, um, none of them have easy answers, right? And, uh, and of course, infrastructure doesn't solve any of those questions directly, right?

The, there, these are things that must be dealt with by AI centers of, of excellence in, in the enterprise and, and executives, and not just data scientists, but lines of business. And, and of course lines of business is where are, where all of the, uh, AI workloads seem to be starting these days. But, uh, you know, for instance, like where is that data?

Uh, that data could be, uh, you know, all spread out through your global organization. A lot of it could be in SAS platforms that aren't even within your perimeter, right? And you have to somehow get that all in.

Um, and you know, what we're finding is that, uh, many times once customers try to start AI and, you know, endeavors, they'll go and do it in the cloud. 'cause it's easier to start doing something with synthetic data or small data sets that aren't represented of the full production workload. And then, uh, when they go and bring it to try to get it to pilot even or to production, all of a sudden, whoops.

Can't, can't do it that easily. I gotta go pull in all those data sources. I gotta do curation, I gotta do duplication, I gotta do all these things.

So, uh, so, but what we're finding is that, um, you do need to, to, to deal with that data very, very early on as you've first determined your use cases and you're doing data discovery, which is in and of itself is a huge project. Uh, but there is value in consolidating at least one copy of that data, um, I'll call it, on equipment you control and locations you can access, right? So that from a compliance perspective, which goes to your fifth question, I think you, you also have to add in governance and compliance to, to, uh, appropriateness and ethicality, right?

'cause that's honestly the way the world works in the diff, you know, with GDPR Cloud Act, all these different things going on, the AI act there, there's a lot of constraints on what you can do with this data. So, um, bringing at least one copy of that data onto stuff that's totally under your control, which you then can now move to clouds and neo clouds, as you're talking about that data mobility, that ability to, to take that data that you've got and do what you want with it, when you want with it, it's gonna be key because you have no idea what your future AI life is going to look like. So if that's the case, then you must build in this ability to change, right?

And so the ability to have data sets move from one platform to another in a common management framework, right? In a common way of accessing, controlling, and governing, right? Um, I, I, I happen to know of about a company that does that.

By the way, Ingo, you, you may know them pretty well, but, uh, that facility coming first before you are even thinking about the broader impli implications of infrastructure, GPUs, right? Um, liquid cooling, all those kind of things. The data is absolutely first, you're right.

But you've gotta bring that data to, uh, in, in the first place. You need to build that kind of core where that data can sit, um, in, in that governed way where that's where your curation, your data duplication, and you can deal with all the five of the questions, at least the, the remaining four, what's in it structured versus unstructured, right? Is it current, right?

And then deal with the, with the governance and the compliance of that data. Those are the other four questions. So to answer, for an organization to answer question number one, it's in their best interest to discover, consolidate onto something physical and then get that data back out where you want to experiment with it.

Yeah. And I would, I would expand on that a little bit. Uh, is that, so this really leads to this idea of unified data model where all of your data is seen in a singular model, and then you can apply your applications against that.

Similar to what you described Glen. And, and part of that is because a lot of times there might be this new groundbreaking model or a new groundbreaking application, or you choose a vendor to, um, apply their chat bots against your data instead of building them your own. We see a lot of statistics that using off the shelves, AI applications, typically majority of cases, cases today, leads to better production outcomes.

Then trying to do it all yourself, it's, so it often is kind of a mixture of things. But let's say you have a public cloud provider that comes out with this new application that you want to apply to your data, and some of the data might sit in that same cloud, might sit in different cloud, might sit on premises. How can you build a peering model or a caching model or a data transfer model that gives you the most efficient way to apply these applications against the data that you have, regardless of where it is?

So for me, that's all a question about this unified data model that spans all of your different deployment locations and infrastructures and sites and models to say, listen, you might have 80% of this data on premises, but 20% is in a cloud. And can you then peer to that cloud? Or can you cache to that cloud just the amount of data that you want?

And then also not leave another copy behind. I think that is one of the biggest problems that we are seeing right now, is that there are so many copies and nobody wants to throw anything away. A lot of companies have very strict rules about how long to keep things, but not very good policies about when to get rid of things.

Nobody wants to be at fault for having permanently removed data, and then suddenly somebody changes their mind. And so this unified data model can really help with that problem of just having this full view, like you described, about what data you have, where it is, um, and then where the applications are that should be looking at this data and using this data. I think it's really interesting, everything y'all have said so far is just data center hygiene.

It's not even data center hygiene, it is application hygiene. So I think everybody has raced to, to juice their ideas and to put those ideas, um, to work and find a way to make them work with different, um, architectures and models and different, um, ways of doing things. But now all of a sudden it's like, Hey, yeah, how do we, like you were saying in the beginning, how do we make this, uh, enterprise ready?

How do we make this production ready? And I think that's where, just from clients that I've been working with, people find out, oh, we really don't have the data the right way, or we don't, we have everything, every place. Or we went and we purchased a bunch of infrastructure and it's not going to suit the purpose of this application.

What do we do? So I think one thing I'm really looking forward to hearing about at NetApp is to hear these customers like be able to talk to customers and hear more about, uh, the transition plans that they have, or kind of the gotcha moments they, they hear, because this, we repeat this every cycle. We do something new.

We were talking about that earlier, whether it's cloud computing, I can remember a lot of this 'cause I was a cisman going from, from whatever to when Linux, well, from Solaris to Linux. Like all of those applications had to be rewritten and why, you know, so it's kind of like the same kind of kind of thing as before, but it always goes back to let's be computer scientist and let's remember what are we building and why are we building, and then what is the correct application for it? And we have so many new things that we can do.

Like, so having a unified namespace or unified area where you can see all of the data where it is and maybe be able to use that at one time for one, um, purpose is, is really kind of mind blowing when you think back across 30 years of, of computing that that's even possible to do. It's pretty amazing. So I'm, I'm looking forward to seeing how forward your customers are doing that and how you guys are actually supporting them.

It's gonna be kind of cool. Yeah. I think that he also, you know, when you, when you have that, the, the enterprise data that's been, that, that, that runs throughout an organization and, um, you've, you've now created this, this factory and, and the first kind of station in the AI factory is of course the data pipeline, right?

Um, that and things just changing so fast in this field that the needs of data and from a performance, from a, uh, uh, just a retention perspective are, are changing, uh, so fast. And, and companies don't know what AI is gonna look like in, in another year or two. Uh, it's most of the time.

And, and we've seen up till now. And, and, and, you know, some of the things you've said imply this, right? That, you know, enterprises are gonna try to pull down existing models, um, or, you know, use commercial models, uh, perhaps and, and just try to use rag against them with vector databases.

And that was kind of, I'll, I'll say that was the first or second wave of the ai, you know, folks that are coming out, we're already seeing that. Um, you know, not all rag is vector databases, right? There's a lot of, especially as you now to bring agentic, right?

Agentic is the next wave. And agentic really implies things talking to things in an automated way, everywhere, uh, data, I mean, just the, the, the amount of compliance and governance, uh, uh, and security concerns that, that, that arise from just the, the explosion of this, uh, it's kind of mind-boggling. Um, companies don't know if they're gonna be maybe dabbling into some fine tuning smaller models, expert models, right?

That all, all cont uh, you know, are contingent upon the data being ready for all of these possible different use cases that you have no idea if you're gonna need to go use it next year, two years, three years. And, and so keeping data in, in any tier, whether it's, you know, in, in checkpoints, I mean, even now with the reasoning models, making things even worse because now, um, we see, uh, KV cash being stored as a state, an intermediate state that can be sent to the edge and used in real time for high performance inference at the edge, um, that also requires high speed storage. It also requires data motion capabilities, consistent data motion capabilities.

It's just like all of these things have come out in the last 18 months and no one has had time to digest all this stuff. The rate of change is only accelerating, ironically, being accelerated by AI itself, right? And, and our use of it.

So it's like, uh, the thing you need first is to make sure the data platform that hosts all this stuff is gonna be able to accommodate this rate of change with you as you go and figure out this new stuff. Because as you get to production, um, you need something that's gonna grow with you and be able to scale on a specific use case that you may not even know you're gonna do for another two years. See, I'm hoping the next wave is people are actually gonna take a breath and take a beat and figure out how to do this in the appropriate way so they can move from, um, science experiments to production.

Oh, I, I hope with you, I'm not, I'm not so sure it's gonna happen, but I think it has to happen. And that's the thing, I think that that is interesting about going to something like NetApp Insight, where you have have like real enterprise people trying to do real enterprise things mm-hmm. As opposed to a lot of this breathless AI hype stuff that we get exposed to on a daily basis.

Like the people that go to Insight and the people that are NetApp customers, they're not interested in hype, they're interested in building a Yeah. Supportable, profitable, you know, productive enterprise application. And so for me, when I go to an event like that, it's talking to people who are really doing this stuff and learning from them.

Like, what were the challenges? And I think that that's the most interesting thing that Ingo, uh, brought up, is that, um, it, it doesn't start with GPUs and models. It starts with data, right?

Yeah. It, it really does. And I think, um, all of your points are really, really valid.

And, uh, I wish we had like six hours on this podcast to go into how we are going to move from file and block protocols to semantic understanding of data and how you just talk in conversationally with your storage system in the future and, and how all of that is changing. But, um, Steven, to your points, I think a lot of the conversations that I have had with customers over the, the last, you know, year or two were really focused on, oh, ransomware attacks are now powered by ai, how I'm going to protect from that, you know, oh, now I need AI powered ransomware protection to protect me from that. Now I have AI workloads and, and pipelines that are sharing the same infrastructure as my, all of my other Oracle sql, all of my other workloads that I have in my enterprise today.

So suddenly this old conversation about secure multi-tenancy is a core conversation. Again, how and quality of service, if somebody were to introduce a rogue agent into my shared infrastructure, can this rogue agent take down my other production application that I rely on that are actually producing things in a physical factory? Can my AI factory take down my physical factory, right?

So there's just, you know, how do you peaceful coexist with the infrastructure that your business rely on today while you're building applications for your next competitive advantage that ultimately run on the same infrastructure unless you do wanna build a separate data center. Gina and I talked about exactly this two episodes ago on the Tech Field Day podcast, and we are a hundred percent in agreement in alignment with you on this. Definitely.

Absolutely. And so it's, I just find, um, there is a little bit of a translation layer, and I've seen it with cloud, I've seen it with DevOps and containers the same, uh, thing. And I think, Gina, you talked about Linux, how Linux became an enterprise class operating system, and, uh, production applications running on it is starting with web service, right?

Which is really kind of important, you know, aspect of an enterprise environment. And so, um, we need to, I think all of us in this industry need to do a better job at translating the language of what AI practitioners are looking for and, and map that to the things that IT practitioners understand the value of snapshots, the values of efficient mirroring the values of data deduplication, and, uh, protecting from copy sprawl, uh, efficient data protection, peering into the cloud, caching into the cloud, and back from the cloud. All things that people have really figured out over decades.

But we are all using our language. And then AI practitioners usually use a different language, and we almost need a little bit of that translation layer to say like, this is not a brand new thing that you have to build. This is something that companies have figured out in the past.

We can apply these experiences, especially for production. And what that will help us achieve is that we go away from this, you know, making decisions based on milliseconds and iops and, you know, how many agent requests can I transact per second into, oh, my data scientists can be productive three weeks faster, right? If I have a data scientist sit around for three weeks, not doing anything, waiting for infrastructure to actually deliver data to the application, that is very, very bad, right?

From a productivity perspective, if I can cut days or weeks out of these waiting periods, um, that is really ultimately making a huge difference. So I, I love that analogy so much. And, um, I think you are absolutely spot on with, you know, our job has always been as operations people to support the business.

And that this point in time, it's like, how do we support folks doing ai? How do we support the data scientists? How do we support everyone on that end?

Um, and that translations needed. I remember I started doing the translations for SaaS. I could show you some of my presentations I did when everybody laughed at me when I did 'EM at operations, different type of operations shows.

So I think, um, I'm, I'm looking forward to seeing how y'all are gonna do that translation when we get to Insight and, um, hopefully there's gonna be a lot of hands-on labs and stuff, so we can actually get our hands dirty and do that too. Absolutely. Yes, for sure.

And, uh, we are building like some really great experiences, uh, is specifically targeted at giving people with a strong storage background and, and, you know, the opportunity to expose themselves to, oh, this is what a data engineer is asking for, and this is how I can help. This is what a data scientist is looking for, the end of the data data scientist wants to see your I to point an application at. They don't wanna know what all the infrastructure is, where the data is coming from.

They typically don't, don't care. The data engineer cares, right? And so we wanna create some transparency there and expose the very storage focused audiences at insight to this is what a data engineer's asking for and why, and how you can help.

And this is what a data scientist is asking for, and how your knowledge will help the data scientists and it'll make it practitioners just that much more relevant and important, uh, and it will make all of our customers that much more impactful and, and quicker, uh, to getting their AI factories up and running and be productive. Yeah, I think, I think that we, we've all probably seen the MIT study that was talking about how, so, you know, such a large percentage of of AI projects today are not producing the value that was anticipated, right? And so, um, the, I think, and, and they're finding really was just that you're, you're going after the wrong things.

And so the good news is that, and, and the folks usually see it inside of the kind of folks we're gonna be solving the back office, the, the productivity problems, not just the, the content marketing stuff that we've been, you know, that we've been enjoying over the past couple years now, but now that I think organizations are gonna be focused on really producing true business outcomes that are gonna be visible on the bottom lines, right? I think you're gonna see a lot more folks in IT get a lot more knowledgeable on AI and, you know, all the constructs on, on what a neural network is, what is back propagation, right? What, how does all this work?

You know, what, what's an intention mechanism? They're gonna know you're gonna need to know this stuff. And, um, then those same people are gonna be able to apply that knowledge back to their IT architecture, um, you know, their kind of schema that they kind of know because they're gonna, they're gonna be the ones who are gonna have to build the data fabrics of the future, right?

They're the ones who are gonna have to build this distributed, whether it's in cloud, and it's gonna be, by the way, an and not an OR in the, in the multi-cloud, in the, you know, on on-prem, multiple regions, right? Uh, out to the edge. Um, and so that data platform is gonna be that, that, that thing that drives the success and, and all the repetitive outcomes out of AI that are gonna be, need to be generated.

This isn't gonna be like, you're going have to two tr two things, take the win and go home. This is gonna be a repetitive, and, and every business is gonna be looking for more and more and more value out of ai. And every, every time you do that, they're gonna be getting better.

And that data platform is gonna become more and more important because now it's driving everything in the business that drives all the AI outcomes, all the models, all the tuning and rag, all that stuff starts with that data platform. So that's why it's just so imperative that people figure this out. I, I absolutely agree, uh, with you.

And, and there is absolutely a lot of educational opportunity there, um, that, that we need to, you know, see through and offer. And in fact, we even offer NVIDIA certifications this year. So if for folks that are attending inside or have attended inside in the past, you know, you can all get all kinds of, you know, like NetApp certifications, et cetera.

You can even get certified on NVIDIA certifications, uh, at our conference. And it's reflecting exactly what you said is you can't just ignore ai. I mean, you can, but, you know, if you would like to stay employed and be effective and have an impact, uh, in your organization, it's probably a good idea to get up to speed on all these CI topics and how you as an IT specialist, a storage specialist, how you can make a really meaningful impact, uh, to your organization.

Yeah. I, I was actually gonna ask you about some of the recommendations Ando that you have for Insight, um, if people are attending, and also for people joining remotely, uh, I know that there's going to be some, uh, sessions including, by the way, I should point out Tech Field Day videos. We will be posting some Tech Field Day videos, uh, maybe featuring from some familiar spaces from this, uh, this here recording, um, after the, uh, shortly, shortly after NetApp Insight.

Um, what else? Uh, you know, is it too late for people to get involved? Uh, how can they come and join us, and are there opportunities to connect online?

Yeah, absolutely. I mean, uh, the, uh, the registrations are open for insight. Uh, we're still, uh, taking, uh, registrations it getting tight, so, uh, sign up now.

Um, but obviously for those of you that can't make it, uh, we have the, uh, we have the streams, especially for the main stage sessions, which I think will be very, very interesting. And I, I cannot tell you about some of the amazing speakers we are going to have. Um, but certainly AI will be a very, very big topic, uh, at inside this year.

And then yet, tech Field Day will be able to really sit down and, and have some very in-depth and detailed conversations. Um, you know, Stephen, between your representatives and some key people here at NetApp, including, I believe we will have our chief data scientist, uh, there involved in one of the sessions. And we have some really great engineering, uh, folks that will really provide a lot of the behind the curtain kind of details of how we have achieved the things that we have achieved, why we have made certain choices, and how this is really changing fundamentally how customers should think about infrastructure in the context of AI in production.

And on that note, um, you know, I think that there's, there's definitely a lot coming from this. Uh, Gina Glenn, you're both attending with me. I already said that my favorite part of events like Insight is talking to the customers that are there, uh, who have incredible experience.

Another thing that I always look forward to is, uh, getting a chance to meet some of the incredible, um, sort of luminaries that companies bring in for these events, whether they're on stage or contributing. Uh, Gina, what do you look forward to at NetApp Insight, and what are you hoping to take away from it? Uh, both of the things you said, I definitely wanna talk to customers.

I really wanna see who's going to be on the show floor, the other vendors, and talk to them about how they're working with NetApp. I have a lot of friends that work at NetApp, so I'm, I'm very excited to catch up with people. Um, sounds like I'm gonna be doing some hands-on labs and seeing some of these things that Ingo is talking about.

Um, what I wanna take away from is I wanna see where customers are, number one. Like, where really are they? Because I'm, I'm hearing from, you know, other, what some of my clients hearing from them about what their customers are saying and what the problems are and how we're gonna, you know, from a marketing perspective, how do we work on that?

And I'm also interested in hearing about, to talking to my friends who are on the marketing teams. Like what, how are you guys positioning things and how are you con positioning, um, working with your partners and things like that. So, Um, so, so Insight.

Um, I've been going for quite a few years now, and, um, net NetApp kind of set the standard at Insight for technical content that they delivered in the sessions. Um, they, they, they tried to stay away from being too, uh, too salesy and, and, and tried to show you how things worked and got pretty down and dirty with stuff. So the sessions, uh, you know, I'll try to consume as many of them as I can, uh, in, in especially those sessions that are demonstrating how NetApp is solving for outcomes in ai, right?

How you're, how am I getting this data ready to be consumed by either, you know, inference through rag or for, you know, getting it ready and, and, uh, and, and getting it, um, you know, you know, curated and groomed for, uh, for training and tuning and things like that. So, uh, and, and also moving it around, uh, in that case, how, how is NetApp using its core strengths in data motion and consistency, um, to meet the needs of, of customers in the distributed AI world. And so that, that, uh, has particular meaning to me in my role, but, uh, showing how that works.

Insight's always been really good at, at getting to that how, right? Instead of just the what. So that's what I'm looking forward to.

Well, I'm glad that y'all are coming. I can't wait to see y'all there. Um, those of you watching, again, watch the Tech Field Day Channel, watch the Techstrong TV app.

You'll see a lot of great content there. Uh, before we go, uh, Ingo, Gina, Glenn, where can we connect with you and continue this conversation other than Las Vegas at Insight? Yeah, well, insight and ideally at the bar, uh, would be preferred, but, uh, LinkedIn, I'm there.

So LinkedIn is, is definitely a good way to get, get in touch with me. Um, I'm also presenting at a lot of conferences. I was just in Berlin last week presenting at the EI and Quantum, uh, summit there and exploring some topics like, uh, uh, post quantum cryptography and how to get ready for the upcoming quantum thread, but that's a different conversation.

Um, so that's, uh, certainly another way. But yeah, LinkedIn is, is a good way to get in touch. And I do, I try to post.

Um, but, uh, frankly, it's, uh, I'm not posting anywhere near as often as I should. You can find me on LinkedIn, that's the best place. Um, you can find me on LinkedIn.

Uh, that's a great, I do respond to messages there. com. And at the end of October, you can, uh, I'll be presenting at the, uh, Nvidia GTC DC conference for Public sector.

So catch me there. And, uh, NetApp, NetApp will be at GTC DC as well. We'll be able to see who they're, Yeah, our boots aren't that far away from each other.

com/insight. There's a lot more information there, uh, to come in person or to connect online. So thank you all for joining us, and thank you for listening to this episode of The Tech Field, a podcast.

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