UT08x05: Bringing IT to Healthcare and Research with PEAKAIO and Solidigm

AI applications have unique requirements for server infrastructure. So a new platform is required. At the same time, we have to start with the fundamentals.

What does the user need? What does the application need? Instead of just thinking about what the technology can deliver, that's the subject of this episode of Utilizing Tech, featuring Mark Lesinski of Peak, A IO, Janice Roski, and myself, Steven Foskett.

Welcome to Utilizing Tech, the podcast about emerging technology from Tech Field Day, part of the Futurum Group. This season is presented by our friends from Solid I and focuses on AI and the Edge and other related topics. I'm your host, Steven Foskett, organizer of the Tech Field Day events series, including AI Field Day and Edge Field Day, and joining me from Solid, IM as my co-host today is Janice Roski.

Welcome to the show, Janice. Hi, Steven. Thank you for having us.

It's good to be back. It is good to be here as well. Um, and we have been focusing all season long on the sort of unique application requirements for AI servers, for Edge servers, the fact that these are somewhat different than what we found in the conventional data center.

Yeah. And you know, the world is pretty wrapped up right now around enterprise and all things, you know, power and cooling, but it's, it's really interesting to take a look at how are organizations deploying ai truly at the edge. And so we're, we're delighted to have with us here today.

Uh, mark Klazinski from Peak a IO, who's gonna talk a little bit about the unique use cases that they deploy at the Edge. Welcome to the show, mark. Uh, why don't you introduce yourself quickly.

Uh, thank you Steven. And hello Janist. Uh, I'm Mark Lesinski, PKIO.

I am the founder of PKIO, and I have the, uh, good fortune to have worked with soine for a few years now, uh, with a real focus on ai, predominantly in that incubations period and that edge case where it's developing more and more. And hopefully we can discuss some of those, uh, those exciting and up and coming, uh, current and emerging, uh, use cases. So, mark, uh, tell us a little bit more about, more about, uh, peak a IO specifically.

What is it that you're building? So, Steven, let me jump back a few years, you know, pre Covid, which we have almost forgotten now. Um, I was, I've been in storage, as you can see, I've, I've gone past the gray stage and I've been in storage for 35 years now.

And I was happily consulting to within the Nvidia channel at the time. And at this point, AI was beginning to take off. This is way before chat GPT and some of the early pioneers, which were the obvious use cases like healthcare, et cetera.

They were, they were moving ahead and piring in some amazing projects. But the challenge was that while NVIDIA had made this new amazing ecosystem and this new market completely, the, the rest of the infrastructure hadn't really caught on. And so, you know, the solutions were going out, but they were really not performing maybe quite as well as they should do, because everybody had really re-badged traditional IT products and met, turned them into AI branded products, but they weren't really working for a whole bunch of recent technical and, you know, use case pitch.

So we actually started PPIO and we, we realized that there was a need. This was not just a new market, uh, that was demanding a completely different level of performance and had a different use case. It needed a whole different range of, uh, ecosystems and infrastructure.

Why would we expect, um, data storage that's being de developed for enterprise use to suddenly working in AI use? That's completely the opposite. So we really focused on developing, uh, AI storage to accelerate, uh, the use case at the time.

And we were really fortunate to work with some of those early pioneers in the healthcare and beyond to allow us to, for the first time, probably in my lifetime in storage, where we didn't design something and tell the market what they needed. We actually listened to the market and go, Hey, what, what is this new thing? And what, what challenge do you have?

And the challenges, which just so fundamentally different. We, we just simply started afresh, luckily enough to, uh, to work with soine. And we built from that Soine foundation upwards to deliver what they needed, and it's actually what they needed to achieve the best out of that ai, uh, roadmap.

So, so Mark, with that, um, thank you for that introduction. Uh, I just wanna follow up and ask, so is it, is it software that you guys do, or what, how is it that your solution is vastly different than than others on the market today? Yeah, it is purely software now.

There's nothing necessarily new about software defined storage, as we call it. Um, what is different in our case is we took a step back and we said, Hey, you know, when we were making software de defined storage from 20 years ago, we had hard drives. We had 10 gig nicks.

We had a, a bunch of 20-year-old technology. Today we have amazing MVME from you. We have amazing networking from others and wonderful off the shelf servers that are, you know, of the power that we would've only dreamt about.

So in this case, what we did is we said, well, let's not take everything we know. Let's just take everything that's available and put it together and get as close to that hardware as we can to make it work in a way that the user needs it. Nothing more, no smoke, no movers, just deliver exactly what the user needs.

And the advantage of that one was that it's a, you know, a a different level of simplicity, uh, which is exactly what an AI user needs, because they're often a, you know, clinician, a doctor professor, a biochemist, and not an IT specialist, but also that we're so close to the hardware that, you know, I'd love to say it was an amazing strategy, but that meant that when you guys brought out generation five, we just doubled in performance because we were basically taking what you delivered and making it usable. So that's what separates us. We take off the shelf hardware and turn it into hyper fast AI focused, uh, data acceleration.

Now, when you say, uh, AI focused, and, um, you know, what exactly do you mean? I mean, is this, is this for the, the big AI supercomputers in the cloud, or is this for, um, your doctors and engineers mm-hmm. And so on in the field?

Originally it was the doctors and engineers in the field. We, as AI's moved and projects have become more mainstream, then they are getting bigger and they're becoming more known. But one of the largest challenges, Steven, actually, I mean, it's, it's obvious when you know it, but if you think, if we just simply think about what we had before ai, what we had was an enterprise customer who may have a thousand machines.

You know, 500 of them were probably mobile. Uh, you know, you know, laptops. Uh, 200 of them were probably workstations, 10 servers, you know, an email server, database server.

So thousands of connections, but from thousands of machines, none of them demanding ridiculous amounts of performance. Maybe one or two, but all of them just want in a decent amount. On the opposite side, you add HPC or still have HPC that generally would have millions of calls over thousands of compute nodes.

And so you've now got storage here that's delivering tremendous performance to millions of calls over thousands of networks. Whereas suddenly AI came along and Nvidia said, well, hey, we've got a million calls on two machines. Well, we've never seen that, you know, we'd never had one machine demand that much performance and be able to, to sort of basically take the entire performance of storage over a protocol and just, you know, we were able to deliver that.

But generally, in, in the past, that would be over so many machines. It was AI fundamentally changed the way we delivered data. And in the beginning, yes, to be fair, there wasn't the giant super pods that we see today.

You know, everybody was learning ai, right? You know, nobody really knew what they were doing, they just knew they needed it. The amount of conversations I had that were saying, yeah, we we're going down the AI path, and it would be, well, what you doing?

Well, we don't know, but we know we need ai. And, and everybody did that. I think it was probably some years later before, you know, it became obvious that there was a use case in just about every vertical.

So really at the beginning, it was very much smaller, uh, clusters of one to what we call DG x's, HG x's, which is sort of the, the NVIDIA servers. And that would really be, you know, a professor in his team, team or a company that was testing some AI projects or even, you know, a HPC company that was trying to work out how to use GPUs. And so they certainly were a lot smaller, but, you know, they still demanded that amazing performance.

So really the difficulty was we'd always had performance. Our, the, the advantage we had was that we, we were able to deliver that to many machines, and it was aggregated, suddenly having demand and to be able to deliver it to one or two was really challenging. Yeah.

And you mentioned Mark, you know, uh, being customer centric and, and really getting in with the customer and listening to what they have, you know, what their challenges are. And, and I think you're right, not everybody gets access to the big, you know, DGX, uh, you know, servers and, uh, let's be honest, who can get their hands on A GPU right now? Right?

So there's, there's lots of challenges, but, you know, your, your solution being that it's true edge, right? It has all that power that you mentioned of some of the big super pods, but you're putting it as close to the patient, if you will, and the physician in, in a, in a hospital environment. So let's take like an MRA use case, right?

I heard you guys once say, you know, someone coming out of that machine before they even tie their shoe laces are able to get their results. And, and tell us a little bit about what, what enables that, what does that look like? Yeah, I mean, we, we were really fortunate that one of our first encounters in AI was with, um, a large university in the UK called King's College London and Associated universities.

And they, they, they were really focused on what they called ai, uh, value based healthcare. Because if you think about healthcare, there's an advantage in at every level to the patient, to the government, to the, in our case, in the uk, the National Health Service, the local authorities to the insurance companies. There's an advantage in diagnosing or getting, you know, providing a better pathway or outcome quicker.

It saves costs, it saves lives, it saves stuff. And so we were really, you know, blessed to, to have worked very close with these guys, and they were doing such tremendous, uh, work. And I can remember, uh, actually one of the lectures that one of the gentleman was doing, um, he actually said the, the overall goal was, let me try and get this right.

It's not for verbatim, so I apologize to George. But it said the overall goal was for them to be able to, to collect the collective intelligence of every radiographer in the entire world that has the knowledge of every rare disease, as well as every other, um, you know, MRI scan output, and be able to put it into a little box and into a model. So regardless of where you went for an MRI, that could be in the middle of California, Sacramento, or it could be in the Outback in Wales and the uk, you will get the exactly the right person looking over your MRI and being able to make an instantaneous dis uh, decision.

Now, the first, the, the difficulty with that, you should, you then learn with ethics. Is that correct? Should that be right?

Uh, but actually, if you twist it around a little bit and say, well, actually we, certain in the uk, and I suspect it's worldwide, we, we have a shortage of radiographers. Not many people grow up, you know, in school today wanting to be a radiographer. It's not on the curriculum.

And so this isn't to replace that. This just helps that workload. So for instance, it generally, the, the decisions today are often put in three categories.

Uh, I've gone into an MRI, it sees a problem, it sees what is, it is pretty sure it is a problem that needs real investigation. It's not sure or it doesn't see a problem. And in reality, the, the, they're not sure, or I don't see a problem, they will still go to a radiographer.

A human should still see that, that to double check it, obviously. However, if you do see, if it does see a problem with a degree, you know, of, uh, confidence, why not take it straight to the next step? Why wait in that waiting list for a radiographer to look at it and just take 'em straight to look consultant that's going to overview, yep, this really is a problem, and we're gonna start, you know, some treatment.

So we were fortunate to be involved in the early trials of that, and also the development of something called MON ai. And that's become, uh, KCL started this, uh, professor Sebastian and his team. Uh, and that's become my world standard open source, adopted by Nvidia.

Because prior to this time, and I don't wanna re elongate this much, but I remember doing some work, and we looked around the world, and at that moment it was something like 450 individual, uh, healthcare AI projects, or doing something similar, but none of them shared data. None of them had anything in common. They were all their own teams doing their own work.

So one, AI was created to make a common operating system almost for the hospitals. So it gives a basis that then the individuals can write their projects on top of it, which means as we move forward, that there's a common framework that will allow every hospital to, in some way interact and gain from each other, even if they're not sharing data. It, you know, it's interesting that to hear you speak, because what you're saying is, I think something we don't hear all that often in tech generally, and in AI specifically, which is that you're starting with the application, uh, with the use case, with the need mm-hmm.

Rather than starting with the technology. Yes. You know, you have the basis in technology, but you're saying, first, let's think about how this is gonna be used, what it's gonna be used for, how will it benefit people?

This is such a contrast from Yeah. What we are hearing, um, in popular culture about ai, you know, the explosion of generative AI apps and chat bots and so on. I think the biggest criticism of most of that is that people are not doing what you're doing.

People are not saying, what are we trying, what is this technology for? What are we trying to achieve? And how can we achieve that?

And instead they're saying, wow, this is cool. How can we push this further and further and further to do something? And, and also everything you're talking about is, is very much not chatting with a large language model.

Now, it could be that could be one of the tools you're using. Mm-hmm. But again, AI applications and productive AI applications, especially at the edge, as we heard about when we talked to Nature Fresh Farms and how they're growing tomatoes with ai, um, you know, the stuff that you're doing is, is a completely different world.

And it, and it's, it's really refreshing. Um, you know, how do you bring technology to the problem and vice versa? And how do you avoid that sort of irrational exuberance for such cool, fun technology as generative ai?

No, that's actually a really good question at many levels, because that was one of the big learning curves for me, because clearly, as I said before, I've spent many years in IT and storage, and most businesses, me included in previous storage companies, what we tend to do is we tend to, we think somebody like me designs what we believe is the next generation. We do that in staff mode, then we launch it, and then we go out and evangelize to everybody why they need it. And the strange thing is, is when we came to AI and everybody, you know, every vendor did pretty much the same things.

You know, you need this to make your AI go faster and better, better return on investment, all the things. And yet you got to the professor and you went, I, I don't understand you and, and I don't need you. And I don't want that at all.

That's all I wanna do is solve a, B, C. And what was really refreshing, I suppose, when you got to my age, is it was actually the first time where the market was so new, it was so, uh, at the edge that nobody really knew where it was gonna go, and still don't, today, we still get surprised by some of the outcomes. And so I, I initially sat down when we were trying to work out what was going wrong with storage as we had it at the time.

And I can clearly remember some of the early conversations with some of the universities that were doing some really pretty cool work. And I remember saying, okay, we we're involving two deject here. How are we gonna deal with the storage?

And they generally turned on to me and says, what do you mean by storage? Because they were looking at this as a problem. And that specific problem was a medical one, again, where patients who had given birth on a Friday, if it happened to be over five o'clock when the doctors had gotten home, they had to wait till Monday to determine whether or not their baby had a problem, a particular type of problem, because only the doctor ran that test.

And so the clinician was saying, Hey, this makes no sense. We could just use AI to do this. Yet he had absolute no understanding of it, didn't want any understanding of it, he just had a problem and a bunch of tools that could probably make it work.

So it was refreshing and back to the question to, to actually, not to the market what they need and have it people waiting for you to give next generation, but to actually have a market saying, look, we need this to solve this problem. And A, that's refreshing. And b, it's just a lot more fun because you're doing some, we've spoke a lot about medical, but we, you know, as you know, you knows, we, we've worked a lot with the, um, serological Society of London.

And that was just an amazing project because that's dealing with real life worldwide conservation of animals. And to see the impact that they are making and the ability that ai, if we would've thought AI would help them regenerate what would extinct birds and slowly build back populations or keep control and help the growth of populations that are slowly dying out. But by having the ability to analyze data and see trends in data that was just not possible before, they can run through so many scenarios that allows them to create, uh, conservation plans that we would never have been able to do before.

So really, Steven, you know, this has been a and eye opener for me, but really a great eye opener because for once in my life, this isn't about, you know, making a company make more profit or run that bit faster or be more productive, which is all excellent and very important. This is actually, the outcome is something that makes you smile often. I, I couldn't agree more, mark.

I mean, to the ability to, you know, save the hedgehog as we've talked about before, right? Or look at the, the ecosystem than the patterns of that, you know, adorable little animal, right? Is just, is something amazing and, and, uh, you're giving those researchers such more of an, an advantage to do so.

Right. So we were talking about, um, the, the London Zoo Project. Uh, you guys were utilizing, I think I'm forgetting the exact server and I apologize.

Um, uh, but, uh, we populated that server with a bunch of 1 22 terabyte sds. Um, and, and tell us a little bit about what did that do for that particular researcher? What was, what was interesting on this, which is, I mean, this is something again, new.

If you look at traditional IT, people have data centers. They have, they, they've got nice cooling power and it back and everything you need. London Sioux had an old office that they put it back in and it was an Nvidia, uh, maybe two Nvidia as, as I'm trying to remember now, um, DGXH one hundreds.

And they pretty much stopped every bit of electric power that, that that office could deliver because it isn't a data sensor. Um, but they needed immense amount. I mean, if you imagine that they're doing worldwide projects of animal traps, footage of every tiger and every lion and everything, every hoho that's, you know, out there, the amount, the immense amount of data that they have.

So they needed to get petabytes of data, but they had no power. So without solid iron's actual big or high capacity drives, we were not able, we just could not make this work. And there's, there's a sort of a bit of a funny story on this one, because what we don't realize is, you know, with power comes cooling, well with power comes here, which means cooler.

And in their case, they actually had to build like a refrigerator on the outside of this office block, which was right next to the water buffalos. I think they were the Chinese water buffalos. And, and they actually, the noise that this made, they actually had to relocate the actual Chinese water buffer.

Those is, uh, while they actually installed all this. So the implications that this has on normal people that are using, uh, normal offices to do really remarkable things, actually just taking a lot of technology, and you can't just go in there with the age old, would just put in a lot of storage in, you know, we had to get that, the immense amount of petabytes in about four u uh, it, and deliver tremendous amount of performance so that they could train these models and learn from these. And on the serious side on that, you know, we, they're only just beginning to realize what they can do with it.

Because prior to this, one of the things we do, if we take, if you think about a, you know, a cam trap that you, you often see on tv, it takes photos of animal, anything passing. They, they generally run this through a, an application first that gets rid, you know, removes anything human-like, or a picnic table, a car, a human, you know, a ball or whatever, so that you end up with an image that's hopefully got an animal on there. But prior to this box, they could do something like three a minute, I think it was, that's, that's what their, their server would do after this storage and the solidarity drives, et cetera.

They, they could do something like over a thousand a minute. So now what they can process now, they've suddenly got those images, now they're beginning to learn what they can do. And just as an example, and I, I know we were talking earlier is, I know, you know, the hedgehog is not a, um, you know, it's not a native in America, but in, in the UK we grew up with hedgehogs.

There were, everybody had one in the back garden, just trans ing around. Now you rarely see them. And if you think about it, you know, we've urbanized everything.

We've got roads everywhere. Nobody's got much grass in the gardens nowadays. 'cause they've got parking, hedgehogs park get across a bypass to get to another hedgehog.

So they're interbreeding, they're not mixing, the colonies are getting smaller. And so they're beginning to use things like AI to actually be able, so when, when they get permission from new developments, use AI to develop pathways for hedgehogs to be able to mingle as they always did, do, yet still allowing us to urbanize and to move forward. Now, you take that to, you know, India and the tigers over in India, they, they know every single tiger by its pattern.

So they, they can recognize that within a millisecond, no matter if that, you know, unfortunately ends up in a rug somewhere, which hopefully it never does. They know exactly where it came from. And so what that will end up doing, which is fundamentally a small map in an office, is changing, you know, wildlife around the world.

And we're still learning it. You know, we're still seeing what their next challenge is. Now they can do all these images, what did they do next?

And how did they deal with these? So, you know, they, they learn worldwide and they're opening that service over to, you know, um, you know, conservation experts around the world. It's quite amazing to be involved in and to see the results is something so small, but yet so big in its impact.

When you mentioned, uh, the hedgehogs, uh, and you said they're using ai, I pictured hedgehogs using ai. Yeah. Jack away there, But, you know, but to be honest, um, you know, not, it shouldn't just be researchers in the ivory tower using ai.

Mm-hmm. Now, it probably shouldn't be hedgehogs, but it should be everyone doing tasks that should be able to use this. Yeah.

One of the exciting things that I'm seeing in the AI space is an explosion of, um, I guess you could call it open source. They call it open source. Mm-hmm.

In many cases, even though maybe it's a different type of thing, but just open science, open development, open applications, uh, catalogs full of, of models, again, sort of refuting the, the C cha, the, the, the, the, the challenge that's thrown at AI sometimes that it's just a bunch of chatbots and they're just getting better and better, and all you're trying to do is burn down the rainforest and, and, and make a, a supermind. It's not like that at all. These researchers can go and they can benefit from each other's work.

They can go to a conference and learn about how an image recognition, um, model is able to recognize tigers by their stripes. And somebody else in some other place could say, well, I'm looking at sharks and they have distinctive patterns as well. I wonder if we could use this same visual model.

And similarly on the, the, you know, the hardware and software side, people saying, you know, how can we leverage this technology in another area? That's what makes this whole edge space so interesting too, is because edge is fundamentally a world of constraints. It's, it's, it's not unconstrained data center where you have, you know, an acres and acres of, of, of ultra high performance servers.

No, this is, as you said, the building next to the water buffaloes. And, and we've gotta figure out how we can deploy, um, servers in there that can, that can do the task that we need in this location without just destroying everything. Exactly.

And, and, and in a way I find that positive because when people are faced with constraints, they tend to come up with novel and interesting solutions. When they're faced with no constraints, they tend to just burn everything. Right?

Yeah. You tend to just like, like turn it all the way, turn it to 11. Yeah.

If it only goes to one, you have to figure out how to, how to, how to make it work. And is that your experience kind of trying to deliver these solutions in those environments? Yeah.

And those edge environments, Um, becoming more so, um, and again, one of the products, um, that we've been working with solid on, uh, over the last, possibly the last year or so is, uh, when we started PKL, we realized that what we needed to do was deliver a six, six times the performance in a sixth space with a sixth of the, the power consumption. So, uh, we, we, we did that and we did that because that's what the labs, and that's what the people and the users demanded that it wasn't because they were, you know, they, they didn't wanna spend the extra money or, which often helps, but it was because they simply couldn't power it. And you know, if you look at the way GPUs are going now, you know, a new GP PU server probably takes I know 14 kilowatts.

That's, that's a tremendous amount of power. And I, and I, I think I saw a statement that's longer ago by Jensen saying that, and he can see a time when every data center has a mini nuclear system, uh, nuclear power plant. I mean, that's scary, right?

And one of the things we've been starting, uh, to work on, likely just about two an ounce is, is, uh, an extension of something we call Apex Drive, which will actually enablers to save 50% power on the MVME drives themselves. So that's, if you're talking about an edge that's not so significant, but if you start talking about a lot of the GPUs, the service providers that are those that are stimulating a lot of the, you know, inception, the new starts, the incubation, they've got thousands and millions of these drives. And if you can, if you can save 12 watts a drive, that's a significant amount of power cooling carbon.

Um, and so it is, you know, it's almost the opposite of what we've always done, which is we've always had space and move. And so, you know, when you want to go bigger and faster, you just add another thing in, just add another widget and it goes faster and everybody's happy. Take away that space, take away that power, and, you know, only allow you to turn up to number one, you've gotta innovate.

You've gotta say, actually, how do I get where they need it to be? But I can't turn this up to two. Uh, do you know, I've gotta stay at one.

So something has to get better. And that in many ways is, is being the, the interesting part of our journey is although we've had, you know, software technology that does similar things for the last 20 years, plus we've had to scrap many of it, which is disappointing thing. You wrote it, but you, you know, you know, it's actually because that would've took us to seven, not one.

And you know, now when you've gotta get one, you've gotta get closer. Uh, now the advantage is you've got superstars like Soine who are doing most of the work for you. And I know most of the storage community will probably hate me for this, but storage is, you know, me included.

We've lived on smoke and mirrors for, for the last few decades. We, you know, we've generally lived on, somehow we do witchcraft. We convert these drives that you don't wanna know anything about, and they're not really intelligent and we make them work for you magically.

But the reality is, is most of the work is, you know, over the last decade has been on that NVME side, you know, soine have done the work, oh, we really need to do, we don't need witchcraft, we've just gotta make them work for a user. So we've got the advantage that I think also related to your open source, uh, uh, analogy, it's in many ways the, in many ways, it's the same with the hardware. If, if we truly don't hide and try and disguise everybody's contribution, then collaboratively we can all create a better solution.

If we acknowledge the, the, the, the advanced nature of solid island and use it what it is and don't send anything other, then we make a better product. When we start adding smoking mirrors and, you know, coming up with cool names and every other way that we can think of marketing it, we just create confusion and proprietary solutions that are taken us away from what, what, what the new world needs. Now, the enterprise space isn't going, that's, that's the h HPC space isn't going, but AI and GPU workloads, that, that's a completely new market and probably the most, the largest technological shift that I've seen since the day of the personal computer.

I can remember sitting, looking at a personal computer thinking, what would anyone want one of these on their desk for until I saw, you know, word perfect or whatever it was in them days. And you know, now it's the same with ai. It's the most significant shift I've ever seen.

And it's time for vendors to stop, uh, trying to do it alone and trying to create proprietary solutions and their own standard and only their value. You know, it's about collaboration now and for the, the better good of, of a movement. Wow.

Um, that is a, a, a great way to end the discussion. I think, I think we should stop there. That was amazing.

A lot of good detail. Uh, thank you so much for the examples. We are, uh, you know, we are delighted to have you here today and to have gone through the level of detail, uh, with Peak is, is eyeopening and I'm a big believer in your organization.

Um, and after talking with multiple customers, I'm excited to see, uh, where Peak will go into the future. But, um, why don't we tell the audience though, where, where can folks learn more about your organization? Uh, uh, for those of you that I maybe just seen, we've been at GTC, uh, over with Western Digital and, and sales.

com and I'm over on LinkedIn. We a name like Kki, you can find me. And, and I think, which is what is pretty obvious is, you know, even though I'm the founder, I'm still passionate about what we are learning every day.

So, you know, if you are a user and you really wanna get in touch and you wanna influence what we are designing, reach out to me. That's, that's great. Thank you so much, mark.

And, um, thank you also, uh, Janice for being part of this conversation. And everyone else, thank you for listening, uh, to this episode of the Utilizing Tech podcast. You'll find this podcast in your favorite podcast applications as well as on YouTube.

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