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Episode Description
In this episode of On Record, hosts Mike Anzalone and Carmen Haak speak with Barry Owen, Chief Solutions Architect at Wowza, the streaming engine technology that powers the video component of the VALT platform.
Barry took an engineering path from the start, working as a programmer on image processing products before video work drew his interest. He knew Wowza's founders from his previous work at Adobe, and the timing lined up for him to join the company, where he began in engineering. He has since moved into his current role as Chief Solutions Architect, an outward-facing position focused on understanding customer needs and determining how video can address them.
The episode opens with Barry's take on where the streaming industry stands today. Getting live video out at scale, securely and reliably, is now a largely solved problem, and the newer challenge is drawing real-time insight out of video that would otherwise sit unwatched in storage. He points to Wowza's recent AI product launch and the beta program that preceded it, including a use case from an offshore monitoring customer that inspects oil rigs and other harsh-environment infrastructure. AI models trained on remote camera feeds can flag events such as bubbles escaping from a pipe or a pressure gauge reading that is too high, generating a real-time alert instead of requiring a person to watch video around the clock. Barry notes that the same approach generalizes to clinical simulation settings, where a model could flag whether a patient is out of an expected position at the wrong time.
Barry Owen can be reached on LinkedIn or through his posts on the Wowza blog.
"There's data buried in that video, and you're really looking to make the most out of your infrastructure and your video investments. — Barry Owen"
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00:00:02:00 - 00:00:29:06
Unknown
Hello and welcome to the On Record podcast. This is a podcast where we have the opportunity of speaking with subject matter experts that are using video and audio and innovative ways to support their organizations objectives. In today's episode, we're taking a unique turn and we're going to explore some of the underlying technology that supports the VALT video platform.
00:00:29:08 - 00:01:01:05
Unknown
My name is Mike Anzalone. My co-host is Carmen Hawk, and today our guest is Barry. Owen Barry is the chief solution architect with Wowza. Wowza is the streaming engine that really drives the video component of VALT. So welcome to the podcast, Barry. Thanks. Nice to be here. So before we get started, can you tell us a little bit about yourself, how you got started with Wowza and what your overall mission is?
00:01:01:07 - 00:01:30:14
Unknown
Sure. I've always been one of those curious problem solver types of people. So, you know, I went down the engineering route in school and became a programmer out of school and, you know, did all kinds of engineering type things, but was always looking for, you know, new challenges and things that change. And I was I was drawn to video after a roundabout way of, of many other things.
00:01:30:15 - 00:01:50:15
Unknown
I feel like I always had something to do with visuals in my products that worked on a lot of image processing products and things like that. And, you know, as video became more prevalent to just was super interesting to me. And I had known those founders from a past life at Adobe, and the timing just worked out for me to come join Wowza.
00:01:50:15 - 00:02:16:01
Unknown
And at that point, I started and, you know, started in the running engineering. And then I've moved on to several other things, and now I have more of an outward facing role as the chief solutions architect, which is really all about understanding customers needs and how video can help solve those. You know, there's lots of challenges in that.
00:02:16:07 - 00:02:42:06
Unknown
Every everybody's streaming now and streaming is in some, some extents, you know, somewhat mature. Used to be getting live video out at scale and securely. Reliably was a huge challenge. You know, that's reasonably well done at this point. And now we're finding new problems to solve, right? I mean, how can you leverage that video to have real time insights?
00:02:42:06 - 00:03:05:07
Unknown
How can you actually make sure that video is solving a problem and not just wind up being recorded and stashed in a drive somewhere, and some in the cloud, and never to be heard from again. You know, a lot of organizations are sitting on tons of video that, you know, nobody's watching. And that's that's a missed opportunity. I mean, it could be in the live sense.
00:03:05:07 - 00:03:37:09
Unknown
It could be a missed opportunity to detect something that could save somebody's life or, you know, detect something that could make some experience better for the user. So there's a lot of really interesting things happening now, and I think you'll see a lot more uses of artificial intelligence within that stack to kind of help automate and, and solve some of those problems that are traditionally.
00:03:37:10 - 00:03:53:04
Unknown
Some of the ways, some of the ways you could do that was by throwing a lot of people at them. When you mentioned just now all that video content that people are sitting on. It reminds me of the 5th of July after all my fireworks videos. What am I going to do with them the following year or the next year?
00:03:53:04 - 00:04:16:07
Unknown
But also we're seeing a lot of the alignment. You're just describing why you are the chief solution architect for your organization. You are coming up with the solutions and you're being really creative. What is a current challenge? Right now you're speaking about AI. What is a current challenge that your customers bring to you on a broad level or a very niche that you would explore with us that you are helping to solve with AI?
00:04:16:07 - 00:04:44:17
Unknown
I give you an example that was from we just we just launched a lot of our AI this week and we're, you know, we had a beta program leading up to that. We had some really interesting examples and stuff you probably wouldn't think of. Right. So one of our customers is a is a company called oceanic. And what oceanarium does is they do a lot of offshore monitoring of oil rigs and harsh environments and things like that.
00:04:44:18 - 00:05:21:11
Unknown
So areas where it's really not practical to do things like detective, a pipe is leaking or, you know, see if a pressure gauge on some system is too high, or even things like motion on the rig itself. You know, someone's using a crane to tell people to stay away from that area, but some of the cool things they were able to do is they were able to, you know, via a very remote camera under the ocean, you'll see a little tiny stream of bubbles coming out of a pipe and recognize that that's actually bubbles coming out of not something else.
00:05:21:11 - 00:05:51:02
Unknown
And that's applying this whole system to really understand. It's like, hey, this could be an issue. We can actually send that alert off in real time so somebody can determine then somebody. Because again, how many of those things do they actually have to watch? Nobody's watching all that video in real time. It's impossible. So we can trigger an alert and allow people to draw attention to something that needs to be drawn attention to, and they can then pick the appropriate action.
00:05:51:02 - 00:06:13:10
Unknown
And I think that's really interesting use case. And and you can generalize that into, you know, tons of other stuff. Right. I mean, the the VALT platform could apply AI to, you know, detect a clinical study where, I don't know, someone's not in bed when they're supposed to be in bed or someone's not seeing the position they're supposed to be in.
00:06:13:12 - 00:06:43:12
Unknown
There's all kinds of interesting use cases. You can you can apply to this. So, Barry, Wowza. First of all, I think is a really cool name for a company. Can you define what the Wowza Streaming Engine is and how it works? Yeah, I usually I usually tell people what you know, Wilder provides streaming infrastructure for people. So why is that streaming engine is this very sophisticated kind of end to end platform.
00:06:43:12 - 00:07:04:15
Unknown
So we can take video in from a variety of sources, whether it be a camera, a file, a satellite link, whatever it might be. And we can take that video and we can we can process it, we can package it for delivery to any device anywhere in near real time. And then we can also do analysis on it.
00:07:04:15 - 00:07:25:09
Unknown
Along the way we can make sure that video is secure. We can encrypt it if it needs to be encrypted. We can make sure only the people who are supposed to be watching the video are supposed to be watching the video. And they were from, you know, hundreds of inputs to very few people watching or machines watching or single inputs to millions of people watching, right?
00:07:25:10 - 00:07:49:15
Unknown
It scales both ways. And Streaming Engine was built to be this kind of general purpose workhorse. In the middle of all that, it's very API driven, so it's often built into other products like the folks at IVS have used with fault is the engine is built in, you know, it's part of the it's part of the product. It's not necessarily used by itself.
00:07:49:15 - 00:08:16:14
Unknown
It's it's part of the offering. And it's built into the to the product at a very deep level. And one of the questions a lot of our clients ask is how scalable is VALT? And that scalability really is built on in large part the Wowza Streaming Engine. Can you talk a little bit about the topic of scalability and how you make that possible?
00:08:16:16 - 00:09:01:04
Unknown
Sure. A couple different ways. Right. So, so streaming Engine itself is built to take maximum advantage of any underlying hardware that it can. So things like video processing lend well to being processed with GPUs or use really increase the density of the video. So if you have 1000 cameras you need to process, depending on how you want to deploy your architecture, you can take advantage of those to make highly efficient workflows and highly efficient hardware setups without having to have tens or hundreds of servers necessarily running.
00:09:01:06 - 00:09:21:15
Unknown
I often tell you it's also a deployment flexibility, right? So streaming engine can run on prem. It can run on the alleged devices, it can run in the cloud and a combination of all of them. So I often tell people it's like, you know, what's your most important aspect? Are you looking to scale up and down a lot?
00:09:21:16 - 00:09:41:01
Unknown
You know, so we have customers who maybe have 100 streams going all the time, but maybe on the weekends they might go to a thousand streams. So I would probably architect their solution a little differently so it can scale up and down very quickly and efficiently versus someone that maybe they have ten streams that are going out to lots and lots of viewers.
00:09:41:04 - 00:10:01:19
Unknown
Maybe they need some very fixed infrastructure. So it's really dynamic and it's really dependent on customer use cases, which is, you know, honestly, kind of one of the fun parts of my job is I get to see all kinds of different uses of this. And Huffman often used in ways you wouldn't expect. What are some you mentioned one use case.
00:10:01:20 - 00:10:29:17
Unknown
What are some of the more interesting use cases that you've come across? Oh, tons. So we stream live video from Formula One cars to the pits, both with telemetry and track data and things like that. We have streamed live video from the moon, so being part of the NASA project to stream live video from Artemis was an amazing experience.
00:10:29:17 - 00:10:59:21
Unknown
So, you know, being part of things like that are great. And then, you know, products like products like yours, products like, you know, we have customers who are doing E911 services so that operators can, you know, get real time video information from people who are calling, I want. So it really does range all over the place. And, you know, there's always something very interesting.
00:11:00:01 - 00:11:30:08
Unknown
You've mentioned some savings that was related to the oil rig that's monitoring underwater. We've also talked about like the scalability of the solution and the stability that's required of a solution that's going to be supporting moon live capture formula one to pit capture. What are some other types of problem solving that your company is doing? We see a lot of overlap with those sentences I just uttered the reliability scalability cost effectiveness in our solution.
00:11:30:09 - 00:12:01:20
Unknown
Just curious if there's anything else from a from a presentation perspective that's helpful for prospects to know that your team does exceptionally well. Well. So with with the new tooling and all the AI capabilities, what we're really talking about is learning to treat video as a data source, right? So how can we treat video as data and really make that data in that video as effective as possible for actions.
00:12:01:21 - 00:12:27:12
Unknown
So making video on actionable resource where perhaps it was more of a passive or non-real time before. I would say our team has been really, you know, on the engineering and products that we've been really leaning into that to look at really interesting use cases for that. On our implementation and support side, it's it's really that feedback back from the customers.
00:12:27:15 - 00:12:50:20
Unknown
How can these little things we're doing add up into big changes for, for a customer's outcome. And I think, you know, the overarching scheme of that is, is really how can we how can we turn this into a first class data source where.
00:12:51:00 - 00:13:33:21
Unknown
Perhaps in the past it was, you know, it was just missing that component? It's interesting. We you know, we spent a lot of time in the healthcare market working with like, nursing schools, medical schools where they're doing simulation. And one of the big drivers in that marketplace is collecting video as data to support an evidence based framework. So that evidence being able to really show learner competency in a particular field of discipline.
00:13:34:01 - 00:14:13:08
Unknown
So I'm curious from from an analytics perspective, what how Wowza may maybe looking at that kind of application as well? I think as long as you can put your risk around what you're looking for, right. So what determines that competency and what does that look like in a video scenario? I think then you could probably, you know, we don't pretend to have every model needed for everybody's video.
00:14:13:12 - 00:14:59:02
Unknown
We designed an open platform for people to be able to add models and train models or team models to really meet their precise needs. So I can envision somebody coming up with something that's tuned to those clinical settings, and maybe it's okay if you notice that A happened within 10s, you should be expecting B to happen. And if you don't see B, maybe you do some sort of alert or some sort of a notification, or even something as simple as marking the time in the video, and then you start to build that over time, and you can take that data back in your models and keep training them for even, you know, more input and
00:14:59:02 - 00:15:25:07
Unknown
more, more correctness, if you will. So really, it's going to be about the ability to again see into the video and and notice things like that. So I have a question was related to health care. Just you mentioned a health care use case. As Mike also alluded to health care industry we will serve we have law enforcement research.
00:15:25:09 - 00:15:53:12
Unknown
These are really sensitive spaces. If they're interested in using video AI, what is different about how we serve as partners or how is the service as partners when they're looking to use video AI in those sensitive industries? Yeah, I think there's a couple of things is we're not sending the video off to the cloud to be analyzed. So with Streaming Engine and the wife product, which is the video intelligence framework, it all runs on prem.
00:15:53:14 - 00:16:17:06
Unknown
It can run on the same devices. You're running your videos now. You don't have to go in and rip and replace a bunch of stuff. You're not sending video off to a cloud service where you lose control of it gets parked in a cloud environment somewhere. This can all happen in real time, on the edge or on prem, and streaming engine was meant to be deployed in those kind of fire, right?
00:16:17:07 - 00:16:43:03
Unknown
It gets built in. It can be deployed in a hospital within the hospitals network. It can deployed in, you know, we actually are deployed in police stations and places like that where, you know, not to mention government and other secure installations where, you know, those are private networks. York detect solutions such that you have the resources necessary to handle the scale.
00:16:43:03 - 00:17:11:00
Unknown
They need to handle that. And that could be anywhere from a single small edge machine or a laptop on up to multiple servers. And then, you know, GPUs to handle both the AI and the video processing. So it's it's really nice that you don't have to leverage a cloud service or a SaaS platform to get this. You know, AI is everybody that we're talking to.
00:17:11:01 - 00:17:37:11
Unknown
That's usually their number. One question is how are you using AI? And I'm kind of curious because it's evolving so rapidly for sure. What's Wow's roadmap like a year to even five years down the road and implementing AI in your technology? I think there are a few things, and you're starting to see an inkling of what some of those can be with the product release.
00:17:37:11 - 00:18:01:16
Unknown
So we started with relatively straightforward object detection. So, you know, hey, look at a video. There's cars there. They you can count the cars, whatever it is. Then you move to things like, okay, now I see cars going both directions on a highway. Can I count them now? Can I tell you if the cars are going the right direction or not?
00:18:01:19 - 00:18:28:11
Unknown
And then you're starting to get into even more dynamic things like, okay, now I see a bunch of cars on a highway. Can I predict if something's going to happen, whether it be a traffic jam or an accident or something like that, based on the scenes of data I'm seeing? And then you can you can move into the reason we designed the framework like we did, is because we want to be able to easily add these new models and these new capabilities as they evolve.
00:18:28:16 - 00:18:56:06
Unknown
So one of the models we did work with was a model from Nvidia that is all about synthetic video detection. So what you're starting to see is obviously more and more, more and more AI generated video. And from a perspective of, you know, news, social media, whatever, you might want to be able to detect that that doesn't necessarily mean that synthetic generated videos bad.
00:18:56:09 - 00:19:15:07
Unknown
It's just you want to be able to know if it's synthetic or if it's real and take the appropriate action, you know, social media site may not care. It's like they may just label the day I hate this is, you know, an AI generated video reading something but a news a news, a news source might not want that.
00:19:15:08 - 00:19:40:07
Unknown
They might want to make sure that they see if it's synthetic video, they do something completely different. So things like that will keep evolving. And then you'll be able, as the models get more powerful and you'll be able to do more things across scenes of video. Right? You can already do things like context detection, and that's good for a bunch of different reasons.
00:19:40:07 - 00:20:06:03
Unknown
But what it's what it's starting to be more commonly as far as making sure that an upcoming advertisement in a video stream isn't wildly different than the content before it. You don't want a happy added with a sad, you know you see sad scene in the movie. All of a sudden you see puppies. You know, maybe you do, but but you know, you at least want to know.
00:20:06:05 - 00:20:31:18
Unknown
So things like that, you just start to see this go a lot of different directions. But I think some of the early wins are certainly the kind of chaining of events together to determine outcomes or if an outcome may happen and some predictions. And also, you know, the there's huge problems with content moderation across user generated content services and social media.
00:20:31:19 - 00:20:58:15
Unknown
So better better applying AI to that. So you don't have to just solve the problem with teams of people because to be honest, nobody really wants to watch that video in the first place. And so just teams of teams, of people, you're not only subjecting them to having to watch that video, but they they miss stuff. I like everything you just mentioned from a ethical perspective.
00:20:58:15 - 00:21:37:09
Unknown
It sounds very wherever you sit on the spectrum of your AI approval, everything that you just mentioned is all with good intent and to do good. So. We hope. Right? Yeah. Hey, Barry, what makes the Wowza Streaming engine unique? I think. Well, so streaming Engine is is kind of the OG of streaming engines, right? We launched our first product in 2007, and from the ground up it was built to be customized and added to by users.
00:21:37:10 - 00:22:01:07
Unknown
Right. So it's got a full API for you to not only for command and control, but also for extensibility. So earlier this year, as an example, I decided I wanted to learn how to use cloud to write code. I wrote code all my life, but I haven't written a lot of code in the last ten years. I was like, well, I'll see what cloud can help me do.
00:22:01:09 - 00:22:31:10
Unknown
And I was able to add a new streaming protocol to streaming engine over the course of really just a couple of weeks using engine APIs only. I didn't have to touch any engine code, but I was able to add the protocols called mock, and it's kind of an up and coming streaming protocol for lowly C, potentially to replace WebRTC, but potential for replace a bunch bunch of other protocols.
00:22:31:12 - 00:23:01:18
Unknown
But the fact that I was able to do that without having to change any code in engine itself, gives you a glimpse into the other how powerful those APIs are, that you could truly integrate engine very deeply into whatever workflow you need to to integrate it into, without having to worry about backward compatibility or anything changing. And you can control that code moving forward.
00:23:01:20 - 00:23:39:03
Unknown
You mentioned WebRTC, which kind of piqued my curiosity. How is MOC different than WebRTC? I don't have enough time for this. We can use code to summarize, MOC is a new streaming protocol, kind of designed from the ground up to take to take advantage of some newer protocols like what's called quick, which is part of TTP three, which is basically a UDP based protocol that can be used in the browser.
00:23:39:05 - 00:24:07:15
Unknown
And there's significant advantages to that for streaming. TCP has a lot of overhead, and it builds latency when there's when there's packet loss because it does retransmission. And you know, traditional streaming right now we have you know, we have HLS and Dash, which are the most common things for one to many streaming. Right. You're watching Netflix if you're watching Apple TV, whatever it is, it's going to be HLS or Dash.
00:24:07:17 - 00:24:37:00
Unknown
WebRTC was originally designed for video conferencing. Still does a pretty good job of that. It's not as great at one to many. It has problems in other areas. It wasn't really designed for things like accessibility, and it wasn't designed for things like DRM. So basically what media or quick is, is attempting to do is take a new look at streaming based on the technology we know now, rather than evolved over the last 10 or 15 years.
00:24:37:03 - 00:24:57:07
Unknown
It's like, how would we do this if we were starting over and kind of trying to be the best of all worlds, and it's got a lot of cool features. You can do super low latency with it. You know, it's playable in browsers, it's you can broadcast from our browser, but you can also broadcast from a camera. It scales really well.
00:24:57:08 - 00:25:23:05
Unknown
You can kind of tune the latency for your size. It's designed to be deployed with what we call relays, which you send a stream to a relay. That relay can then same streams to people or other relays. So you get this kind of treed fan out effect. So there's a lot of promise there. It's getting close. You can certainly go read lots about it.
00:25:23:06 - 00:26:01:03
Unknown
Open mock is an industry group that's kind of deploying versions of it as the spec evolves, but I think it's going to be a really interesting, interesting alternative to some of the other protocols over the next couple of years. What kind of guidance and recommendations can you offer organizations that may either be looking to upgrade some of their video streaming capabilities, or just getting started out in terms of yep, we recognize a need to stream video.
00:26:01:05 - 00:26:28:14
Unknown
It could be a nursing simulation that wants a stream from a live simulation to a classroom of students. For example. Whether that classroom of students is across the hall or across the country. And they're kind of getting started like, well, you know, the big question could be where and how do we get started? Hey, my first advice would, you know, start small, right.
00:26:28:16 - 00:26:59:01
Unknown
So pick one clear use case, one workflow, one problem that you're trying to solve and get it working end to end and get it working. Well, we'll take your case. I have I have one class I'm trying to stream from one place to another. Get that working, get the recording, working, getting our activity elements working. Get that dialed in and then you'll you'll understand what you need to do that at scale.
00:26:59:03 - 00:27:27:08
Unknown
So, you know, I would say don't try to jump in and do 200 classes next week to, you know, 200 different sites, get it working. And then many times you have the infrastructure already, you have cameras, you may have servers. You don't necessarily need to go out and buy a whole broadcast studios worth of infrastructure to get started.
00:27:27:13 - 00:27:52:11
Unknown
You know, start with what you have because, you know, it may be more than good enough to get you there. And then, you know, find people to help you, right? I mean, whether it be vendors or whether it be communities, but there's a lot of there's a lot of places to get advice on, you know, representative best practices and how to do things.
00:27:52:12 - 00:28:11:01
Unknown
I'm not I'm not going to say the right way to do things, because that's different for every implementation and different for every person's use case. But there's a lot of, yeah, there's a lot of resources out there for you to take advantage of. And that's where I think, you know, while this certainly is is great, have a great support team.
00:28:11:01 - 00:28:34:13
Unknown
We have a great services team. They understand not only Wilder's products, but they understand a lot of other products and how things all work together. And I think that's important to so you mentioned infrastructure. I'm kind of curious what kind of infrastructure requirements are you looking for in order to kind of deploy your solutions? So we have a ton of flexibility in that regard.
00:28:34:15 - 00:28:59:18
Unknown
You know, as I mentioned, we can deploy on tiny little edge servers, you know, not much bigger than a Raspberry Pi, to be honest. They're very, very small little devices. And if you only have a camera or two, you're looking to get repackaged and streamed out for mobile consumption or whatever it is that may be an eye. And if you have a remote setting where you're trying to do something, maybe plunking one of those down in that remote setting is going to be good.
00:29:00:01 - 00:29:25:04
Unknown
And you know, you have options as far as what your input source might be. You could you could be streaming from a phone into that box. You could be streaming from a, you know, a dedicated camera or even a professional camera. And, you know, you can start there. And, you know, typically people are these days is almost most streams are going to be viewed on a mobile device.
00:29:25:06 - 00:29:49:03
Unknown
So optimize your stream. So it works well on a mobile device. It works well under all different bandwidth conditions, you know. And streaming engine is great. And creating sets of outputs that look good on mobile devices under varying bandwidth conditions and varying screen sizes and things like that. I like that advice you gave, and I'm curious what type of internal organizational context do you work with?
00:29:49:05 - 00:30:34:12
Unknown
Do you work with a lot of end users, or do you work with the network team? Or who are you really collaborating with from the ground up? So it's it's kind of a combination, right? So I will typically talk to the business owners of the problem to understand what it is they're trying to accomplish. But then when it comes to architecture and implementation, it's it's someone in the technical team, whether whether they have a video engineering group or whether it's the IT team or, or a combination of of the two will work with them to understand their infrastructure constraints or opportunities on the deployment side and how to set up a solution.
00:30:34:12 - 00:31:14:03
Unknown
And that's where we often also will say, you know, bring in other vendors. You know, you may need a delivery provider to deliver things at scale. You may need you may need a cloud provider at some point to step in to do massive storage or something. So understanding where things are and what things, what needs to happen, well, some influence the kinds of partners you might need to bring into the solution, and you help with that overall design so you can help make referrals or you can help guide on we we have tons of partners in the industry, not only from like a systems integration perspective, but vendors like, if we know we need a
00:31:14:03 - 00:31:37:01
Unknown
deer in vendor, we can bring in a vendor we've worked with in the past that we know is going to work encoders, cameras, you know, CMS. So we definitely have the whole aspect of of the ecosystem covered as far as how we attack problems.
00:31:37:03 - 00:31:48:17
Unknown
So bear before we wrap up, is there anything else that you'd like to share with our audience or questions that we should have asked that we missed?
00:31:48:18 - 00:31:53:07
Unknown
Let's see.
00:31:53:09 - 00:31:57:09
Unknown
I think.
00:31:57:11 - 00:32:13:18
Unknown
Let me look through my notes. I think, I mean, we we covered a wide range of ground. And I think it's interesting and I think, you know what I would what I would, you know, say if you're streaming now.
00:32:13:20 - 00:32:45:08
Unknown
Take a look how you're actually using that video. And if some of the new things coming out in terms of, of AI and video intelligence might help you dance, maybe not right now, and that's fine. But, you know, I think what you'll find is that there's data buried in that video, and you're really looking to make the most out of your infrastructure and your video investments.
00:32:45:09 - 00:33:00:12
Unknown
Building that case for, hey, we've done great with video now, but look at all this stuff that's actually opening up. And we can we can look at and do some experiments. Right? I mean, it's whatever it might be.
00:33:00:13 - 00:33:35:09
Unknown
That's awesome advice. How can our listeners get in touch with you if they they want to have a deeper conversation? Well, they can certainly find me on LinkedIn. Pretty easy to find there. It's Barry J. Own whatever, the whole LinkedIn thing. Barry Jones, you can find me on Wasa. I write a lot of blog posts there. And, you know, occasionally you will see me speaking at events or at conferences, certainly at trade shows.
00:33:35:11 - 00:33:58:00
Unknown
But I'd love to hear from people happy to connect on LinkedIn and talk more. And you know, I'm not going to come at you with a hard sell. I you know, you must buy Wowza. Because of this. I really want to come at you more of a problem are you trying to solve and what's the right way to go about it and how you know, how are you going to get the most out of what you're doing?
00:33:58:01 - 00:34:14:11
Unknown
What a great resource. Yeah, well, thanks for your time, Barry. We really appreciate it. I think our listeners of We'll learn a lot from our podcast. Well, I hope so. It's fun. Appreciate the time. Thank you.
00:34:14:13 - 00:34:29:02
Unknown
On Record is a podcast presented by Intelligent Video Solutions, hosted by Mike Anzalone and Carmen Haak and produced by Kyle Shelstad. Find us online at IPIVS.com/onrecord and on social media at Intelligent Video Solutions.
Unknown
Hello and welcome to the On Record podcast. This is a podcast where we have the opportunity of speaking with subject matter experts that are using video and audio and innovative ways to support their organizations objectives. In today's episode, we're taking a unique turn and we're going to explore some of the underlying technology that supports the VALT video platform.
00:00:29:08 - 00:01:01:05
Unknown
My name is Mike Anzalone. My co-host is Carmen Hawk, and today our guest is Barry. Owen Barry is the chief solution architect with Wowza. Wowza is the streaming engine that really drives the video component of VALT. So welcome to the podcast, Barry. Thanks. Nice to be here. So before we get started, can you tell us a little bit about yourself, how you got started with Wowza and what your overall mission is?
00:01:01:07 - 00:01:30:14
Unknown
Sure. I've always been one of those curious problem solver types of people. So, you know, I went down the engineering route in school and became a programmer out of school and, you know, did all kinds of engineering type things, but was always looking for, you know, new challenges and things that change. And I was I was drawn to video after a roundabout way of, of many other things.
00:01:30:15 - 00:01:50:15
Unknown
I feel like I always had something to do with visuals in my products that worked on a lot of image processing products and things like that. And, you know, as video became more prevalent to just was super interesting to me. And I had known those founders from a past life at Adobe, and the timing just worked out for me to come join Wowza.
00:01:50:15 - 00:02:16:01
Unknown
And at that point, I started and, you know, started in the running engineering. And then I've moved on to several other things, and now I have more of an outward facing role as the chief solutions architect, which is really all about understanding customers needs and how video can help solve those. You know, there's lots of challenges in that.
00:02:16:07 - 00:02:42:06
Unknown
Every everybody's streaming now and streaming is in some, some extents, you know, somewhat mature. Used to be getting live video out at scale and securely. Reliably was a huge challenge. You know, that's reasonably well done at this point. And now we're finding new problems to solve, right? I mean, how can you leverage that video to have real time insights?
00:02:42:06 - 00:03:05:07
Unknown
How can you actually make sure that video is solving a problem and not just wind up being recorded and stashed in a drive somewhere, and some in the cloud, and never to be heard from again. You know, a lot of organizations are sitting on tons of video that, you know, nobody's watching. And that's that's a missed opportunity. I mean, it could be in the live sense.
00:03:05:07 - 00:03:37:09
Unknown
It could be a missed opportunity to detect something that could save somebody's life or, you know, detect something that could make some experience better for the user. So there's a lot of really interesting things happening now, and I think you'll see a lot more uses of artificial intelligence within that stack to kind of help automate and, and solve some of those problems that are traditionally.
00:03:37:10 - 00:03:53:04
Unknown
Some of the ways, some of the ways you could do that was by throwing a lot of people at them. When you mentioned just now all that video content that people are sitting on. It reminds me of the 5th of July after all my fireworks videos. What am I going to do with them the following year or the next year?
00:03:53:04 - 00:04:16:07
Unknown
But also we're seeing a lot of the alignment. You're just describing why you are the chief solution architect for your organization. You are coming up with the solutions and you're being really creative. What is a current challenge? Right now you're speaking about AI. What is a current challenge that your customers bring to you on a broad level or a very niche that you would explore with us that you are helping to solve with AI?
00:04:16:07 - 00:04:44:17
Unknown
I give you an example that was from we just we just launched a lot of our AI this week and we're, you know, we had a beta program leading up to that. We had some really interesting examples and stuff you probably wouldn't think of. Right. So one of our customers is a is a company called oceanic. And what oceanarium does is they do a lot of offshore monitoring of oil rigs and harsh environments and things like that.
00:04:44:18 - 00:05:21:11
Unknown
So areas where it's really not practical to do things like detective, a pipe is leaking or, you know, see if a pressure gauge on some system is too high, or even things like motion on the rig itself. You know, someone's using a crane to tell people to stay away from that area, but some of the cool things they were able to do is they were able to, you know, via a very remote camera under the ocean, you'll see a little tiny stream of bubbles coming out of a pipe and recognize that that's actually bubbles coming out of not something else.
00:05:21:11 - 00:05:51:02
Unknown
And that's applying this whole system to really understand. It's like, hey, this could be an issue. We can actually send that alert off in real time so somebody can determine then somebody. Because again, how many of those things do they actually have to watch? Nobody's watching all that video in real time. It's impossible. So we can trigger an alert and allow people to draw attention to something that needs to be drawn attention to, and they can then pick the appropriate action.
00:05:51:02 - 00:06:13:10
Unknown
And I think that's really interesting use case. And and you can generalize that into, you know, tons of other stuff. Right. I mean, the the VALT platform could apply AI to, you know, detect a clinical study where, I don't know, someone's not in bed when they're supposed to be in bed or someone's not seeing the position they're supposed to be in.
00:06:13:12 - 00:06:43:12
Unknown
There's all kinds of interesting use cases. You can you can apply to this. So, Barry, Wowza. First of all, I think is a really cool name for a company. Can you define what the Wowza Streaming Engine is and how it works? Yeah, I usually I usually tell people what you know, Wilder provides streaming infrastructure for people. So why is that streaming engine is this very sophisticated kind of end to end platform.
00:06:43:12 - 00:07:04:15
Unknown
So we can take video in from a variety of sources, whether it be a camera, a file, a satellite link, whatever it might be. And we can take that video and we can we can process it, we can package it for delivery to any device anywhere in near real time. And then we can also do analysis on it.
00:07:04:15 - 00:07:25:09
Unknown
Along the way we can make sure that video is secure. We can encrypt it if it needs to be encrypted. We can make sure only the people who are supposed to be watching the video are supposed to be watching the video. And they were from, you know, hundreds of inputs to very few people watching or machines watching or single inputs to millions of people watching, right?
00:07:25:10 - 00:07:49:15
Unknown
It scales both ways. And Streaming Engine was built to be this kind of general purpose workhorse. In the middle of all that, it's very API driven, so it's often built into other products like the folks at IVS have used with fault is the engine is built in, you know, it's part of the it's part of the product. It's not necessarily used by itself.
00:07:49:15 - 00:08:16:14
Unknown
It's it's part of the offering. And it's built into the to the product at a very deep level. And one of the questions a lot of our clients ask is how scalable is VALT? And that scalability really is built on in large part the Wowza Streaming Engine. Can you talk a little bit about the topic of scalability and how you make that possible?
00:08:16:16 - 00:09:01:04
Unknown
Sure. A couple different ways. Right. So, so streaming Engine itself is built to take maximum advantage of any underlying hardware that it can. So things like video processing lend well to being processed with GPUs or use really increase the density of the video. So if you have 1000 cameras you need to process, depending on how you want to deploy your architecture, you can take advantage of those to make highly efficient workflows and highly efficient hardware setups without having to have tens or hundreds of servers necessarily running.
00:09:01:06 - 00:09:21:15
Unknown
I often tell you it's also a deployment flexibility, right? So streaming engine can run on prem. It can run on the alleged devices, it can run in the cloud and a combination of all of them. So I often tell people it's like, you know, what's your most important aspect? Are you looking to scale up and down a lot?
00:09:21:16 - 00:09:41:01
Unknown
You know, so we have customers who maybe have 100 streams going all the time, but maybe on the weekends they might go to a thousand streams. So I would probably architect their solution a little differently so it can scale up and down very quickly and efficiently versus someone that maybe they have ten streams that are going out to lots and lots of viewers.
00:09:41:04 - 00:10:01:19
Unknown
Maybe they need some very fixed infrastructure. So it's really dynamic and it's really dependent on customer use cases, which is, you know, honestly, kind of one of the fun parts of my job is I get to see all kinds of different uses of this. And Huffman often used in ways you wouldn't expect. What are some you mentioned one use case.
00:10:01:20 - 00:10:29:17
Unknown
What are some of the more interesting use cases that you've come across? Oh, tons. So we stream live video from Formula One cars to the pits, both with telemetry and track data and things like that. We have streamed live video from the moon, so being part of the NASA project to stream live video from Artemis was an amazing experience.
00:10:29:17 - 00:10:59:21
Unknown
So, you know, being part of things like that are great. And then, you know, products like products like yours, products like, you know, we have customers who are doing E911 services so that operators can, you know, get real time video information from people who are calling, I want. So it really does range all over the place. And, you know, there's always something very interesting.
00:11:00:01 - 00:11:30:08
Unknown
You've mentioned some savings that was related to the oil rig that's monitoring underwater. We've also talked about like the scalability of the solution and the stability that's required of a solution that's going to be supporting moon live capture formula one to pit capture. What are some other types of problem solving that your company is doing? We see a lot of overlap with those sentences I just uttered the reliability scalability cost effectiveness in our solution.
00:11:30:09 - 00:12:01:20
Unknown
Just curious if there's anything else from a from a presentation perspective that's helpful for prospects to know that your team does exceptionally well. Well. So with with the new tooling and all the AI capabilities, what we're really talking about is learning to treat video as a data source, right? So how can we treat video as data and really make that data in that video as effective as possible for actions.
00:12:01:21 - 00:12:27:12
Unknown
So making video on actionable resource where perhaps it was more of a passive or non-real time before. I would say our team has been really, you know, on the engineering and products that we've been really leaning into that to look at really interesting use cases for that. On our implementation and support side, it's it's really that feedback back from the customers.
00:12:27:15 - 00:12:50:20
Unknown
How can these little things we're doing add up into big changes for, for a customer's outcome. And I think, you know, the overarching scheme of that is, is really how can we how can we turn this into a first class data source where.
00:12:51:00 - 00:13:33:21
Unknown
Perhaps in the past it was, you know, it was just missing that component? It's interesting. We you know, we spent a lot of time in the healthcare market working with like, nursing schools, medical schools where they're doing simulation. And one of the big drivers in that marketplace is collecting video as data to support an evidence based framework. So that evidence being able to really show learner competency in a particular field of discipline.
00:13:34:01 - 00:14:13:08
Unknown
So I'm curious from from an analytics perspective, what how Wowza may maybe looking at that kind of application as well? I think as long as you can put your risk around what you're looking for, right. So what determines that competency and what does that look like in a video scenario? I think then you could probably, you know, we don't pretend to have every model needed for everybody's video.
00:14:13:12 - 00:14:59:02
Unknown
We designed an open platform for people to be able to add models and train models or team models to really meet their precise needs. So I can envision somebody coming up with something that's tuned to those clinical settings, and maybe it's okay if you notice that A happened within 10s, you should be expecting B to happen. And if you don't see B, maybe you do some sort of alert or some sort of a notification, or even something as simple as marking the time in the video, and then you start to build that over time, and you can take that data back in your models and keep training them for even, you know, more input and
00:14:59:02 - 00:15:25:07
Unknown
more, more correctness, if you will. So really, it's going to be about the ability to again see into the video and and notice things like that. So I have a question was related to health care. Just you mentioned a health care use case. As Mike also alluded to health care industry we will serve we have law enforcement research.
00:15:25:09 - 00:15:53:12
Unknown
These are really sensitive spaces. If they're interested in using video AI, what is different about how we serve as partners or how is the service as partners when they're looking to use video AI in those sensitive industries? Yeah, I think there's a couple of things is we're not sending the video off to the cloud to be analyzed. So with Streaming Engine and the wife product, which is the video intelligence framework, it all runs on prem.
00:15:53:14 - 00:16:17:06
Unknown
It can run on the same devices. You're running your videos now. You don't have to go in and rip and replace a bunch of stuff. You're not sending video off to a cloud service where you lose control of it gets parked in a cloud environment somewhere. This can all happen in real time, on the edge or on prem, and streaming engine was meant to be deployed in those kind of fire, right?
00:16:17:07 - 00:16:43:03
Unknown
It gets built in. It can be deployed in a hospital within the hospitals network. It can deployed in, you know, we actually are deployed in police stations and places like that where, you know, not to mention government and other secure installations where, you know, those are private networks. York detect solutions such that you have the resources necessary to handle the scale.
00:16:43:03 - 00:17:11:00
Unknown
They need to handle that. And that could be anywhere from a single small edge machine or a laptop on up to multiple servers. And then, you know, GPUs to handle both the AI and the video processing. So it's it's really nice that you don't have to leverage a cloud service or a SaaS platform to get this. You know, AI is everybody that we're talking to.
00:17:11:01 - 00:17:37:11
Unknown
That's usually their number. One question is how are you using AI? And I'm kind of curious because it's evolving so rapidly for sure. What's Wow's roadmap like a year to even five years down the road and implementing AI in your technology? I think there are a few things, and you're starting to see an inkling of what some of those can be with the product release.
00:17:37:11 - 00:18:01:16
Unknown
So we started with relatively straightforward object detection. So, you know, hey, look at a video. There's cars there. They you can count the cars, whatever it is. Then you move to things like, okay, now I see cars going both directions on a highway. Can I count them now? Can I tell you if the cars are going the right direction or not?
00:18:01:19 - 00:18:28:11
Unknown
And then you're starting to get into even more dynamic things like, okay, now I see a bunch of cars on a highway. Can I predict if something's going to happen, whether it be a traffic jam or an accident or something like that, based on the scenes of data I'm seeing? And then you can you can move into the reason we designed the framework like we did, is because we want to be able to easily add these new models and these new capabilities as they evolve.
00:18:28:16 - 00:18:56:06
Unknown
So one of the models we did work with was a model from Nvidia that is all about synthetic video detection. So what you're starting to see is obviously more and more, more and more AI generated video. And from a perspective of, you know, news, social media, whatever, you might want to be able to detect that that doesn't necessarily mean that synthetic generated videos bad.
00:18:56:09 - 00:19:15:07
Unknown
It's just you want to be able to know if it's synthetic or if it's real and take the appropriate action, you know, social media site may not care. It's like they may just label the day I hate this is, you know, an AI generated video reading something but a news a news, a news source might not want that.
00:19:15:08 - 00:19:40:07
Unknown
They might want to make sure that they see if it's synthetic video, they do something completely different. So things like that will keep evolving. And then you'll be able, as the models get more powerful and you'll be able to do more things across scenes of video. Right? You can already do things like context detection, and that's good for a bunch of different reasons.
00:19:40:07 - 00:20:06:03
Unknown
But what it's what it's starting to be more commonly as far as making sure that an upcoming advertisement in a video stream isn't wildly different than the content before it. You don't want a happy added with a sad, you know you see sad scene in the movie. All of a sudden you see puppies. You know, maybe you do, but but you know, you at least want to know.
00:20:06:05 - 00:20:31:18
Unknown
So things like that, you just start to see this go a lot of different directions. But I think some of the early wins are certainly the kind of chaining of events together to determine outcomes or if an outcome may happen and some predictions. And also, you know, the there's huge problems with content moderation across user generated content services and social media.
00:20:31:19 - 00:20:58:15
Unknown
So better better applying AI to that. So you don't have to just solve the problem with teams of people because to be honest, nobody really wants to watch that video in the first place. And so just teams of teams, of people, you're not only subjecting them to having to watch that video, but they they miss stuff. I like everything you just mentioned from a ethical perspective.
00:20:58:15 - 00:21:37:09
Unknown
It sounds very wherever you sit on the spectrum of your AI approval, everything that you just mentioned is all with good intent and to do good. So. We hope. Right? Yeah. Hey, Barry, what makes the Wowza Streaming engine unique? I think. Well, so streaming Engine is is kind of the OG of streaming engines, right? We launched our first product in 2007, and from the ground up it was built to be customized and added to by users.
00:21:37:10 - 00:22:01:07
Unknown
Right. So it's got a full API for you to not only for command and control, but also for extensibility. So earlier this year, as an example, I decided I wanted to learn how to use cloud to write code. I wrote code all my life, but I haven't written a lot of code in the last ten years. I was like, well, I'll see what cloud can help me do.
00:22:01:09 - 00:22:31:10
Unknown
And I was able to add a new streaming protocol to streaming engine over the course of really just a couple of weeks using engine APIs only. I didn't have to touch any engine code, but I was able to add the protocols called mock, and it's kind of an up and coming streaming protocol for lowly C, potentially to replace WebRTC, but potential for replace a bunch bunch of other protocols.
00:22:31:12 - 00:23:01:18
Unknown
But the fact that I was able to do that without having to change any code in engine itself, gives you a glimpse into the other how powerful those APIs are, that you could truly integrate engine very deeply into whatever workflow you need to to integrate it into, without having to worry about backward compatibility or anything changing. And you can control that code moving forward.
00:23:01:20 - 00:23:39:03
Unknown
You mentioned WebRTC, which kind of piqued my curiosity. How is MOC different than WebRTC? I don't have enough time for this. We can use code to summarize, MOC is a new streaming protocol, kind of designed from the ground up to take to take advantage of some newer protocols like what's called quick, which is part of TTP three, which is basically a UDP based protocol that can be used in the browser.
00:23:39:05 - 00:24:07:15
Unknown
And there's significant advantages to that for streaming. TCP has a lot of overhead, and it builds latency when there's when there's packet loss because it does retransmission. And you know, traditional streaming right now we have you know, we have HLS and Dash, which are the most common things for one to many streaming. Right. You're watching Netflix if you're watching Apple TV, whatever it is, it's going to be HLS or Dash.
00:24:07:17 - 00:24:37:00
Unknown
WebRTC was originally designed for video conferencing. Still does a pretty good job of that. It's not as great at one to many. It has problems in other areas. It wasn't really designed for things like accessibility, and it wasn't designed for things like DRM. So basically what media or quick is, is attempting to do is take a new look at streaming based on the technology we know now, rather than evolved over the last 10 or 15 years.
00:24:37:03 - 00:24:57:07
Unknown
It's like, how would we do this if we were starting over and kind of trying to be the best of all worlds, and it's got a lot of cool features. You can do super low latency with it. You know, it's playable in browsers, it's you can broadcast from our browser, but you can also broadcast from a camera. It scales really well.
00:24:57:08 - 00:25:23:05
Unknown
You can kind of tune the latency for your size. It's designed to be deployed with what we call relays, which you send a stream to a relay. That relay can then same streams to people or other relays. So you get this kind of treed fan out effect. So there's a lot of promise there. It's getting close. You can certainly go read lots about it.
00:25:23:06 - 00:26:01:03
Unknown
Open mock is an industry group that's kind of deploying versions of it as the spec evolves, but I think it's going to be a really interesting, interesting alternative to some of the other protocols over the next couple of years. What kind of guidance and recommendations can you offer organizations that may either be looking to upgrade some of their video streaming capabilities, or just getting started out in terms of yep, we recognize a need to stream video.
00:26:01:05 - 00:26:28:14
Unknown
It could be a nursing simulation that wants a stream from a live simulation to a classroom of students. For example. Whether that classroom of students is across the hall or across the country. And they're kind of getting started like, well, you know, the big question could be where and how do we get started? Hey, my first advice would, you know, start small, right.
00:26:28:16 - 00:26:59:01
Unknown
So pick one clear use case, one workflow, one problem that you're trying to solve and get it working end to end and get it working. Well, we'll take your case. I have I have one class I'm trying to stream from one place to another. Get that working, get the recording, working, getting our activity elements working. Get that dialed in and then you'll you'll understand what you need to do that at scale.
00:26:59:03 - 00:27:27:08
Unknown
So, you know, I would say don't try to jump in and do 200 classes next week to, you know, 200 different sites, get it working. And then many times you have the infrastructure already, you have cameras, you may have servers. You don't necessarily need to go out and buy a whole broadcast studios worth of infrastructure to get started.
00:27:27:13 - 00:27:52:11
Unknown
You know, start with what you have because, you know, it may be more than good enough to get you there. And then, you know, find people to help you, right? I mean, whether it be vendors or whether it be communities, but there's a lot of there's a lot of places to get advice on, you know, representative best practices and how to do things.
00:27:52:12 - 00:28:11:01
Unknown
I'm not I'm not going to say the right way to do things, because that's different for every implementation and different for every person's use case. But there's a lot of, yeah, there's a lot of resources out there for you to take advantage of. And that's where I think, you know, while this certainly is is great, have a great support team.
00:28:11:01 - 00:28:34:13
Unknown
We have a great services team. They understand not only Wilder's products, but they understand a lot of other products and how things all work together. And I think that's important to so you mentioned infrastructure. I'm kind of curious what kind of infrastructure requirements are you looking for in order to kind of deploy your solutions? So we have a ton of flexibility in that regard.
00:28:34:15 - 00:28:59:18
Unknown
You know, as I mentioned, we can deploy on tiny little edge servers, you know, not much bigger than a Raspberry Pi, to be honest. They're very, very small little devices. And if you only have a camera or two, you're looking to get repackaged and streamed out for mobile consumption or whatever it is that may be an eye. And if you have a remote setting where you're trying to do something, maybe plunking one of those down in that remote setting is going to be good.
00:29:00:01 - 00:29:25:04
Unknown
And you know, you have options as far as what your input source might be. You could you could be streaming from a phone into that box. You could be streaming from a, you know, a dedicated camera or even a professional camera. And, you know, you can start there. And, you know, typically people are these days is almost most streams are going to be viewed on a mobile device.
00:29:25:06 - 00:29:49:03
Unknown
So optimize your stream. So it works well on a mobile device. It works well under all different bandwidth conditions, you know. And streaming engine is great. And creating sets of outputs that look good on mobile devices under varying bandwidth conditions and varying screen sizes and things like that. I like that advice you gave, and I'm curious what type of internal organizational context do you work with?
00:29:49:05 - 00:30:34:12
Unknown
Do you work with a lot of end users, or do you work with the network team? Or who are you really collaborating with from the ground up? So it's it's kind of a combination, right? So I will typically talk to the business owners of the problem to understand what it is they're trying to accomplish. But then when it comes to architecture and implementation, it's it's someone in the technical team, whether whether they have a video engineering group or whether it's the IT team or, or a combination of of the two will work with them to understand their infrastructure constraints or opportunities on the deployment side and how to set up a solution.
00:30:34:12 - 00:31:14:03
Unknown
And that's where we often also will say, you know, bring in other vendors. You know, you may need a delivery provider to deliver things at scale. You may need you may need a cloud provider at some point to step in to do massive storage or something. So understanding where things are and what things, what needs to happen, well, some influence the kinds of partners you might need to bring into the solution, and you help with that overall design so you can help make referrals or you can help guide on we we have tons of partners in the industry, not only from like a systems integration perspective, but vendors like, if we know we need a
00:31:14:03 - 00:31:37:01
Unknown
deer in vendor, we can bring in a vendor we've worked with in the past that we know is going to work encoders, cameras, you know, CMS. So we definitely have the whole aspect of of the ecosystem covered as far as how we attack problems.
00:31:37:03 - 00:31:48:17
Unknown
So bear before we wrap up, is there anything else that you'd like to share with our audience or questions that we should have asked that we missed?
00:31:48:18 - 00:31:53:07
Unknown
Let's see.
00:31:53:09 - 00:31:57:09
Unknown
I think.
00:31:57:11 - 00:32:13:18
Unknown
Let me look through my notes. I think, I mean, we we covered a wide range of ground. And I think it's interesting and I think, you know what I would what I would, you know, say if you're streaming now.
00:32:13:20 - 00:32:45:08
Unknown
Take a look how you're actually using that video. And if some of the new things coming out in terms of, of AI and video intelligence might help you dance, maybe not right now, and that's fine. But, you know, I think what you'll find is that there's data buried in that video, and you're really looking to make the most out of your infrastructure and your video investments.
00:32:45:09 - 00:33:00:12
Unknown
Building that case for, hey, we've done great with video now, but look at all this stuff that's actually opening up. And we can we can look at and do some experiments. Right? I mean, it's whatever it might be.
00:33:00:13 - 00:33:35:09
Unknown
That's awesome advice. How can our listeners get in touch with you if they they want to have a deeper conversation? Well, they can certainly find me on LinkedIn. Pretty easy to find there. It's Barry J. Own whatever, the whole LinkedIn thing. Barry Jones, you can find me on Wasa. I write a lot of blog posts there. And, you know, occasionally you will see me speaking at events or at conferences, certainly at trade shows.
00:33:35:11 - 00:33:58:00
Unknown
But I'd love to hear from people happy to connect on LinkedIn and talk more. And you know, I'm not going to come at you with a hard sell. I you know, you must buy Wowza. Because of this. I really want to come at you more of a problem are you trying to solve and what's the right way to go about it and how you know, how are you going to get the most out of what you're doing?
00:33:58:01 - 00:34:14:11
Unknown
What a great resource. Yeah, well, thanks for your time, Barry. We really appreciate it. I think our listeners of We'll learn a lot from our podcast. Well, I hope so. It's fun. Appreciate the time. Thank you.
00:34:14:13 - 00:34:29:02
Unknown
On Record is a podcast presented by Intelligent Video Solutions, hosted by Mike Anzalone and Carmen Haak and produced by Kyle Shelstad. Find us online at IPIVS.com/onrecord and on social media at Intelligent Video Solutions.