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Code Comments

Bringing Deep Learning to Enterprise Applications

About the episode

There are a lot of publicly available data sets out there. But when it comes to specific enterprise use cases, you're not necessarily going to able to find one to train your models. To realize the power of AI/ML in enterprise environments, end users need an inference engine to run on their hardware. Ryan Loney takes us through OpenVINO and Anomalib, open toolkits from Intel that do precisely that. He looks specifically at anomaly detection in use cases as varied as medical imaging and manufacturing.

Want to learn more about Anomalib? Check out the research paper that introduces the deep learning library.

Ryan Loney Product manager, OpenVINO Developer Tools, Intel®

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Hi, I'm Burr Sutter.

I'm a Red Hatter

who spends a lot of time talking to technologists about technologies.

We say this a lot of Red Hat.

No single technology provider holds the key to success, including us.

And I would say the same thing about myself.

I love to share ideas.

So I thought it'd be awesome

to talk to some brilliant technologists at Red Hat Partners.

This is Code Comments, an original podcast

from Red Hat.

I'm sure, like many of you here, you have been thinking about

AI, ML, artificial intelligence and machine learning.

I've been thinking about that for quite some time

and actually had the opportunity

to work on a few successful projects here at Red Hat using those technologies,

actually enabling a dataset, gathering a dataset,

working with data scientists and data engineering team,

and then training a model and putting that model into production

runtime environment.

It was an exciting set of projects and you can kind of see

those on numerous YouTube videos I have published out there before.

But I want you to think about the problem space a little bit

because there are some interesting challenges about AI/ML.

One is simply just getting access to the data,

and while there are numerous publicly available datasets

when it comes to your specific enterprise use case, you might not be to find

publicly available data.

In many cases, you cannot, even for our applications that we created,

we had to create our dataset, capture our dataset,

explore the dataset, and of course train a model accordingly.

And we also found there's another challenge to be overcome

in this AML world, and that is access to certain types of hardware.

If you think about the enterprise environment

and the creation of an enterprise application specifically for AML

and users need an inference engine to run on their hardware,

hardware that's available to them to be effective for their application.

Let's say an application like computer vision,

one that can detect anomalies in medical imaging or maybe on a factory floor,

You know, those things are whizzing by on the factory line.

They're looking at them and trying to determine if there is an error or not.

Well, how do you actually make it run on your hardware,

your accessible technology that you have today?

Well, there's a solution for this as an open toolkit called Open vino.

And you might be thinking, hey, wait a minute,

don't you need a GPU for a I inferencing a GPU

for artificial intelligence machine learning?

Well, not according to Ryan Loney, product manager of Open Vino Developer

Tools at Intel.

I guess we'll

start with trying to maybe dispel the myths, right?

I think that CPUs are widely used for inference today.

So and if we look at the data center segment, you know, about 70% of the A.I.

inference is happening on Intel Xeon on our data center CPUs.

And so you don't needed a GPU, especially for running inference.

And that's part of the value of open vino, is that we're you know,

we're taking models that may have been trained on a GPU

using deep learning frameworks like PyTorch or TensorFlow

and then optimizing them to run on Intel hardware.

Ryan joined me to discuss AI/ML and the enterprise

across various industries and exploring numerous use cases.

Let's talk a little bit about the origin story behind Open Vino.

Tell us more about it and how it came to be

and why it came out of Intel.

Definitely. So we had the first release of Open Vino

was back in 2018, so still relatively new.

And at that time we were focused on computer vision and pretty tightly coupled

with open CV, which is another open source library with origins at Intel.

You know, it

had its first release back in 1999, so it's been around a little bit longer.

And many of the software engineers and architects at Intel

that were involved with and contributing to open CV are working on open Vino.

So you can think of open vino as complementary software to open CV.

And we're providing like an engine for executing inference

as part of a computer vision pipeline, or at least that's how we started.

But since 2018, we've we've started to move beyond

just computer vision inference.

So when I say computer vision inference, I mean like image

classification, object detection, segmentation.

And now we're moving into natural language processing, things

like speech synthesis, speech recognition, knowledge, graphs,

time series forecasting, and other use cases that don't involve

computer vision and don't involve inference on pixels.

Our latest release, the 20 22.1 that came out earlier this year,

there was a most significant update that we've had to open vino

since we started in 2018, and the major focus of that release

was optimizing for use cases that go beyond computer vision.

And I like that concept that you just mentioned right there, computer vision.

And you said that you extended those use cases and went beyond that.

So could you give us more concrete examples of computer vision?

Yeah, sure.

So when you think about manufacturing quality control in factories, everything

from ARC welding, defect detection to inspecting BMW cars on assembly lines,

they're using cameras or sensors to collect data.

And usually it's cameras collecting images like RGV images that you and I can see.

And looks like something taken from a camera or video camera,

but also things like infrared or computerized tomography

scans used in health care, X-ray, different types of images where we can

draw bounding boxes around regions of interest

and say, you know, this is a defect or this is not a defect.

And also, is this worker wearing a safety hat or did they forget to put it on?

And so you can take this and integrate it into a pipeline

where you're triggering an alert if somebody forgets

to wear their safety mask or if there's a defect in a product

on an assembly line, you can just use cameras and open

vino and open CV running these on Intel hardware and help to analyze.

And that's what a lot of the partners that we work with are doing.

So these independent software vendors

and there's other use cases for things like retail.

You think about going to a store and using an automated checkout system.

You know, sometimes people use those automated checkouts

and they they slide a few extra items into their bag that they don't scan.

And it's a huge loss for the retail outlets

that are providing this way to check out real time shelf monitoring.

We have this bear on one of our is fees that helps keep store shelves

stocked by just analyzing the cameras in the stores, detecting

when objects are missing from the shelves so that they can be restocked.

We have Vistry, another ISP that works with quick service restaurants.

So when you think about automating the process of

when do I drop the fries into the fryer so that they're warm

when the car gets to the drive thru window,

you know, there's quite a bit of industrial health care retail examples

that we can walk through and we should dig into some more of those.

But I got to tell you, I have I have a personal experience

in this category

that I want to share with, and you can tell me how

how silly you might think at this point in time.

It is.

We actually built an AI keynote demonstration for the Red Hat big stage

back in 2015, and I really want to illustrate the concept of asset tracking.

So we actually gave everybody in the conference a little Bluetooth token,

but a little battery, a little watch battery and a little Bluetooth emitter.

And we basically tracked those things around the conference.

We basically put a Raspberry Pi in each of the meeting rooms

and up in the lunch room, and you could see how the tokens

moved from room to room to room as a relatively simple application.

But it occurred to me after we figured out,

okay, how to do that with Bluetooth and triangulating

Bluetooth signals by looking at relative signal strength from one radio to another

and putting that through an Apache Spark application at the time,

we then realized, you know what, this is easier done with cameras

and just simply looking at a camera and having some form of animal

or machine learning model that would say, Oh,

there are people here now are there are no people here now.

What do you think about that?

Yeah, I mean, what you just described is sort of exactly that the product

that either one of our partners is offering,

you know, that they're doing it with computer vision and cameras.

So when partner tries to help retail stores

analyze the foot traffic and understand with Heatmaps,

where people are spending the most time in stores, how many people are coming

in, what size groups are coming into the store,

you know, and trying to help understand if there was a successful transaction

from the people who entered the store and left the store so that you can,

you know, to help with the, you know, retail analytics

and marketing sales and positioning of products.

And so they're doing that in a way that also protects privacy.

And that's something that's really important.

So when you talked about those Bluetooth beacons, probably,

you know, if everyone who walked into a grocery store was asked

to put a tracking device in their cart or on their person and say, you know,

you're going

to be tracked around the store, they probably wouldn't want to do that.

The way that you can do this with cameras is you can,

you know, detect people as they enter and, you know, remove their face.

Right.

So you can ignore any biometric information

and and just track the person based on pixels

that are present in the detected region of interest.

So they're able to analyze, say, a family walks in the door

and they can group those people together with object detection

and then they can track their movement throughout the store

without keeping track of their face or any biometric

or any personal identifiable information to avoid things like bias

and to make sure that they're protecting the privacy of the shoppers in the store

while still getting that really useful marketing analytics data rate

so that they can make better decisions about where to place their products.

So that's one really good example of how

computer vision AI with open vino is being used today.

And that is a great example because you're definitely spot on.

It is invasive when you hand someone to Bluetooth devices,

say, please keep this with you as you go throughout our our store

or our mall or throughout our hospital, wherever you might be.

Now, you mentioned another example earlier

in the conversation which was related to like worker safety.

Are they wearing a helmet?

I want to talk more about that concept in a real industrial setting,

a manufacturing setting where there might be a factory floor

and there are certain requirements, or better yet, there's like a

a quality assurance requirement, let's say, when it comes to looking

at a factory line, I run to that use case often what some of our customers.

Can you talk more about those kinds of use cases? Yeah.

So one of our partners, Robuchon and we you know, published a case study

I think last year where they're working with BMW at one of their factories

and they do quality control inspection, but they're also doing

things related to worker safety and analyzing.

You know, I used the safety had example.

There's a number of of our ISP's and partners who have similar use cases.

And it comes down to there's a few reasons

that are motivating this and some are related to like insurance, right?

It's important to make sure that

if you want to have your factory insured and that your workers

are protecting themselves and wearing the gear.

Regulatory compliance. Right.

You're you're being asked to properly protect from exposure to chemicals or,

you know, potentially having something fall and and hit someone on the head.

So wearing a safety vest, wearing goggles, wearing a helmet,

these are things that you need to do inside the factory.

And you can really easily automate and detect and sometimes without bias.

I think that's one of the interesting things about the robots on BMW example

is that they were also blurring sort of blocking out and so drawing a box

to cover the face of the workers in the factory

so that somebody who was analyzing the video footage

and getting the alerts saying that, hey, you know, Bay 21 has a worker

without a hat on, that it's not sending their face

and in the alert and potentially, you know, invading

or going against privacy laws or just the ethics of the company.

Right.

They don't want to introduce bias or have people targeted because

it's much better to to have it be, you know, blur the face

and alert and have somebody take care of it on the floor.

And then if you ever need to audit that information later,

they have a way to do it where people who need to be able to see

who the employee was and look up their personal information, they can do that.

But then just for the purposes of maintaining safety,

they don't need to have access

to that personal information or biometric information,

because that's one thing that when you hear about computer vision

or object person tracking, object detection,

there's a lot of concern, and rightfully so, about privacy

being invaded and about tracking information, face ID,

identifying people who may have committed crimes through video footage.

And that's just not something that a lot of companies want to

you know, they want to protect privacy

and they don't they don't want to be in a situation

where they might be violating someone's rights.

Well, privacy is certainly opening up Pandora's box.

There's a lot to be explored in that area,

especially in a digital world that we now live in.

But for now, let's move on and explore different area.

I'm interested in how machines and computers offer advantages,

specifically in certain use cases like a quality control scenario.

I asked Ryan to explain how AML and specifically machines

computers can augment that capability.

I can give a specific example where we have a partner

that's there doing defect detection with

and looking for anomalies in batteries.

So, you know, sure, you've heard there's a lot of interest right now

in electric vehicles, a lot of batteries being produced.

And so if you go into one of these factories,

they have images that they collect of every battery that's going through this

assembly line and through these images, people

can look and see and visually inspect with their eyes and say,

this battery has a defect, send it back.

And that's one step in the quality control process.

And there's other steps, I'm sure, like running diagnostic tests and, you know,

measuring voltage and doing other types of non-visual inspection.

But for the visual inspection piece where you can really easily identify

some problems, it's much more efficient to introduce computer vision.

And so that's where we have this new library that we've introduced

called Anomali, that's open vino.

While we're focused on inference, you know, we're also thinking

about the pipeline or the funnel that gets these models to open vino.

And so we've we've invested in this anomaly segmentation,

anomaly detection library and that we've recently open source

and there's a great research paper about it about Anomali.

But the idea is you can take just a few images

and train a model and start detecting these defects.

And so for this battery example, that's a more advanced example.

But to make it simpler, you know, take some bolts and, you know, take ten bolts.

You have one that has a scratch on it or one that is chipped

or has some damage to it.

And you can easily get started in training

to recognize the bolts that do not have an anomaly.

And the ones that do, which is a small data set

and I think that's really one of the most important things today.

Challenges is one is access to data, but the other is

is needing a massive amount of data to do something meaningful.

And so we're starting to try to change that dynamic with Anomali.

So you may not need 100,000 images, you may need 100 images,

and you can start detecting anomalies in everything from batteries to bolts to,

you know, maybe even the wood varnish use case that you mentioned.

That is very key point because often in that data scientist

process, that data engineer and data scientist process, right.

The one key thing is can you gather

the data that you need for the input for the model training?

And we've often sat at least people I've worked

with over the last couple of years, you know, you need a lot of data.

You need tens of thousands of correct images

so we can sort out the difference between dogs versus cats, let's say,

or you need dozens and dozens of situations

where if it's a natural language processing scenario,

you know, a good customer interaction, a good customer conversation,

and in this case,

it sounds like what you're saying is show us just the bad things, right?

Fewer images, fewer incorrect things,

and then let us look for those kind of anomalies.

Can tell us more about that because that is very interesting.

The concept that I can use a much smaller dataset as my input

as opposed to gathering terabytes of data in some cases

to just simply get my model training underway.

You know, like you described, the idea is if you have some good images

and then you have some of the the known defects

and you can just label here's a set of good images

and here's a few of the defects and you can right away

start detecting those specific defects that you've identified.

And then also, you know, be able to

determine when it doesn't match

the expected appearance of a non defective item.

So if I have the undamaged screw and then I introduce

one with some new anomaly that's never been seen before,

I can say, you know, this one is not a valid screw.

And so that's sort of the the approach that we're taking.

And it's really important because so often you need to have

subject matter experts often like if you think the take the battery example,

there's these workers who are on the floor

in a factory and they're the ones who know best when they look at these images,

which one's going to have an issue, which one's defective?

And then they also need to take that subject matter, expertise

and then use it to annotate data sets.

And when you have these, you know,

tens of thousands of images you need to annotate, it's asking those people

to stop working on the factory floor so they can come annotate some images.

That's a tough business call to make, right?

But if you only need them to annotate a handful of images, it's a much easier

ask to get the ball rolling and demonstrate value.

And maybe over time you will want to annotate more

and more images because you'll get even better accuracy in the model.

Even better, even if it's just small incremental improvements.

You know, that's something that if it generates value for the business,

it's something the business will invest in over time.

But you have to convince the decision makers that it's worth

the time of these subject matter experts to stop what they're doing

and go and label some images of the things that they're working on in the factory.

And that labeling process can be very labor intensive of the annotations,

basically saying what is correct, what's wrong, what is this, what is that?

And therefore, if we can minimize that time frame to get the value quicker,

then there's something that's useful for the business, useful for the organization

long before we necessarily good.

There are huge model training based,

so we talk about labeling and how that is labor intensive activity.

But I love the idea of helping the human

and helping the human models specifically not get bored.

Basically, if the human is eyeballing a bunch of widgets flying by over time,

they make mistakes, they get bored

and they don't pay as close attention as they should.

That's why the concept of Amazon specifically computer vision, augmenting

that capability and really helping the human identify anomalies faster,

more quickly, maybe with greater accuracy could be a big win.

We focused on manufacturing, but let's actually go into health care

and learn how these tools can be used in that sector and that industry.

Ryan talked to me about how Open Windows runtime can be incorporated into medical

imaging equipment with intel processors and better than c.T.

MRI and ultrasound machines.

Well, these inferences, this AML workload can be operating

and executing right there in the same physical room as the patient.

We did a presentation when she last year.

I think they said

there's at least 80 countries that have their X-ray machines deployed

and they're doing things like helping doctors place breathing tubes in patients.

So during COVID, during the pandemic, that was a really important tool

to help with nurses and doctors who were intubating patients

sometimes like in a parking lot or a hallway of the hospital.

And, you know, when they had a statistic that you said, I think one out of four

breathing tubes gets placed incorrectly

when you're doing it outside the operating room,

because when you're in an operating room, it's much more controlled

and there's someone who's an expert at placing the tubes.

It's something you have more of a controlled environment

than when you're out in a parking lot, in a tent.

You know, when the hospital's completely full and you're triaging patients

with COVID, that's when they're more likely to make mistakes.

And so they had this endotracheal tube placement

model that they trained,

and it helped to use an x ray and give an alert and say, hey,

this tube is placed wrong, pull it out and do it again.

And so things like that help doctors so that they can avoid mistakes.

And, you know, having a breathing tube placed incorrectly

can cause collapsed lung and a number of other unwanted side effects.

So it's really important to do it correctly.

Another example is Samsung Medicine.

They actually are doing estimating fetal angle of progression.

So this is analyzing ultrasound

of pregnant women with that, being able to to help take measurements

that are usually hard to calculate that can be done in an automated way.

They're already taking the ultrasound scan and now they're executing this model.

They can take some of these measurements to help the doctor avoid potentially more

intrusive alternative methods so the patient wins.

It makes their life better.

And the doctors is getting help from this A.I.

model.

And those are, you know, just a few examples.

Those are some amazing examples when it comes to all these things.

We're talking like CT scans, right, and x rays, other examples of computer vision.

One thing that's kind of interesting in the space, I think

whenever I get a chance to work on, let's say an object traction model

and one of our workshops, by the way, is actually putting that out

in front of people to say, Hey, look, you can use your phone.

And it basically sends the image over to our OpenShift, right,

with our data science platform and then analyzes what you see.

And even in my case, where I take a picture of my dog

as an example, it can't really decide is it a dog or a cat?

I have a very funny looking dog,

and so there's always a percentage outcome, you know?

In other words, I think it's a dog 52%.

So I want to talk about that more.

What how important is it to get to 100% accuracy?

How important is it to really, depending on the use case, to allow

for the gray area, if you will, where it's an 80% accuracy or 70% accuracy?

And where are the trade offs there associated with the application?

Can you can you discuss that more?

Accuracy is definitely, you know, a touchy subject

because how you measure it makes a huge difference.

And then I think with like what you were describing with the dog example, there's

sort of a top five potential classes that might may be identified.

So let's say you're doing object detection and you detect a region of interest

and it says 65% confidence.

This is a dog.

Well, the next potential label that could be maybe 50% confidence

or 20% confidence might be something similar to a dog or in the case of models

that have been trained on like the image net dataset

or on cocoa data set, they have like actual breeds of dogs.

So if I want to look at the top five labels for a dog,

for my dog, for example, she's a mixed mostly Labrador retriever.

But I may look at the top five labels and it may say 65% confidence that she's

a flat coated retriever and then confidence that she's a husky,

as you know, 20% and then 5% confidence that she's a Greyhound or something.

Those labels, all of them are dogs.

So if I'm just trying to figure out is, is this a dog,

I could probably find all of the, you know, classes within the data set

and say, well, these are all, you know, class ID

65, 132, 92 and 158 all belong to a group of dogs.

So if I wanted to just write an application to tell me if this is a dog

or not, I would probably use that to determine if it's a dog.

But how you measure that is accuracy.

Well, that's where it gets a little bit complicated,

because if you're being really strict about the definition and you're

trying to validate against the data set of labeled images

and I have specific dog breeds or some specific detail

and it doesn't match, well, then the accuracy is going to go down.

That's especially important when we talk about things like compression

and quantization,

which, you know, historically has been difficult for to get adoption

in some domains like health care, where even the hint of accuracy going down

implies that we're not going to be able to help in some small case,

maybe if it's even half a percent of the time

we want to take that that tube is placed incorrectly or that, you know,

that patient's, you know, lung has collapsed or something like that.

And that's something that really prevents adoption of some of these methods

that can really boost performance like quantization.

But if you take that example of sort of different from the dog example

and you think about like segmentation of kidneys.

So if I'm doing kidney segmentation, which is, you know, taking a CT scan

and then trying to pick the pixels out of that

scan that belong to a kidney,

how I measure accuracy may be

how many of those pixels I'm able to detect and how many did I miss?

Missing some of the pixels is maybe not a problem, right,

depending on how you built the application because you still detect the kidney

and maybe you just need to apply padding around the region of interest

so that you don't miss any of the the actual kidney.

When you compress the model and when you quantized the model. But

that requires, you know, data scientist and email engineer somebody to really

they have to be

able to go and apply that after the fact, after the inference

happens to make sure that you're not losing critical information,

because the next step from detecting the kidney may be detecting a tumor.

And so maybe you can use the more optimized model

to detect the kidney, but then you can use a slower model to detect the tumor.

But that also requires somebody to architect and make that decision

or that tradeoff and say,

well, I need to add padding, or I should only use the quantized model

to detect the region of interest for the kidney and then use the model

that takes longer to do the inference

just to find the tumor, which is going to be on a smaller size.

Right. The dimensions are going to be much smaller

once we crop to the region of interest.

But all of those details, that's maybe not easy to explain in a few sentences.

And even the way I explained it is probably really confusing.

I do love that use case.

Like you mentioned, the cropping

even in one such an area that we worked on for another project,

we specifically decided to pix like the image that we had taken

because we knew that we could get the outcome we wanted by even

just using a smaller or less having less resolution in our image.

And therefore, as we transferred it from the mobile device storage device

up into the cloud, we wanted that smaller image just for transfer purposes

and it still we could get the accuracy we needed by a lot of testing.

And one thing that's interesting about that from my perspective is

if you're doing image processing, sometimes it takes a while

for this transaction to occur.

Like I,

I come from a traditional application background, you know, where I'm reading

and writing things from a database

or a message broker or moving data from one place to another.

Those things happen subsequent.

Normally, even with great latency between your data centers, you know,

it's still subsequent.

In most cases, while on a transaction like this, one can actually take 2 seconds

or 4 seconds as it's doing its analysis and actually coming back with, you know,

I think it's a dog, I think it's a kidney, I think it's whatever, and provided me

that accuracy statement.

So that concept of optimization is very important

in the overall application architecture.

Would you agree with that or how do you think about that concept?

Yeah, definitely.

It depends too on the use case.

So if you think about how important it is to reduce the latency

and increase the number of frames per second that you can process when you're

talking about a loss prevention model that's running at a grocery store.

So you want to keep the lines moving.

You don't want every person who's at the self-checkout

to have to wait 5 seconds for every item they scan.

You need it to happen as quickly as possible.

And if sometimes you, you know, the accuracy

decreases slightly or the I'd say the accuracy of the whole pipeline.

So not just looking at the individual model or the individual inference, but

let's say that the the whole pipeline is not as successful at detecting

when somebody steals one item from the self-checkout,

it's not going to be a life threatening situation.

Whereas, you know, being in the hooked up to the X-ray machine

with the two placement model, they might be willing to have the doctor,

the nurse wait 5 seconds to get the result.

They don't need it to happen in 500 milliseconds.

So they're willing their threshold for waiting is a little bit higher.

So that, I think, also drives some of the decision, like

you want to keep people moving through the checkout line

and you can afford to to potentially if you lose a little bit of accuracy here

and there, it's not going to

cost the company that much money or it's not going to be life threatening.

It's going to be worth the tradeoff of keeping the line moving

and not having people leave the store and not check out at all.

And to say, I'm not going to shop today because the line's too long.

There are so many trade offs and enterprise

AML use cases, things like latency, accuracy and availability.

And certainly complexities abound, especially in an obviously ever evolving

technological landscape where we are still very early in the adoption of AML.

And to navigate that complexity, the direct feedback from real world

end users is essential to Ryan and his team at Intel.

What would you say are some of the big hurdles or big

outcomes, big opportunities in that space?

And do you agree that we're kind of still at the very beginning in our infancy,

if you will,

of adopting these technologies and and discovering what they can do for us?

Yeah, I think we're definitely in the infancy

and I think that what we've seen is our customers are evolving

and the people who are deploying on Intel hardware, they're trying to run

more complicated models.

They're the models that are doing object detection or, you know,

detecting defects and, you know, doing segmentation.

You know, in the past you could say, oh, here's a generic model that will do face

detection or person detection or vehicle detection and license plate detection.

And those are sort of like

general purpose models that you can just grab off the shelf and use them.

But now we're moving into like the anomaly scenarios

where I've got my own data and I'm trying to do something very specific

and I'm the only one that has access to this data.

And you don't have a public data set that you can go download

that's under Creative Commons license for, you know, car batteries.

It's, you know, it's just not something that's available.

And so those use cases, the challenge with with training those models

and and getting them optimized is the beginning of the pipeline.

It's the data you have to get the data you have to annotated

and the tools have to exist for you to do that.

And that's part of the problem that we're trying to help solve.

And then the models are getting more complex.

So if you think, you know, just from working with customers recently,

you know, they're no longer

just trying to do image classification and, you know, like is it a dog or a cat?

They've moved on to like 3D point clouds

and, you know, 3D segmentation models and things that are like the speech

synthesis example, doing things these GPT models that are generating,

you know, you, you put a text input and it generates an image for you.

It's just becoming much more advanced, much more sophisticated

and on larger images.

And so things like running super resolution enhancing images, upscaling

images, instead of just trying to take that, you know, 200 by 200 pixel

image and classifying if it's a cat.

Now we're talking about gigantic

huge images that we're processing and that all requires

more resources or more optimized models.

And, you know, every computer vision conference or A.I.

conference, there's there's a new latest and greatest architecture.

There's new research paper, and things are getting adopted much faster.

The lead time for a nurse paper or CV PR

for a company to actually adopt and put those into production.

It's like the time shortens every year.

Well, Ryan, I got to tell you, I could talk to you

literally all day about these topics, the various use cases, the various ways

models are being optimized,

how to put models into a pipeline for average enterprise applications.

I've enjoyed learning about pop and vino and anomalies,

but I'm fascinated by this because I will have a chance to go try this myself.

Taking advantage of Red Hat OpenShift

and taking advantage of our data science platform.

On top of that, I will definitely go be poking at this myself.

So thank you so much for your time today.

Thanks, Burr.

This was a lot of fun.

Thanks for having me.

And you can check out

the full transcript of our conversation and more resources,

like a link to a white paper on open vino and normal lib at Red Hat dot

com slash code Comments Podcast.

This episode was produced by Brant Seminole and Caroline Prickett.

Our sound designer is Christian.

From our audio team includes Lee Day, Stephanie Wunderlich, Mike Esser,

Laura Barnes, Claire Allison, Nick Burns, Aaron Williamson, Karen King,

Booboo House, Rachel Artell, Mike Compton, Ocean

Mathews, Laura Walters, Alex Trabelsi and Victoria Lutton.

I'm your host, Burt Sutter.

Thank you for joining me today on Code Comments.

I hope you enjoyed today's session and today's conversation, and I

look forward to many more in.

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