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>> We're back at the Red Hat Summit
at the Seaport in Boston, theCUBE's coverage.
This is day two. Dave Vellante and Paul Gillin.
Chris Wright is here,
the chief technology officer at Red Hat.
Chris, welcome back to theCUBE.
Good to see you.
>> Yeah, likewise. Thanks for having me.
>> You're very welcome.
So, you were saying today in your keynote.
We got a lot of ground to cover here, Chris.
You were saying that, you know, software,
Andreessen's software is eating the world.
Software ate the world, is what you said.
And now we have to think about AI.
AI is eating the world.
What does that mean?
What's the implication for customers and developers?
>> Well, a lot of implications.
I mean, to start with,
just acknowledging that software isn't this future dream.
It is the reality of how businesses run today.
It's an important part of understanding
what you need to invest in to make yourself successful,
essentially, as a software company,
where all companies are building technology
to differentiate themselves.
Take that, all that discipline,
everything we've learned in that context, bring in AI.
So, we have a whole new set of skills to learn,
tools to create and discipline processes to build around
delivering data-driven value into the company,
just the way we've built software value into companies.
>> I'm going to cut right to the chase
because I would say data is eating software.
Data and AI, to me, are like, you know, kissing cousins.
So here's what I want to ask you as a technologist.
So we have the application development stack, if you will.
And it's separate from the data and analytics stack.
All we talk about is injecting AI into applications,
making them data-driven.
You just used that term.
But they're totally two totally separate stacks,
organizationally and technically.
Are those worlds coming together?
Do they have to come together
in order for the AI vision to be real?
>> Absolutely, so, totally agree with you on the data piece.
It's inextricably linked to AI and analytics
and all of the, kind of, machine learning that goes on
in creating intelligence for applications.
The application connection to a machine learning model
is fundamental.
So, you got to think about not just the software developer
or the data scientist,
but also there's a line of business in there that's saying,
"Here's the business outcomes I'm looking for."
It's that trifecta that has to come together
to make advancements and really make change in the business.
So, you know, some of the folks we had on stage today
were talking about exactly that.
Which is, how do you bring together
those three different roles?
And there's technology that can help bridge gaps.
So, we look at what we call intelligent applications.
Embed intelligence into the application.
That means you surface a machine learning model with APIs
to make it accessible into applications,
so that developers can query a machine learning model.
You need to do that with some discipline and rigor
around, you know, what does it mean
to develop this thing and life cycle it
and integrate it into this bigger picture.
>> So the technology is capable of coming together.
You know, Amanda Purnell is coming on next.
>> Oh, great.
>> 'Cause she was talking about, you know,
getting, you know, insights in the hands of nurses
and they're not coders. >> That's right.
>> But they need data.
But I feel like it's, well, I feel very strongly
that it's an organizational challenge, more so.
I think you're confirming.
It's not really a technical challenge.
I can insert a column into the application development stack
and bring TensorFlow in or AI or data, whatever it is.
It's not a technical issue. Is that fair?
>> Well, there are some technical challenges.
So, for example, data scientists.
Kind of a scarce kind of skillset within any business.
So, how do you scale data scientists
into the developer population?
Which will be a large population within an organization.
So, there's tools that we can use
to bring those worlds together.
So, you know, it's not just TensorFlow
but it's the entire workflow and platform
of how you share the data,
the data training models and then just deploying models
into a runtime production environment.
That looks similar to software development processes
but it's slightly different.
So, that's where a common platform can help bridge the gaps
between that developer world and the data science world.
>> Where is Red Hat's position in this evolving AI stack?
I mean, you're not into
developing tool sets like TensorFlow, right?
>> Yeah, that's right.
If you think about a lot of what we do,
it's aggregate content together,
bring a distribution of tools,
giving flexibility to the user.
Whether that's a developer, a system administrator,
or a data scientist.
So our role here is, one, make sure
we work with our hardware partners
to create accelerated environments for AI.
So, that's sort of an enablement thing.
The other is bring together
those disparate tools into a workflow
and give a platform that enables data scientists
to choose which, is it PyTorch, is it TensorFlow?
What's the best tool for you?
And assemble that tool into your workflow
and then proceed training, doing inference,
and, you know, tuning and lather, rinse, repeat.
>> So, to make your platform then,
as receptive as possible, right?
You're not trying to pick winners
in what languages to work with or what frameworks?
>> Yeah, that's right.
I mean, picking winners is difficult.
The world changes so rapidly.
So we make big bets on key areas
and certainly TensorFlow would be a great example.
A lot of community attraction there.
But our goal isn't to say that's the one tool
that everybody should use.
It's just one of the many tools in your toolbox.
>> There are risks of not pursuing this,
from an organization's perspective.
A customer, they kind of get complacent
and, you know, they could get disrupted,
but there's also an industry risk.
If the industry can't deliver this capability,
what are the implications if the industry doesn't step up?
I believe the industry will, just 'cause it always does.
But what about customer complacency?
We certainly saw that a lot with digital transformation
and COVID sort of forced us to march to digital.
What should we be thinking about
of the implications of not leaning in?
>> Well, I think that the disruption piece is key
because there's always that spectrum of businesses.
Some are more leaning in, invested in the future.
Some are more laggards and kind of wait and see.
Those leaning in tend to be separating themselves,
wheat from the chaff.
So, that's an important way to look at it.
Also, if you think about it,
many data science experiments fail within businesses.
I think part of that is not having
the rigor and discipline around connecting,
not just the tools and data scientists together,
but also looking at what business outcomes
are you trying to drive?
If you don't bring those things together
then it sort of can be too academic
and the business doesn't see the value.
And so there's also the question of transparency.
How do you understand why is a model
predicting you should take a certain action
or do a certain thing?
As an industry, I think we need to
focus on bringing tools together, bringing data together,
and building better transparency into how models work.
>> There's also a lot of activity
around governance right now, AI governance.
Particularly removing bias from ML models.
Is that something that you are guiding your customers on?
Or, how important do you feel this is
at this point of AI's development?
>> It's really important.
I mean, the challenge is finding it
and understanding, you know,
we bring data that maybe already
carrying a bias into a training process
and building a model around that.
How do you understand what the bias is in that model?
There's a lot of open questions there and academic research
to try to understand how you can ferret out,
you know, essentially biased data
and make it less biased or unbiased.
Our role is really just bringing the toolset together
so that you have the ability to do that as a business.
So, we're not necessarily building the next
machine learning algorithm or models
or ways of building transparency into models,
as much as building the platform
and bringing the tools together
that can give you that for your own organization.
>> So, it brings up the question of architectures.
I've been sort of a casual or even active observer
of data architectures over the last, whatever, 15 years.
They've been really centralized.
Our data teams are highly specialized.
You mentioned data scientists,
but there's data engineers and there's data analysts
and very hyper specialized roles
that don't really scale that well.
So there seems to be a move, talk about edge.
We're going to talk about edge.
The ultimate edge, which is space, very cool.
But data is distributed by its very nature.
We have this tendency to try to force it
into this, you know, monolithic system.
And I know that's a pejorative, but for good reason.
So I feel like there's this push in organizations
to enable scale, to decentralize data architectures.
Okay, great.
And put data in the hands of those business owners
that you talked about earlier.
The domain experts that have business context.
Two things, two problems that brings up,
is you need infrastructure that's self-service,
in that instance.
And you need, to your point,
automated and computational governance.
Those are real challenges.
What do you see in terms of the trends
to decentralize data architectures?
Is it even feasible that everybody
wants a single version of the truth,
centralized data team, right?
And they seem to be at odds.
>> Yeah, well I think we're coming from a history
informed by centralization.
That's what we understand.
That's what we kind of gravitate towards,
but the reality, as you put it,
the world's just distributed.
So, what we can do is look at federation.
So, it's not necessarily centralization
but create connections between data sources
which requires some policy and governance.
Like, who gets access to what?
And also think about those domain experts
maybe being the primary source of surfacing a model
that you don't necessarily have to know
how it was trained or what the internals are.
You're using it more to query it as a, you know,
the domain expert produces this model,
you're in a different part of the organization
just leveraging some work that somebody else has done.
Which is how we build software,
reusable components in software.
So, you know, I think building that mindset into data
and the whole process of creating value from data
is going to be a really critical part of how we roll forward.
>> So, there are two things in your keynote.
One, that I was kind of in awe of.
You wanted to be an astronaut when you were a kid.
You know, I mean, I watched the moon landing
and I was like, "I'm never going up into space."
So, I'm in awe of that.
>> Oh, I got the space helmet picture and all that.
>> That's awesome, really, you know, hat's off to you.
The other one really pissed me off,
which was that you're a better skier
'cause you got some device in your boot.
>> Oh, it's amazing.
>> And the reason it angered me is 'cause I feel like it's
the mathematicians taking over baseball, you know.
Now, you're saying, you're a better skier because of that.
But those are two great edge examples
and there's a billion of them, right?
So, talk about your edge strategy.
Kind of, your passion there,
how you see that all evolving.
>> Well, first of all, we see the edge
as a fundamental part of the future of computing.
So in that centralization, decentralization pendulum swing,
we're definitely on the path towards distributed computing
and that is edge and that's because of data.
And also because of the compute capabilities
that we have in hardware.
Hardware gets more capable, lower power,
can bring certain types of accelerators into the mix.
And you really create this world
where what's happening in a virtual context
and what's happening in a physical context
can come together through this distributed computing system.
Our view is, that's hybrid.
That's what we've been working on for years.
Just the difference was maybe,
originally it was focused on data center, cloud, multi-cloud
and now we're just extending that view out to the edge
and you need the same kind of consistency
for development, for operations,
in the edge that you do in that hybrid world.
So that's really where we're placing our focus
and then it gets into all the different use cases.
And you know, really, that's the fun part.
>> I'd like to shift gears a little bit
'cause another remarkable statistic
you cited during your keynote was,
it was a Forrester study that said
99% of all applications now have open source in them.
What are the implications of that
for those who are building applications?
In terms of license compliance and more importantly,
I think, confidence in the code
that they're borrowing from open source projects.
>> Well, I think, first and foremost,
it says open source has won.
We see that that was audited code bases
which means there's mission critical code bases.
We see that it's pervasive, it's absolutely everywhere.
And that means developers are pulling dependencies
into their applications based on all of the genius
that's happening in open source communities.
Which I think we should celebrate.
Right after we're finished celebrating
we got to look at what are the implications, right?
And that shows up as,
are there security vulnerabilities that become ubiquitous
because we're using similar dependencies?
What is your process for vetting code
that you bring into your organization
and push into production?
You know that process for the code you author,
what about your dependencies?
And I think that's an important part of understanding
and certainly there are some license implications.
What are you required to do when you use that code?
You've been given that code on a license
from the open source community,
are you compliant with that license?
Some of those are reasonably well understood.
Some of those are, you know, newer to the enterprise.
So I think we have to look at this holistically
and really help enterprises build safe application code
that goes into production and runs their business.
>> We saw Intel up in the keynotes today.
We heard from Nvidia, both companies are coming on.
We know you've done a lot of work with ARM over the years.
I think Graviton was one of the announcements this week.
So, love to see that.
I want to run something by you as a technologist.
The premise is, you know,
we used to live in this CPU centric world.
We marched to the cadence of Moore's Law
and now we're seeing the combinatorial factors
of CPU, GPU, NPU, accelerators
and other supporting components.
With IO and controllers and NICs all adding up.
It seems like we're shifting
from a processor centric world
to a connect centric world on the hardware side.
That first of all, do you buy that premise?
And does hardware matter anymore with all the cloud?
>> Hardware totally matters.
I mean the cloud tried to convince us
that hardware doesn't matter and it actually failed.
And the reason I say that is
because if you go to a cloud,
you'll find 100s of different instance types that are all
reflections of different types of assemblies of hardware.
Faster IO, better storage, certain sizes of memory.
All of that is a reflection of,
applications need certain types of environments
for acceleration, for performance, to do their job.
Now I do think there's an element of,
we're decomposing compute into all of these
different sort of accelerators
and the only way to bring that back together
is connectivity through the network.
But there's also SOCs when you get to the edge
where you can integrate the entire system
onto a pretty small device.
I think the important part here is,
we're leveraging hardware to do interesting work
on behalf of applications that makes hardware exciting.
And as an operating system geek,
I couldn't be more thrilled,
because that's what we do.
We enable hardware, we get down into the bits and bytes
and poke registers and bring things to life.
There's a lot happening in the hardware world
and applications can't always follow it directly.
They need that level of indirection
through a software abstraction
and that's really what we're bringing to life here.
>> We've seen now hardware specific AI, you know,
AI chips and AI SOCs emerge.
How do you make decisions about what you're going to support
or do you try to support all of them?
>> Well, we definitely have a breadth view of support
and we're also just driven by customer demand.
Where our customers are interested
we work closely with our partners.
We understand what their roadmaps are.
We plan together ahead of time
and we know where they're making investments
and we work with our customers.
What are the best chips that support their business needs
and we focus there first but it ends up being
a pretty broad list of hardware that we support.
>> I could pick your brain for an hour.
We didn't even get into super cloud, Chris.
But, thanks so much for coming on theCUBE.
It's great to have you.
>> Absolutely, thanks for having me.
>> All right. Thank you for watching.
Keep it right there.
Paul Gillin, Dave Vellante,
theCUBE's live coverage
of Red Hat Summit 2022 from Boston.
We'll be right back.
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