>> Narrator: Live from Boston, Massachusetts,
it's theCUBE.
Covering Red Hat Summit 2019.
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>> Good to have you back here on The Cube
as we continue our coverage live at the Red Hat Summit 2019.
Day three of our coverage,
we've been with you since Tuesday.
And now, just fresh off the keynote stage
joining Stu Miniman and myself, Chris Wright,
the VP and chief technology officer at Red Hat.
Good job up there, Chris.
Thanks for being with us this morning.
>> Yeah, thank you, glad to be here.
>> Great, among your central themes,
you talked about this new cycle of innovation
and those components, and how they're integrating
to create all these great opportunities.
So, if you would, just share for those or with those at home
who didn't have an opportunity to see
the keynote this morning, just what you were talking about.
>> Sure. >> How they play
together and where that lies with Red Hat.
>> Yeah, you bet, so I think an important
first kind of concept is a lot of what we're doing
is laying a foundation or a platform.
I mean, Red Hat's focus is in the platform space.
So we think of it as building this platform
upon which you build and innovate.
And so what we're seeing is a critical part
of the future is data.
So we're calling it data centric,
it's the data centric economy.
Along with that is machine learning.
So, all the intelligence that comes, what are you divining,
the insights you're grabbing from that data.
It introduces some interesting challenges.
Data and privacy, and what do we do with that data.
I mean, we're all personally aware of this.
You see the Cambridge Analytica stuff
and, you know, we all have concerns about our own data.
And when you combine all of this together,
techniques for how we can create insights from data
without compromising privacy,
we're really pushing the envelope
into full distributed systems, edge deployments,
data coming from everywhere,
and the insights, that go along with that.
So it's a really exciting time
built on a consistent platform like OpenShift.
>> Chris, I was love getting to dig in with you,
because that big trend of distributed systems
is something, that we've been working at
for quite a long time,
but we fully agree to use that data
at the center of everything,
and that role of even more distributed system,
the multi-cloud world, customers have their stuff everywhere
and getting their arms around that, and managing it,
and being able to leverage and take advantage
of that data is super challenging.
>> Chris: Yeah. It is.
>> Help us understand some of the areas,
that Red Hat and the communities are looking
to solve those problems, where are we,
and what's going well, and what's still left to work on.
>> Well, there's a couple of different aspects,
so number one, we're building these big complex systems.
Distributed systems are challenging.
Distributed systems engineers
are really solving hard problems
and we have to make that accessible to everybody,
operations teams and it's one of the things,
that I think the cloud taught us.
When you sort of outsource your operations
to somebody else,
you get this encapsulated operational excellence.
We need to bring that to wherever
your workloads are running.
And so we talked a lot about AI ops,
how you harness the value of data,
that's coming out of this complex infrastructure,
feed it through models and gain insights,
and then predict and really, ultimately,
we're looking at autonomic computing.
How we can create autonomous clouds,
things, that really are operating themselves
as much as possible with minimal human intervention,
so we get massive scale,
I think that's one of the key pieces.
The other one, really talking about a different audience,
the developers, so developers are trying to incorporate
similar types of intelligence into their applications.
You're making recommendations,
you're trying to personalize applications for end users.
They need easy access to that data,
they need easy access to train models,
so how do we do that?
How do we make that challenging,
data scientist centric workflow accessible to developers?
>> Just some of the challenges out there
I think about 10, 15 years ago you talk to people,
it was like, well, I had my central source of truth
and it was a database.
And you talk to most-- >> Right.
Companies now and it's like, well,
I've got at least a dozen different database
and all my different flavors of them,
and whether they're in the cloud
or whether I have them in my environment.
Things like AI ops trying to help people
get involved with them.
You talked a little bit in your keynote
about some of the partners, that you're working on.
So how do you bring these together and simplify them,
when they're getting even more and more fragmented?
>> Well, it's part of the challenge of innovation.
I mean, I think there's a natural cycle.
Creativity spawns new ideas,
new ideas are encapsulated in projects.
So there's a wave of expansion
in any kind of new technology timeframe.
And then there's, ultimately, you see some contraction
as we get really the clear winners and the best ideas,
and in the container orchestration space
Kubernetes is a great example of that.
We had a lot of proliferation of different ways of doing it,
today we're consolidating as an industry around Kubernetes.
So what we're doing is building a platform,
building a rich ecosystem around that platform
and bringing our partners in, who have specific solutions
they look at whether it's the AI ops side of the house
talking to the operations teams
or whether it's giving developers easy access
to data and training models through some partners,
that we had today, like PerceptiLabs and H2O.ai.
This partnership bringing it to a common platform
I think is a critical part
of helping the industry move forward,
and ultimately we'll see where these best
of breed tools come in to play.
>> Can you maybe help a little bit with
in terms of practical application,
you've got open source, where you've got,
there's community development going on,
and then people customize based on their individual needs.
Great, right?
How does the inverse happen,
where somebody who does some customization
and comes up with a revelation of some kind
and that applies back to the general community,
and can you think of a time where maybe something,
I'm thinking like Boston Children's,
they're imaging that hospital we saw or HCA.
It's actually related to another industry somehow
and gave them an aha moment,
that maybe they weren't expecting
and open source really was the driver of that.
>> Yeah, well I think what we showed today
were some examples of what,
if you distill it down to the core,
there's some common patterns.
There's data, there's streaming data,
there's the data processing
and there's connection to that process data
or training model to an application.
So we've been building an open source project
called OpenDataHub, where we can bring people together
to collaborate on what are the tools that would need to be
in this stack of this kind of framework or stack.
And then as we do that we're talking to banks,
they're looking at any money laundering and fraud detection.
We're talking to these hospitals,
that are looking at completely different use cases,
like HCA healthcare, which is taking data
to reduce the amount of time
nurses need to spend gathering information from patients
and clearly identify sepsis concerns.
Totally different applications, similar framework
and so getting that industry level collaboration
I think is the key,
and having common platforms and common tools
and a place to rally around these bigger problems
is exactly how we do that through open source.
>> So Linux sits at an interesting place in the stack
'cause you talked about, we want commonality
and everything like that.
But, we're actually at a time,
where the proliferation of what's happening
at the hardware level is something that,
I'm an infrastructure and hardware guy by background
and it was like, oh, I thought we're gonna
to homogenize everything, standardize everything
and it was like, oh, well you're showing off
cool NVIDIA stuff, and when we're doing
all these AI pieces, there's all these new things.
You work from the main frame
through the latest ARM processors.
Give us a little insight as to how your team's geeking out
and making sure, that they provide that commonality,
yet can take advantage of some
of the cool awesome stuff that's out there,
that's enabling that next wave of innovation.
>> Yeah, so I share that infrastructure geek-ness with you,
so I'm so stoked, that we're in this cycle
of hardware innovation.
I'll say something, that maybe sounds controversial.
If we go back in time just five years or a little more,
the focus was around cloud computing
and bringing massive number of apps to the cloud,
and the cloud had kind of a T-shirt size
small, medium, large view of the world of computing.
It created this notion,
that compute is homogenous, it's a lie.
If you go today to a cloud provider
and count the number of different machine types
they have or instance types, it's not just three.
It's a big number and those are all specialized.
It's for IO throughput, it's for storage acceleration,
it's big memory, it's all these different use cases,
that are required for the full set of applications.
Maybe you get the 80% in a common core,
but there's a whole bunch of specific use cases,
that require performance optimizations, that are unique.
And what we're seeing, I think, is Moore's law,
the laws of physics are kind of colliding a little bit,
and the way to get increased acceleration
is through specialized hardware.
So we see things like TPUs from Google,
we see Intel doing DL boost, we've got GPUs and even FPGAs,
and the operating system is there
to give a consistent application runtime,
while enabling all those hardware components
and bringing it all together so,
that the applications can leverage
the performance acceleration
without having to be tied directly to it.
>> And actually I think you wrote about that, right,
in one of your blog posts?
>> Chris: Yeah.
>> That came up about how hardware
plays this hugely important role.
You also talked about innovation and change
happening incrementally, and that's not how
we kind of think about like big bangs, right?
>> Yeah. >> Like, wow, this is,
but you pointed out, that in the open source
it really is step, by step, by step,
which if we think about disruption as being very dramatic
and there's nothing sexy about step by step,
but that's how we get to disruption.
>> See, I kind of hate this innovation disruption,
they're buzz words on the one hand,
so it's what captures attention.
It's not necessarily clear what they mean.
I like the idea, that in open source
we do everyday incremental improvements.
And it's the culmination of all these improvements
over time, that unlock new opportunities.
And people ask me all the time, where's the future,
what are doing, what's going on?
You know, we're kind of doing the same thing
we've been doing for a long time.
Think about microservices as a way
to encapsulate functionality,
share and reuse with other developers.
While object oriented programming decades ago
was really trying to establish
that same capability for developers.
The technology's changed, we're building on our history,
we're always incrementally improving.
You bring it all together and yes, occasionally,
you can apply that in a business case,
that totally disrupts an industry and changes the game.
But I really wanna encourage people
to think about what are the incremental changes
you can make to create something fundamentally new?
>> All right, I need to poke at that a little bit, Chris,
'cause there's one thing, I look back at my career,
and took a look back a decade or two decades,
and we used to talk about things
like intelligence and automation.
Those have been around my entire career.
>> Chris: Yeah.
>> You look at today, though,
you talk about intelligence and you talk about automation,
it's not what we were doing.
>> Fair. >> Just the amount of degrees,
what we're having there.
It is like, if we'd looked at it before,
it was like, oh my gosh, science fiction's here.
We sometimes lose when we're doing step by step,
that some things are making step function improvements
and now the massive compute, massive changes.
Love your opinions there.
>> Yeah, well, I think it's a combination.
I talk about the perpetual pursuit of excellence,
so you pick a field, we're talking about management,
we got data and how you apply that data.
We've been working towards
autonomic computing for decades.
The concepts and research are old.
The details and the technologies and the tools,
that we have today are quite different,
but I'm not sure that that's always a major step function,
I think part of that is this incremental change,
and you look at the number or the amount
of kind of processing power in a GPU today.
Well, this is a problem,
that that industry has been working
on for quite a long time.
At some point we realize, hey,
the vector processing capabilities
in a GPU really suit the machine learning
matrix multiplication real world use case.
So that was a fundamental shift,
which unlocked a whole bunch of opportunity
in terms of how we harness data
and turn it in to knowledge.
>> So are there any areas that you look at now,
that we've been working at, that you feel
we're kind of getting to those tipping points
or the waves of technology are coming together
to really enable some massive change?
>> I do think our ability to move data around,
like generate data, for one thing,
move data around efficiently,
have access to it from a processing capability
and turning that into a model
has so fundamentally changed in the past couple of decades,
that we are tapping into the next generation
of what's possible.
And things like having this holy grail
of self-healing, self-optimizing, self-driving cluster
is not as science fiction as it felt 20 years ago.
>> It's kind of exciting when you talk about,
you've been there in the past and the present,
but there's very much a place in the future, right,
and what that future looks like.
Just from, again, that AI perspective,
it's a little scary sometimes too to some people,
so how are you going about, I guess,
working with your partners
to bring them along and accept certain notions,
that maybe five, six years ago
might've been a little tough to swallow
or to feel comfortable with.
>> Yeah, well there's a couple of different dimensions there.
One is finding tasks, that computers are great at,
that augment tasks, that humans are great at.
And the example we had today, I love the example,
which was, let's have computers crunch numbers
and nurses do what they do best,
which is provide care and empathy for the patients.
So it's not taking the nurse's job away,
in fact, it's taking the part that is drudgery,
it's computation, that computers are great at.
>> I forget, what was the--
>> We call it machine enhanced human intelligence.
>> John: That's right, yeah.
>> And a couple of different ways of looking at that,
but the idea, that we're not necessarily
trying to eliminate humans out of the loop,
we're trying to get humans to do what they do best
and take away the drudgery, that computers are awesome at.
Repetitive tasks, big number crunching.
I think that's one piece, the other piece is really
from that developer point of view,
how do you make it easily accessible
and then the one step, that needs to come after that
is understanding the black box.
What happens inside the machine learning model?
How is it creating the insights, that it's creating?
And there's definitely work to be done there,
there's work that's already underway
to help understand really what's behind the insights,
so that we don't just trust, which can create some problems,
when we're introducing data,
that itself might already be biased,
and then we assume, because we gave data to a computer,
which is seemingly unbiased,
it's gonna give us an unbiased result, right?
Well, garbage in, garbage out,
so we've got to be really thoughtful
about what the models are and what the data is,
that we're feeding it.
>> Yeah, makes perfect sense.
Hey, thanks for the time.
Good job on the keynote stage again this morning.
I know you've got a busy afternoon scheduled as well.
We'll cut you loose, but thank you again.
Always good to see you.
>> Yeah, always enjoy being here, thanks, guys.
>> All right, Chris Wright joining us from Red Hat,
Back with more from the Red Hat Summit 2019,
You're watching live here on theCUBE.
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