>> Live from Seattle, Washington, it's theCUBE.
Covering KubeCon, and CloudNativeCon North America 2018.
Brought to you by Red Hat, the CloudNative
Computing Foundation, and Kentico System partners.
>> Okay, welcome back here everyone.
We are here live with Kube coverage,
three days of wall-to-wall coverage here at KubeCon,
CloudNativeCon 2018 in Seattle.
I am John Furrier with theCUBE,
Stu Miniman here breaking it down,
we're at day two, we got to a lot of action.
David Aronchick, whose the head of open-source ML Strategy
at Azure, at Microsoft, Microsoft Azure,
formerly of Google, now at Microsoft,
welcome back to theCUBE.
We had a great chat at Coppenhagan,
good to see you.
>> Great to see you, too.
Thank you so much for having me.
>> You've been there from day one,
it's still kind of day one in Kubernetes.
It's still growing.
You got a new gig, you're at Microsoft, formerly at Google.
You had a great job at Google Next, by the way,
which we watched and caught online,
you're still doing the same thing.
Take a minute to explain kind of what the new job is,
what your focus is, what-- >> Absolutely.
So in many ways, I'm doing a very similar job
to the one I was doing at Google,
except now across all of Azure.
You know, when you look at machine learning today,
the truth of the matter is,
it is about open-source.
It's about pulling the best from academia,
and open-source contributors, developers,
across the spectrum.
And while I was at Google,
I was able to launch the KubeFlow project,
which solves a very specific, but very important, problem.
Now that you look at Azure,
a company that is growing,
excuse me, a division that is growing extremely quickly,
and looking to expand their overall open-source offerings,
make investments, work with partners, and projects,
and make sure that,
that researchers and customers are able
to get the machine-learning solutions very quickly.
I'm coming into help them think about,
how to make those investments,
and accelerate customers' overall time to solutions.
>> So both on the commercial side at Azure,
which has got a business objective, to make money,
but also open-source.
Is it still open-source for you?
Is it all open-source?
Or is it crossing a little bit of both?
Just quickly clarify that.
>> Yeah, there's no question.
You know, obviously Azure's a business,
they pay me a salary,
and we're going to have great first party solution
for all of these various things.
But, the reality is,
much like Kubernetes has both a commercial offering,
and an open-source offering,
I think that all the major cloud providers
will have that kind of duality.
They'll work in open-source,
and you can measure, you know,
how many contributions,
and what they're doing in the open-source projects,
but then they'll also have hosted and other versions
that make it easier for customers to migrate their data,
and adopt some of these new solutions.
>> You know, one of the things that's interesting
on that point because this's a super important point,
is that, open-source community that's here with Kubernetes,
around Kubernetes, you know,
it's all kind of upstream kind of concept,
but the downstream impacts are IT,
and your classic developers.
You have your open-source kind of thing going on.
That's the core of this community and event.
The IT investments are shifting.
In 2019, we are seeing the trend of,
somewhat radical, but a considerably reimagining of the IT.
I mean, certainly, you guys have gone cloud at Azure,
and seen that result.
>> Absolutely.
>> Good pick up by customers. office 365, that's now a SAS,
it's now, now you got cloud, you got CloudScale,
this where machine learning is really shining.
So, the question to you is,
what do you think is going to be the big impact in 2019
to IT investment strategies, in terms of what they,
how they procure, and consume technology,
how they build their apps,
with the new goodness coming in from Kubernetes, et cetera.
>> Absolutely, you know I remember back in the day,
you know, I was an IT admin myself,
and I carried a pager for literally when, you know,
a machine went down, or our power supply went out,
or this RAM was bad, or something like that.
Today, if you went to even the most sophisticated IT shop,
they would, be like, "What are you crazy?
"You should never carry a pager for that.
"You should have a system that understands it's okay
"if something that low-level goes out."
That's exactly what Kubernetes provided.
They provided this abstraction layer on top of this,
so if it went down,
Kubernetes knew how to reschedule a pod,
and move things back and forth.
Taking that one step further, now, into machine learning.
Unfortunately today, people are carrying pagers
for the equivalent of if a power supply goes out,
or something goes wrong.
It's still way to low-level.
We're asking data scientists, ML engineers,
to think about how to provision pods,
how to work on drivers,
how to do all these very, very low-level things.
With things like Kubernetes, with things like Kubeflow,
you're now able to give higher lever abstractions,
so when data scientists can come in,
and, you know, open up their Jupyter Notebook,
work on a model, see how it works,
and when they're done, they hit a button,
and it will provision out all the machines necessary,
all the drivers, all the, everything.
Spin it up, run that training job, and bring it back,
and shut everything down.
>> Steve, wonder if you can help expand on that
a little bit more. >> Yeah.
>> So, you know, one of the things
that's great about Kubernetes,
is it can live in a diverse amount of infrastructures.
One of the biggest challenges with machine learning is,
you know, where's my data?
How do I get to the right place?
Where do I do the training?
You know, we've been spending a lot,
a couple of years looking at, you know,
Edge and, you know, what's the connectivity,
and how we're going to do this.
Can you help, just kind of paint us a picture of the landscape
and what do we have solved,
and are working at trying to get put together.
>> Yeah, you know,
I think that's a really excellent question.
Today, there's so much focus on,
"Well, are you going to choose PyTorch or TensorFlow,
CNTK, MXNet, NumPy, Psycular.
There're a bunch of really great frameworks out there,
done in the open-source, and we're really excited.
But the reality is,
when you look at the overall landscape,
that's just 5% of the work
that the average data scientist goes through.
Exactly in your point.
How do I get my data in?
How do I transform it?
How do I visualize it?
Generate statistics on it?
Make sure that it's not biased towards certain populations?
And then once I am done training,
how do I roll it out to production,
and monitor, and log it, and all these things.
And that's really what we are talking about,
that's what we tried to work on, when it comes to Kubeflow,
is to think about this in a much broader sense.
And so, you take things like data...
The reality is you can beat the speed of light.
If I have a petabyte of data here,
it's going to take a long time to move it over there,
and so you're going to be really thoughtful
about those kind of things.
I'm very hopeful that academic research,
and industry, will figure out ways
to reduce the amount of data and make it much,
much more sane,
in overall addressing this problem,
and make it easier to train in various locations.
But the reality is,
I think you're ultimately going to have models,
and training, and inference,
moved to many, many different locations.
And so you'll do inference at the Edge, on my phone,
or on a, you know, a little Bluetooth device
in the corner of my house saying whether or not
it's too hot or too cold.
We're going to to need that kind of intelligence,
and were going to do that kind of training and data collection
at the Edge.
>> Do you see the landscape evolving,
where you have specialty ML?
For instance, like the big conversation in IoT is move,
you know, compute to the data.
>> Yeah. >> Read that latency.
Do you see machine learning model moving around at code?
So, I can throw machine learning at a problem,
and is that Kubernetes fits in?
I'm trying to put together a mental model of,
how to think about how ML scales?
>> Yeah.
>> Can you, what's your vision on that?
How do you see that evolving?
>> Yeah, absolutely, I think that, you know,
going back to what we talked about at the beginning,
we're really moving to much more
of a solution-driven architecture today.
ML, you know, is great,
and the academic research is phenomenal.
But it is academic research.
It didn't really start to take off
until people invented things,
or you know created things, like,
ImageNet, and MobileNet and things like that,
that did very important things, like, object detection.
But then people, the, you know,
commercial researchers were able to take that,
and move that into locations
where people actually needed it.
I think you will continue to see that migration.
I don't think you're going to have single ML models
that do 100 different things.
You're going to have a single ML model,
that does a vertical specific thing,
anomaly detection in whatever factories,
and you're going to use that in a whole variety of locations,
rather than trying to, you know,
develop one ML model to solve them all.
>> So it's application specific or vertical.
>> Absolutely. >> All right, so that means
the data's super important. >> Absolutely.
>> Quality data, clean data is clean results,
Dirty data has bad results.
>> Absolutely right.
>> People have been in this kind of virtuous circle
of cleaning date, you know,
you guys know at Google, certainly Microsoft as well,
you know, data quality's critical,
but you got the horizontally-scalable cloud,
but you need specialism around the data and for the ML.
How do see that, is that,
I mean obviously it sounds like the right architecture--
>> Yeah, yeah. >> But this is where the,
the finesse is, and the nuance.
How do you see that?
>> So, you know, you bring up a really interesting point.
Today, the biggest problem is how much data there is, right?
It's not a matter of, um,
whether or not you're able to process it,
you are.
But it's so easy to get lost,
to get caught in little anomalies.
You know, if you have a petabyte of data,
and, whatever, a megabyte of it is the thing
that's causing your model to go sideways,
that's really hard to detect.
I think what you're seeing right now
is a lot of academic research,
which I'm very optimistic about,
that will ultimately reduce that,
that will both call out, "Hey, this particular data is,
"smells kind of weird, maybe take a closer look at this,"
or you will see a smaller need for training,
you know, where it was once a petabyte,
you're able to train on just 10 gigabytes.
I am very optimistic that both of those things happen,
and as you start get to that,
you get better signal-to-noise,
and you start saying, "Oh, in fact,
"this is questionable data,
"let's move that off to the side or spend more time on it,"
rather than what happens today which is,
"Oh, I got this model, and it works pretty well,
"I'm just going to throw everything at it,
"and trying, you know, get some answer out,
"and then we'll go from there."
>> And that's where a lot of false positives come in--
>> Absolutely. >> All that good stuff.
>> All right, so take it to the next level.
Here at KubeCon, CloudNativeCon,
in this community where Kubernetes is the center
of all these sets of services and building blocks,
where's the ML action?
What, if I want to jump in this community,
I'm watching this and go "Hey, you know what,
"I got Amazon Web Services, reinvent,
"just pumping up a lot of ML and AI,
"SageMaker and a bunch of other things."
What's going on in this community?
Where are the projects?
What are the notable things?
Where can I jump in and engage?
What's the map look like? >> Yeah.
>> How do I navigate?
>> Absolutely, so, obviously, I'm pretty biased,
you know, I helped start Kubeflow,
we're very, very excited about that.
>> So Kubeflow's one.
>> Yeah, absolutely,
but let me speak a little bit more broadly.
Kubernetes gives you this wonderful platform.
Highly scalable, incredibly portable,
and I can't overstate how valuable that portability is.
The reality is that customers have,
we talked about data a bunch already,
they have data on PRIM,
they have data in Cloud A, Cloud B,
it's everywhere, they want to bring it together.
They want to bring the training and the inference
to where the data is.
Kubernetes solves that for you.
It gives you portability,
it lets you abstract away the underlying stuff,
it gives you great scalability and reliability,
and it lets you compose
these highly complex pipelines together,
that let you do real training anywhere,
rather than having to take all your data,
and move it to a cloud,
and train on a single VM
that your not sure whether or not it's been updated or not,
this is the way to go.
>> Versus the old way, which was what?
'Cause that's an easier way,
orchestrating and managing that, what was the alternative?
>> The alternative was you built it yourself,
you piece together a whole bunch of solutions,
you wired it together,
you made sure that this service over here
had the right user account to access the data,
that that service over there was outputting,
it was just crazy town.
Now, you use Kubernetes constructs,
you use first class objects,
you extend the negative Kubernetes API,
and it works on your laptop,
and it works on Cloud A and B, and on PRIM,
and wherever you needed to,
on your training ready. >> That's the magic, basically.
>> Absolutely. >> All right.
So multi-cloud has come up a lot,
Hybrid cloud's the buzzword of the year,
I call that 2018, maybe '19 buzzword,
but I think the real endgame in all this is,
from a customer's standpoint,
that we're reporting on SiliconANGLE,
and theCUBE is, choice. >> Yep.
>> Multivendor is the new, multicloud is the,
multicloud's the modern version
of the old multivendor concept--
>> Yes. >> Which basically is choice.
>> Absolutely. >> So how does Kubernetes fit
into the mulitcloud, why is that good for the industry,
and what's your take on that,
can you share your prospective?
>> Absolutely, so when you go and look
at the recent RightScale reports,
81% of enterprises today are multicloud, full stop, 81%.
And not just one cloud.
They're on five different clouds,
that could on PRIM, could be Multi-Zone,
could be Google, or Amazon, or Azure.
>> Salesforce-- >> You name it.
>> They pay you to find cloud.
>> They're spreading, they're doing it
because that kind of portability
is right for their business.
Kubernetes gives you the opportunity
to operate on an abstraction layer
that works across all of these clouds.
So whether or not you're on your laptop,
and you're using Docker, or Minikube,
you're on your private training rig,
whether or not you go to GoogleCloud or Azure,
on GoogleCloud you get E, Azure you have AKS.
These, you're able to build CI/CD systems,
continuous delivery systems,
that use common Kubernetes constructs:
I want to roll this application out,
I want there to be seven pods,
I want it to have an end point that looks like this,
and that works anywhere
you have a Kubernetes Conformance Cluster.
And when it gets to really complex apps,
like machine learning,
you're able to do that at an even higher level,
using constructs like Kubeflow,
and all the many, many packages that go into Kubeflow.
We have NVIDIA contributing, and Uber.
We have, you know, Intel,
and I mean just countless,
Cisco, you know, I hesistate to keep naming names,
because I'll be here all day, but we have, literally,
over a hundred contributors. >> I mean Cisco is great,
tailwind for Cisco,
they're going to have network for every,
everybody wins.
The CI/CD sides for developers, one common construct,
the network guys get more pro--
If you decompose an application--
>> Absolutely.
>> The network ties it together,
so everybody wins in the stack.
>> Absolutely, and I think hybrid is really interesting,
you know, hybrid kind of gets a dirty word,
People are like, "Oh my God, you know,
"Why would you ever deploy to multiple clouds?
"Why would you ever spread across multiple clouds?"
And that, I agree with.
A true hybrid deployment today, isn't,
"Well, I'm going to take my app,
"and I'm going to spread it across six different locations."
In fact, what you really want to do,
is have isolated deployments to each place,
that enables you, in a single button,
deploy to all three of these locations.
But, to isolate them,
to have this particular application go,
and if, you know, AWS has an outage, GCP is there,
or if GCP has an outage, Azure is there,
and you can do that very readily,
or you can bring it close for geographic reasons,
or legal reasons, or whatever it might be.
Those kind of flexibility,
that ability to take a single construct
of your application,
and deploy it to each one of these locations,
not spreading them, but in fact,
just giving you that flexibility,
gives you pricing power, gives you flexibility,
and lets you take advantage of what data's in that cloud.
>> If you have an operating model.
>> Exactly. >> If the CI/CD is calm,
and that's the key.
Value right there. >> Absolutely right.
>> David, thanks so much from coming on theCube.
As usual, great commentary, great insight there,
been there from the beginning.
Just final question, predictions for 2019,
(David chuckling) in Kubernetes,
What's going to happen in 2019 with Kubernetes,
what's your prediction?
>> Well, I think you've heard this message
over and over again.
You're seeing Kubernetes become boring,
and that is incredibly powerful,
the stability, the flexibility,
People are building enormous businesses on top of it,
but not just that.
They're also continuing to build things
like the Custom Resource Definition,
which lets you extend Kubernetes in a safe and secure way,
and that's incredibly important.
That means you don't have to go and check in code
into the main tree in order to make extensions.
You're able to build on top of it,
and you're seeing more and more business
build great solutions, customer focused solutions.
>> Well, next time we get together,
I want to do a drill down on what the word stack means,
(David laughing) I've heard people say Kubernetes stack,
I'm like, yeah, they love the stackware,
it's not a stack anymore, sets of services.
David, thanks so much for coming on, appreciate it.
Hear the Kube coverage, live here in Seattle for KubeCon,
CloudNativeCon I'm John Furrier,
with Stu Miniman, be back with more
after this short break.
(upbeat electronic music)