>> From Sunnyvale, California,
in the heart of Silicon Valley, it's theCUBE,
covering Accelerate Your Journey to AI.
Brought to you by NetApp.
>> Hi, I'm Peter Burris, with theCUBE and Wikibon,
and we're here at the NetApp Data Visionary Center today
to talk about NetApp, NVIDIA, AI, and data.
We're being joined by two great guests.
Jim McHugh is the Vice President and General Manager
of Deep Learning Systems at NVIDIA,
and Octavian Tanase is the Senior
Vice President of ONTAP at NetApp.
Gentlemen, welcome to theCUBE.
>> Thanks for having me.
>> So Jim, I want to start with you.
NVIDIA's been all over the place regarding AI right now.
You've had a lot of conversations with customers.
What is the state of those conversations today?
>> Well, I mean, it really depends
on the industry that the customer's in.
So, AI at at its core, is really
a horizontal technology, right?
It's when when we engage with a customer and their data
and their vertical domain knowledge
that it becomes very specialized from there.
So you're seeing a lot of acceleration
where there's been a lot of data, right?
So it's not any secret that you're seeing
a lot around autonomous driving vehicles
and the activity going there.
Health care, right?
Because when you can marry the technology of AI
with the years, and years, and years
of medical research that's going on out there,
incredible things come out, right?
We've seen some things around looking at cancer cells,
we're looking at your retina being sort of
the gateway to so many health indications.
We can tell you whether you have everything
from Dengue fever, to malaria,
to whether you're susceptible to have hypertension.
All of these kind of things that we're finding,
that data is actually letting us to be superhuman
in our knowledge about what we're trying to accomplish.
Now the exciting thing is, if you grew up like we did,
in the IT industry, is you're seeing it go into mainstream
companies, so you're seeing it in financial services,
where they for years were, quants were very specialized,
and they were writing their own apps,
and now they figured out, hey, look,
I could broaden this out.
You're seeing it in cybersecurity, right?
For years, if you wanted to check malware, what did we do?
We looked up the definition in a database and said,
okay, yeah, that's malware, stop it, right?
But now, they're learning the characteristics of malware.
They're studying the patterns of it,
and that's kind of what it is.
Go industry by industry, and tell me if there's enough data
to show a pattern, and AI will come in and change it.
>> Enough data to show a pattern?
Well, that kind of introduces NetApp to the equation.
A company that's been, especially more recently,
very focused on the relationship
between data and business value.
Octavian, what has NetApp seen from customers?
>> Well, we know a little bit about data.
We've been the stewards of that data in the enterprise
for more than 25 years, and AI comes up
in every single customer conversation.
They're looking to leverage AI in their digital
transformation, so we see
this desire to extract more value
out of the data, and make better decisions, faster decisions
in every sector of the industry.
So, it's ubiquitous, and we are uniquely positioned
to enable customers to do their data management
wherever data is being created.
Whether the data is created at the edge,
in the traditional data center, what we call the core,
or in the cloud, we enable this seamless data management
via the data fabric architecture and vision.
>> So, data fabric, data management, the ability
to extract that, turn it into patterns.
Sounds like a good partnership, Jim?
>> Yeah, no, we say, data's the new source code.
Really, what AI is, we're changing
the way software's written.
Where, instead of having humans going in, do the feature
engineering and feature sets that would be required,
you're letting data dictate and guide you
on what the features are going to be of software.
>> So right now, we've got the GPU,
Graphic Data Processing revolution, you guys driving that.
We've got some real advances in how data fabric works.
You have come together and created a partnership.
Talk a little bit about that partnership.
>> Well, when we started down this journey,
and it began, really, in 2012 in AI, right?
So when Alex Krizhevsky discovered how to create AlexNet,
NVIDIA's been focused on how do we meet the needs
of the data scientists every step of the way.
So beginning started around making sure they had
enough compute power to solve things
that they couldn't solve before.
Then we started focusing on what is
the software that was required, right?
So how do we get them the frameworks they need?
How do we integrate that?
How do we get more tuned, so they could
get more and more performance?
Our goal has always been, if we can make the data scientists
more productive, we can actually help democratize AI.
As it's starting to take hold,
and get more deployments, obviously we need the data.
We need it to help them with the data ingest,
and then deployments are starting to scale out to the point
where we need to make this easy, right?
We need to take the headaches of trying to figure out
what are all the configurations between our product lines,
but also the networking product lines, as well.
We have to bring that whole,
holistic picture, and do it from there.
So our goal, and what we're seeing, is not only we've made
the data scientists more productive,
but if we can help the guys that have to do the equipment
for him more productive as well,
the data scientists, she and he, can get back to
doing what their real core work is.
They can add value, and really change a lot of the things
that are going on in our lives.
>> So fast, flexibility, simpler to use.
Does that, kind of, capture some of the,
summarize some of the strategies that NetApp has
for Artificial Intelligence workloads?
>> Absolutely, I think simplicity, it's one of the key
attributes, because the audience for some
of the infrastructure that we're deploying together,
it's a data scientist, and he wants to adopt that solution
with confidence, and it has to be simple to deploy.
He doesn't have to think about the infrastructure.
It's also important to have an integrated approach,
because, again, a lot of the data will be created
in the future at the core, or at edge more than in the core,
and more in the cloud than in traditional data center.
So that seamless data management across the edge,
to the core, to the cloud, it's also important.
And scalability, it's also important, because customers who
look to start, perhaps, simple, with a small deployment,
and have that ability to seamlessly scale.
Currently, the performance of the solution that we just
announced, basically beats the competition by a 4x,
in terms of the performance and capability.
>> So as we think about where we're going,
this is a crucial partnership for both companies,
and it's part of a broader ecosystem
that NVIDIA's building out.
How does the NetApp partnership fit
into that broader ecosystem?
>> Well, starting with our relationship,
when the announcement we made, it should be no secret
that we engaged our channel partners, right?
'Cause they are that last mile.
They are those trusted advisors, a lot of times,
of our customers, and going in,
and we want them to add this to their portfolio,
take it out to 'em, and I think we've had resounding
feedback, so far, that this is something that they can
definitely take, and drive out.
On top of that, NVIDIA is focused on, again,
this new way of writing software, right?
The software that leverages the data to do the things,
and so we have an ecosystem that's built around our
inception program, which are thousan%ds of startups.
If you add to that the thousands of startups that
are coming through Sand Hill, and the investment community,
that are based around NVIDIA compute, as well,
all of these guys are standardizing saying,
hey we need to leverage this new model.
We need to go as quickly as possible,
and what we've pulled together, together,
is the ability for them to do that.
So whether they want to do the data center,
or whether they want to go with one of our joint cloud
providers and do it through their service, as well.
>> So a great partnership that's capable
of creating a great horizontal platform.
It's that last mile that does the specialization.
Have I got that right?
>> You had the last mile helping reach
the customers who are the specialization.
The customers, and their data,
and their vertical domain expertise,
and what the data scientists that they have bring to it.
Look, they're creating the magic.
We're giving them the tools to make sure they can create
that magic as easy as possible.
>> That's great, so one of the things, Octavian,
that Jim mentioned, was industries that are able
to generate significant value out of data are moving first.
One of the more important industries is IT Operations,
because we have a lot of devices,
we're generating a lot of data.
How is NetApp going to use AI in your product set
to drive further levels of productivity, from a simplicity
standpoint, so customers can, in fact,
spend more time on creating value?
>> So interestingly enough, we've been users,
or practitioners, of AI for quite a while.
I don't know if a lot of people in the audience know,
we have a predictive analytics system called Active IQ,
which is an implementation of AI in the enterprise.
We take data from more than 300 thousand assets
that we have deployed in the field,
more than 70 billion data points every day,
and we correlate that together.
We put them in a data lake.
We train a cluster, and we enable our customers to drive
value in best practices from the data that we collect
from the broader set of deployments
that we have in the field, so this is something that we are
sharing with our customers, in terms of blueprint,
and we're looking to drive the ubiquity
in the type of solutions that we enable customers
to build on top of our joint infrastructure.
>> Excellent, Jim McHugh, NVIDIA,
Octavian Tanase, NetApp.
Great partnership represented right here on theCUBE.
Thanks very much for being on theCUBE tonight.
>> All right. >> Thank you.
>> Thank you for having us.
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