>> From around the globe.
It's the CUBE, with digital coverage
of Dell Technologies World Digital Experience
brought to you by Dell Technologies.
>> Welcome to the CUBEs coverage
of Dell Technologies World 2020,
the Digital Experience.
I'm Lisa Martin, and I'm pleased to welcome back
a CUBE alumni and a new CUBE member
to the program today.
Chhandomay Mandal is back with us
director of solutions marketing
for Dell Technologies, Chhandomay,
it's great to see you at Dell Technologies World,
even though we are very socially distant.
>> Happy to be back, thank you Lisa.
>> And Thomas Henson is joining us
for the first time,
at global business development manager
for AI and analytics.
Thomas, welcome to the CUBE.
>> Hey, I'm excited to be here,
it's my first virtual CUBE.
>> Yeah, well you better make it a good one.
All right guys.
So we're here to talk about AI.
So, so much has changed
and Chhandomay the last time I saw you,
we probably were sitting a lot closer together.
So much has changed in the last six,
seven months, but a lot has changed
with the adoption of AI, Thomas kick us off.
What're some of the big things
fueling AI adoption, right now.
>> Yeah, I would have to say
the two biggest things right now
or as we look at accelerated compute
and by accelerated compute,
we're not just talking about
the continuation of Moore's law,
but how in data analytics,
we're actually doing more processing now
with GPUs, which give us faster insights.
And so now we have the ability
to get quicker insights
in jobs that may have taken weeks to months,
as long as we were measuring.
And then the second portion is when we start
to talk about the innovation
going on in the software
and framework world, right?
So no longer do you have to know C plus plus
or a lower level language,
you can actually do it in Python
and even pull it off of GitHub,
and it's all part of that open source community.
So we're seeing more,
more folks in the field of data science
and deep learning
that can actually implement some code,
and then we've got faster compute
to be able to process that.
>> Chhandomay what are your thoughts.
>> Thing I want to add is
the explosive growth of data,
and that's actually fueling the AI adoption.
Think of like all the
(indistinctive)
Devices we have, IOT,
and its devices, doing data are pumping data
into the pipeline, high resolution,
satellite imagery,
all social media generating data.
Now all of these data
are actually helping the adoption of AI
because, now we have very granular data
to or train the AI model, make the AI models,
much better besides,
so, the combination of both in,
data the power of like GPU powered solvers
are coupled with the inefficience
in the AI software and tools helping fuel
the AI growth that we are seeing today.
>> Chhandomay, one of the things
that we've known for a while now is that
it's, for AI to be valuable,
it's about extracting value from that data.
You talked about the massive explosion
and data, but yeah, we know for a long time
we've been talking about AI for decades,
initiatives can fail.
What can Dell Technologies do now
to help companies have successful AI projects?
>> Yeah, so as you were saying, Lisa,
what we are seeing is the companies
are trying to add up AI technologies
to drive value
and extract value from their data set.
Now, we, it needs to be framed
is there is a business challenge that customers
are trying to solve.
The business challenge gets transformed
into a data science problem that data scientist
is going to work with AI technology trend,
the model, that data science problem
gets to that data science solution,
and then it needs to be mapped
to production deployment as a business solution.
What happens a lot of the time is,
the companies do not plan for how to transition
from all scale, proof of concept
that data scientists are playing with
like a smaller set of data,
to when it goes to
the large production deployment
dealing with terabytes to petabytes of data.
Now that's where we come in.
At Dell Technologies
we have end to end solutions
for the AI solution
in the customer's journey,
starting from a proof of concept to production.
And it is all seamless modular and very scalable.
>> So, if some of the challenges
there're just starting with iterations,
Thomas question for you
as business development manager,
those folks that Chhandomay talked about
the data scientists, the business,
how are you helping them
come together from the beginning
so that when the POC is initiated,
it actually can go on the right trajectory
to be successful.
>> Now, that's a great point
and just to kind of build off
of what Chhandomay was talking about,
we call it that last mile, right?
Like, hey, I've got a great POC,
how do I get into production?
Well, if you have executive sponsorship
and it's like, hey, everybody was on board,
but it's going to take six months to a year,
it's like, whoa, whoa, whoa
you're going to lose some momentum.
So where we help our customers is,
by partnering with them
to show them how to build,
from an IT and a infrastructure perspective,
what that AI architecture looks like, right?
So we have multiple solutions around that,
and at the end of the day,
it's about just like Chhandomay was saying,
we may start off with a project
that maybe it's only half a terabyte,
maybe it's 10 terabytes,
but, once you go into production,
if it turns out
to be three petabytes, four petabytes,
nobody really, has the infrastructure built
unless they built on those solid practices.
And that's where our solutions come in.
So we can go from small scale laboratory
all the way to large scale production
without having to move any of that data, right?
So, at the heart of that is PowerScale
and giving you that ability to scale your data
and no more data migrations.
So that you can handle one PC or multiple PCs
as those models continue to improve
as you start to move into production.
>> Yeah >> And Thomas sticking
with you for a second,
oh, sorry Chhandomay go ahead.
>> So, I was going to add that,
just like how has Thomas said, right?
So if you are a data scientist,
you are working with this data science
work stations, but getting the data
from LEMC PowerScales,
the scale out platform,
and as it is growing from POC
to large scale production,
data can stay in place
with the PowerScale platform.
You can add notes and it can grow two petabytes
and you can add in not just the work stations,
but also Dell PowerEdge Servers, switches
building out our entire AI ready solutions
as AI ready solution for your production.
Even a very seamless experience
from the data scientist with the IT.
>> So, Chhandomay we'll stick with you then.
I'm curious to know in the last six
to seven months,
since 2020 has gone in a very different direction
than we all would have predicted
our last Dell Technologies World together.
What are you seeing Chhandomay
in terms of acceleration
or maybe different industries?
What're customers needs, how they changed?
I guess I should say, in the last, in 2020.
>> So, in 2020, we are seeing the adoption of AI
even more, rapidly.
If you think about customers ranging from,
like say, media and entertainment industry
to, the customer services of any organization
to, the healthcare and life sciences
where lots of, genome analysis going on
in all of these places,
where we are dealing with large data sets,
we are seeing a lot of adoption foster processing
of AI technologies, even with, say the,
all the research
that is biosciences organizations are happening.
Thomas, I know like you are working with
like a customer,
so, can you give us a little bit,
more example in there?
>> Yes, one of the areas,
if we're talking about 2020,
one of the things that we're seeing more
and more is just the expansion of just look at
the need for customer support, right?
There're more folks working remotely,
there're more folks that are learning remote.
I know my child is going through virtual school.
So think about your IT organization
and how many calls you're having now to expand.
And so this is a great area
where we're starting to see innovation within AI
and model building to be able to have,
let's call it,
the next generation of chatbots, right?
So you can actually build these models
off the data to augment those support systems
because you have two choices, right?
You can either,
expand out your call center, right?
For, we're not sure how long
or you can use AI and analytics
to help augment, to help maybe answer
some of those first baseline questions.
The great thing about customers
who are choosing PowerScale
and Dell Technologies their partner is,
they already have the resources
to be able to hold onto that data,
that's going to help them
train those models to help.
>> So, Thomas, whenever we're talking about data
and the explosions,
it brings to mind compliance, protection,
security, and we've seen ransomware
really skyrocket in 2020 just,
the other week there was the VA was hit,
I think there was also social media, Facebook,
Instagram, Tik Tok, 235 million users,
'cause there was an unsecured cloud database
so that
(indistinctive)
is expanding.
How can you help customers
accelerate their AI projects,
while ensuring compliance and protection
and security of that data.
>> Really, that's the sweet spot for PowerScale
when we're talking with customers, right?
Built on OneFS
with all the security features in mind.
And I too came from the analytics world.
So I remember in the early days of Hadoop
where, as a software developer,
we didn't need security, right?
We, were doing researchy stuff.
But then when we took it to the customer,
and we're pushing to production,
what about all the security features we needed,
same thing for artificial intelligence, right?
We want to, make sure
that we're putting those security features
and compliances in.
And that's where,
from an AI architecture perspective
by starting with OneFS
as at the heart of that solution,
you can know that you're protecting for,
all the enterprise features that you need
whether it be from compliance, to data strategy,
to backup and recovery as well.
>> So when we're talking about big data volumes,
Chhandomay we have to talk about the hyperscalers
talk to us about,
they each offer Azure, AWS, Google Cloud,
hundreds of AI services.
So how does Dell help customers
use the public cloud
the data that's created outside of it
and use all of those,
use the right AI services to extract that value.
>> Yeah, now, as you mentioned,
all of these hyperscalers,
they differentiate with, artifices like,
AI, ML, deep learning technologies, right?
And as a customer,
you want to leverage best of all the,
cloud has to offer
and not stuck with one particular cloud provider.
However, we are talking about terabytes of data,
right? So if you are happy
with doing service A from cloud provider,
say Google, but you want to move
to take advantage of another service
of from Azure, it comes with a very high.
(indistinctive)
Migration risk and time it will take
to move the data itself.
Now that's not good, right?
As the customer,
we should be able to leverage best-of-breed,
out services for AI, and for that matter
for anything across the board.
Now, how we help customers
is you can have all of your data,
say in a managed cloud service provider
running on PowerScale,
but then, you can connect
from this managed cloud service provider
directly to any of the hyperscalers.
You can connect to AWS, Azure, Google Cloud,
and even like even the interest analytics
that PowerScale offers.
You can run, those apps
I mean, run those cloud AI services
directly on that data simultaneously,
from these three.
And I'll add like one more thing, right?
Is deep learning technologies
need GPU powered solvers, right?
And cloud even within like one cloud
is not a homogeneous environment.
Like sometimes you'll find that U.S East
has all, GPU part solvers,
but, like you are in west
and same for other provider.
Now we, our, Dell Technologies Cloud PowerScale
for Multi-cloud, PowerScale is sitting outside
of those hyperscalers connected directly to,
any of this, and then you can burst
into different clouds,
take advantage of our spot instances,
and
like leverage all the GPUs
not from one particular service provider,
but all of those three are hyperscalers.
So those are some examples of the work
we are doing in the Multi-cloud World for AI.
>> So that's data, you're talking about data there
so PowerScale for multi-cloud for data
that's created outside the public cloud
but Thomas, what about for data that's created
inside the cloud?
How does Dell help with that?
>> Yeah, so this year
we actually released a solution
in conjunction with GCP,
so, within Google Cloud,
you can have PowerScale for OneFS, right?
And so that's that native feature.
So, goes through all the compliance
and all the features
within being a part of that GCP natively,
so counts towards your credits
and your Google billing as well,
but it's still all the features that you have.
And so we've been running some,
actually some benchmarks.
We've got a couple white papers out there
that kind of detail, what we can do
from an artificial intelligence perspective,
like the Chhandomay's example,
where he was talking about,
being able to use more and more GPU.
So we, we've done that to run
some of our AI Benchmarks against that,
and then also, jumped into the Hadoop space
because, that's one,
area from a PowerScale perspective customers
are really interested.
And they have been for years.
And then really the awesome portion about this
is for customers
that're looking for a hybrid solution,
or maybe it's their first kickoff to it.
So back Lisa to those compliance features
that we were talking about,
those are still inherent
within that native Google GCP OneFS version,
but then also for customers that have it on prem,
you can use those same features
to burst your data into your Isilon cluster
using all the same native tools
that you've been using for years
within your enterprise.
>> Got it, so starting out
for PowerScale for Google cloud,
Chhandomay back to you,
kind of wrapping things up here,
what are some of the things
that we're going to see next from Dell
from an AI solutions perspective?
>> Yes, so we are working on many different
interesting projects, renting from, the latest,
NVIDIA solvers that they have announced
DGX A 100, and in fact,
two weeks ago at a DTC,
NVIDIA announced the super parts with,
DGX A 100 solvers,
we're part of that ecosystem
and we are working with, the leading,
solutions to benchmark our AI environments
for all the storage,
ensuring like we are providing
like all the throughput and scalability,
that we have to offer.
>> And Thomas finishing with you
from a customer perspective
as we talked about so many changes
this year alone,
as we approach calendar year 2021,
what're some of the things that Dell is doing
with its customers, with its partners,
the hyperscalers and video for example,
do you think customers
are really going to be able to truly accelerate
successful AI projects?
>> Yeah, so the first thing
I'd like to talk about is
what we're doing with the DGX A 100.
So, this month at GTC, you saw our solution
for our reference architecture
for the DGX A 100 plus PowerScale,
so you talk about speed
and how we can move customers insights,
I mean, some of the numbers
that we're seeing off of that
are really amazing, right?
And so this is gives the customers
the ability to still,
take all the features and use Isilon and OneFS
like they have in the past,
but now combined with the speed of the A 100,
still be able to speed up
how fast they're using those,
building out those deep learning models.
And then secondly, with that,
that gives them the ability to scale too.
So there's some features inherent
within this reference architecture
that allow for you to make more use, right?
So bring more data scientists and more modelers,
GPUs because that's one thing you don't see
data scientists turning away, right?
They're always like, hey,
what I mean, this project here needs the GPU.
And so, from a powerScale OneFS perspective,
we want to be able to make sure
that we're supporting that,
so that as that data continues to grow
which we're seeing as one of the large factors,
whenever we're talking about
artificial intelligence is the scale for the data
we want to them to be able to continue
to build out that data consolidation area
for all of these multiple different workloads
that are coming in.
>> Excellent, Thomas, thanks for sharing that,
hopefully next time
I get to see you guys in person,
and we can talk about a customer
who has done something very successful
with you guys.
Chhandomay always great to talk to you.
Thank you for joining us.
>> Thank you.
>> Chhandomay Mandal and Thomas Henson,
I'm Lisa Martin.
You're watching the CUBEs coverage
of Dell Technologies World 2020.
(upbeat music)