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>> Live from Las Vegas,
it's theCUBE
covering AWS re:Invent 2018.
Brought to you by Amazon Web Services,
Intel,
and their ecosystem partners.
>> Okay, welcome back everyone.
Live coverage here in Las Vegas for Amazon re:Invent 2018.
Day three, we're winding down over 150 videos.
We'll have over 500 clips.
Losing the voice.
Dave Vellante, my co-host.
Suzi analyst tech that we're going to extract theCUBE insights,
James Kobielus.
David Floyer from Wikibon.
Jim you've been prolific on the blogs,
Siliconangle.com, great stories.
David you've got some research.
What's your take?
Jim, you're all over what's going on in the news.
What's the impact?
>> Well I think what this years re:Invent shows
is that AWS is doubling down on A.I.
If you look at the sheer range
of innovative A.I. capabilities they've introduced
into their portfolio, in terms of their announcements,
it's really significant.
A. They have optimized tense or flow for their cloud.
B. They now have an automated labeling,
called Ground Truth, labeling capability
that leverages mechanical turf,
which has been an Amazon capability for a while.
They've also got now the industries first,
what's called reinforcement learning plug-in
to their data science tool chain, in this case Sage Maker,
reinforcement learning is becoming so important
for robotics, and gaming, and lots of other applications
of A.I., and I'm just scratching the surface.
So they've announced a lot of things,
and David can discuss other things,
but I'm seeing the depth of A.I.
Their investment in it shows
that they've really got their fingers
on what enterprises are doing,
and will be doing to differentiate themselves
with this technology over the next five to ten years.
>> What's an area that you see that people are getting?
Clearly A.I.
What areas are people missing
that's compelling that you've observed here?
>> When you say people are missing,
you mean the general...?
>> Journalists.
>> Oh.
>> Audience.
There's so much news.
>> Yeah. Yeah.
>> Where are the nuggets that are hidden in the news?
(laughing)
What are you seeing that people might not see
that's different?
>> Getting back to the point I was raising,
which is that robotics is becoming a predominant application
realm for A.I.
Robotics, outside the laboratory,
or outside of the industrial I.O.T.,
robots are coming into everything,
and there's a special type of A.I.
you build into robots,
re-enforcement learning is a big part of it.
So I think the general, if you look at the journalists,
they've missed the fact that I've seen in the past
couple of years, robotics and re-enforcement learning
are almost on the verge of being mainstream in the space,
and AWS gets it.
Just the depth of their investments.
Like Deep Racer, that cute little autonomous vehicle
that they rolled out here at this event, that just shows
that they totally get it.
That will be a huge growth sector.
>> David Floyer, outpost is their on premises cloud.
You've been calling this for I don't know how many years,
>> (laughing) Three years.
>> Three years? >> Yeah.
What's the impact?
>> And people said, no way Foyer's wrong (laughing).
>> So you get vindication but...
>> And people, in particular in AWS. (laughing)
>> So you're right.
So you're right, but is it going to be out in a year?
>> Yeah, next in 2019.
>> Will this thing actually make it to the market?
And if it does what is the impact?
Who wins and who loses?
>> Well let's start with will it get to the market?
Absolutely.
It is outposts, AWS Outposts, is the name.
It is taking AWS in the cloud and putting it on premise.
The same API's.
The same services.
It'll be eventually identical between the two.
And that has enormous increase in the range,
and the reach that AWS and the time
that AWS can go after.
It is a major, major impact on the marketplace,
puts pressure on a whole number of people,
the traditional vendors who are supplying
that marketplace of the moment,
and in my opinion it's going to be wildly successful.
People have been waiting that, wanting that,
particularly in the enterprise market.
They reasons for it are simple.
Latency, low latency, you've got to have
the data and the compute very close together.
Moving data is very, very expensive over long distances,
and the third one is many people want,
or need to have the data in certain places.
So the combination is meeting the requirements,
they've taken a long time to get there.
I think it's going to be,
however wildly successful.
It's going to be coming out in 2019.
They'll have their alpha, their betas
in the beginning of it.
They'll have some announcements, probably about mid 2019.
>> Who's threatened by this?
Everybody?
Cisco?
HP?
Dell?
>> The integration of everything, storage, networking,
compute, all in the same box is obviously a threat
to all suppliers within that.
And their going to have to adapt to that pretty strongly.
It's going to be a declining market.
Declining markets are good if you adapt properly.
A lot of people make a lot of money from,
like IBM, from mainframe.
>> It's a huge threat to IBM.
>> You're playing it safe.
You're not naming names.
(laughing)
Okay, I'll rephrase.
What's your prediction?
>> What's my prediction on?
>> Of the landscape after this is wildly successful.
>> The landscape is that the alternatives is going to be
a much, much smaller pie,
and only those that have volume,
and only those that can adapt to that environment
are going to survive.
>> Well, and let's name names.
So who's threatened by this?
Clearly Dell, EMC, is threatened by this.
>> HP.
>> HP, New Tanix,
the VX rat guys, Lenovo is in there.
Are they wiped out?
No, but they have to respond.
How do they respond? >> They have to respond, yeah.
They have to have self service.
They have to have utility pricing.
They have to connect to the cloud.
So either they go hard after AWS, connecting AWS,
or they belly up to Microsoft
>> With Azure Stack, >> Microsoft Azure.
that's clearly going to be their fallback place,
so in a way, Microsoft with Azure Stack is also threatened
by this, but in a way it's goodness for them
because the ecosystem is going to evolve to that.
So listen, these guys don't just give up.
>> No, no I know.
>> They're hard competitors, they're fighters.
It's also to me a confirmation of Oracle's
same same strategy.
On paper Oracle's got that down, they're executing on that,
even though it's in a narrow Oracle world.
So I think it does sort of indicate that that iPhone
for the enterprise strategy is actually quite viable.
If I may jump in here, four things stood out to me.
The satellite as a service, was to me amazing.
What's next?
Amazon with scale,
there's just so many opportunities for them.
The Edge, if we have time.
>> I was going to talk about the Edge.
>> Love to talk about the Edge.
The hybrid evolution, and Open Source.
Amazon use to make it easy
for the enterprise players to complete.
They had limited sales and service capabilities,
they had no Open Source give back,
they were hybrid deniers.
Everything's going to go into the public cloud.
That's all changed.
They're making it much, much more difficult,
for what they call the old guard, to compete.
>> So that same way the objection?
>> Yeah, they're removing those barriers, those objections.
>> Awesome. Edge.
>> Yeah, and to comment on one of the things
you were talking about, which is the Edge,
they have completely changed their approach to the Edge.
They have put in Neo as part of Sage Maker,
which allows them to push out inference code,
and they themselves are pointing out
that inference code is 90% of all the compute, into...
>> Not the training.
>> Not the training,
but the inference code after that,
that's 90% of the compute.
They're pushing that into the devices at the Edge,
all sorts of architectures.
That's a major shift in mindset about that.
>> Yeah, and in fact I was really impressed
by Elastic Inference for the same reasons,
because it very much is a validation
of a trend I've been seeing in the A.I. space
for the last several years, which is,
you can increasingly build A.I. in your preferred
visual, declarative environment with Python code,
and then the abstraction layers of the A.I. Ecosystem
have developed to a point where,
the ecosystem increasingly will auto-compile
to TensorFlow, or MXNet, or PyTorch,
and then from there further auto-compile
your deployed trained model to the most efficient format
for the Edge device, for the GP, or whatever.
Where ever it's going to be executed,
that's already a well established trend.
The fact that AWS has productized that,
with this Elastic Inference in their cloud,
shows that not only do they get that trend,
they're just going to push really hard.
I'm making sure that AWS, it becomes in many ways,
the hub of efficient inferencing for everybody.
>> One more quick point on the Edge, if I may.
What's going on on the Edge reminds me of the days
when Microsoft was trying to take Windows
and stick it on mobile.
Right, the windows phone.
Top down, I.T. guys coming at it,
>> Oh that's right.
>> and that's what a lot of people are doing today in IT.
It's not going to work.
What Amazon is doing see, we're going to build
an environment that you can build applications on,
that are secure, you can manage them
from a bottoms up approach.
>> Yeah. Absolutely.
>> Identifying what the operations
technology developers want.
Giving them the tools to do that.
That's a winning strategy.
>> And focusing on them producing the devices,
not themselves.
>> Right.
>> And not declaring where the boundaries are.
>> Spot on.
>> Very very important.
>> Yep.
>> And they're obviously inferencing,
you get most value out of the data if you put that
inferencing as close as you possibly can to that data,
within a camera, is in the camera itself.
>> And I eluded to it earlier,
another key announcement from AWS here is,
first of all the investment in Sage Maker itself
is super impressive.
In the year since they've introduced it,
look at they've already added,
they have that slide with all the feature enhancements,
and new modules.
Sage Maker Ground Truth, really important,
the fully managed service for automating labeling
of training datasets, using Mechanical Turk .
The vast majority of the costs in a lot of A.I. initiatives
involves human annotators of training data,
and without human annotated training data
you can't do supervised learning,
which is the magic on a lot of A.I,
AWS gets the fact that their customers
want to automate that to the nth degree.
Now they got that.
>> We sound like Fam boys
(laughing).
>> That's going to be wildly popular.
>> As we say, clean data makes good M.L.,
and good M.L. makes great A.I.
>> Yeah.
(laughing)
>> So you don't want any dirty data out there.
Cube, more coverage here.
Cube insights panel, here in theCUBE at re:Invent.
Stay with us for more after this short break.
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