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>> Hello everyone.
Welcome to Silicon Angle news breaking story here.
Amazon Web Services, expanding their relationship
with Hugging Face, breaking news here on Silicon Angle.
I'm John Furrier, Silicon Angle reporter, founder
and also co-host of theCUBE.
And I have with me Swami
from Amazon Web Services, vice president
of database analytics machine learning with AWS.
Swami, great to have you on for this breaking news segment
on AWS's big news.
Thanks for coming on, taking the time.
>> Hey John, pleasure to be here.
>> We've had many conversations on theCUBE over the years.
We've watched Amazon really move fast
into the large data modeling.
You SageMaker became a very smashing success.
Obviously you've been on this for a while,
now with Chat GPT, open AI,
a lot of buzz going mainstream,
takes it from behind the curtain, inside the ropes,
if you will, in the industry to a mainstream.
And so this is a big moment I think in the industry.
I want to get your perspective
because your news with Hugging Face, I think is a
is another tell sign that we're
about to tip over into a new accelerated growth
around making AI now application aware
application centric, more programmable, more API access.
What's the big news about with AWS Hugging Face,
you know, what's going on with this announcement?
>> Yeah, first of all,
they're very excited to announce our expanded collaboration
with Hugging Face because with this partnership,
our goal, as you all know, I mean Hugging Face
I consider them like the GitHub for machine learning.
And with this partnership, Hugging Face and AWS
will be able to democratize AI for a broad range
of developers, not just specific deep AI startups.
And now with this we can accelerate the training,
fine tuning, and deployment of these large language models
and vision models from Hugging Face in the cloud.
So, and the broader context, when you step back
and see what customer problem we are trying to
solve with this announcement, essentially
if you see these foundational models are used to now create
like a huge number of applications,
suggest like tech summarization, question answering,
or search image generation, creative, other things.
And these are all stuff we are seeing
in the likes of these Chat GPT style applications.
But there is a broad range of enterprise use cases
that we don't even talk about.
And it's because these kind
of transformative generative AI capabilities
and models are not available to,
I mean, millions of developers.
And because either training these elements
from scratch can be very expensive or time consuming
and need deep expertise, or more importantly,
they don't need these generic models.
They need them to be fine tuned for the specific use cases.
And one of the biggest complaints we hear is that these
models, when they try to use it for
real production use cases,
they are incredibly expensive to train
and incredibly expensive to run in (indistinct),
to use it at a production scale, so
And unlike search, web search style applications where
the margins can be really huge,
here in production use cases and enterprises,
you want efficiency at scale.
That's where a Hugging Face and AWS share our mission.
And by integrating with training (indistinct),
we're able to handle the cost efficient
training and inference at scale.
I'll deep dive on it and
by training teaming up on the SageMaker front now
the time it takes to build these models
and fine tune them as also coming down.
So that's what makes this partnership very unique as well.
So I'm very excited.
>> I want to get into the, to the time savings
and the cost savings as well on the
on the training and inference.
It's a huge issue.
But before we get into that,
just how long have you guys been working with Hugging Face?
I know this is a previous relationship.
This is an expansion of that relationship.
Can you comment on the
what's different about what's happened before and then now?
>> Yeah, so Hugging Face, we have had an great relationship
in the past few years as well where they have actually
made their models available
to run on AWS in a fashion,
even inspect their Bloom project was something
many of our customers even used.
Bloom Project for context is their open source project,
which builds a GPT three style model.
And now with this expanded collaboration,
now Hugging Face selected AWS for that next generation
of this generative AI model, building on their highly
successful Bloom project as well.
And the nice thing is
now by direct integration with Trainium and Inferentia,
where you get cost savings in a really significant way.
Now for instance, tier 1 can provide up to
50% cost to train savings,
and Inferentia can deliver up to 60% better costs
and Forex more higher throughput.
Now these models, especially as they train
that next generation generated AI model, it is going to
be not only more accessible to all the developers who
use it in open.
So it'll be a lot cheaper as well.
And that's what makes this moment really exciting
because yeah, we can't democratize AI unless we make it
broadly accessible and cost efficient,
and easy to program and use as well.
>> Okay, thanks Swami. We really appreciate.
Swami's a Cube alumni, but also vice President,
database analyst machine learning web services
breaking down the Hugging Face announcement.
Obviously the relationship
he called it the GitHub of machine learning.
This is the beginning of what we will see,
a continuing competitive battle with Microsoft.
Microsoft launching OpenAI.
Amazon's been doing it for years.
They got Alexa, they know what they're doing.
It's going to be very interesting to see
how this all plays out.
You're watching Silicon Angle News, breaking here.
I'm John Furrier, host of the Cube. Thanks for watching.
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