[MUSIC PLAYING]
CHEN GOLDBERG: Good afternoon.
Thank you so much for joining us for the Operations and Platform
Engineering Spotlight session.
Are you ready to have some fun?
SPEAKER: Yeah.
CHEN GOLDBERG: Yes!
[CHEERING]
I'm Chen Goldberg, and I'm the VP and GM of Containers
and Kubernetes, and I'm super excited to be here today
we're going to dive into a range of topics
from building AI applications and platform to equipping
engineers with AI tools.
Plus, we will share with you how you can optimize security
and cost control across your environments, both AI
and traditional workloads.
But before I start, I want to take a moment
to celebrate a major milestone for Kubernetes.
This year marks the 10th anniversary
of the very first commit to the Open Source project.
[APPLAUSE]
And I guess this screenshot that we're going to see here
is probably the first photograph of Kubernetes.
Pretty photogenic, I think.
The evolution of Kubernetes has been nothing but extraordinary.
And now, we are supporting the explosive world of AI.
And this is where really the new fun begins.
AI is yet another modern workload.
And the investments we have made in containers and developer
experience benefit them as well.
So you're probably asking, so where does the AI fit?
When new technologies are introduced,
it requires a new platform.
Take the move from physical to virtual machines,
then from VMs to containers.
However, similar to Kubernetes, North Star,
our container management platform design principle,
has always been to support all of you,
our builders, our developers, today and in the future.
We can now support AI applications
being another modern workload on the platform
along other modern workloads.
And this is why this time, you don't
need to spin off a big migration or integration project.
With our container management platform,
you can accelerate your AI journey
without reinventing the wheel and building a new platform.
Our container management platform
includes both GKE and Cloud Run.
It is built from the ground up with flexibility, optionality,
security, and cost optimizations.
This way, the needs of every company
from the AI unicorn to the traditional enterprise are met.
Where you are dictates where you start,
but no choice is a one way door.
Sometimes, especially when you're just at the beginning,
you prefer to optimize the time to building the application
and not worrying about the infrastructure.
So if you're looking for the easiest way
to build applications, point your developers
to Cloud Run for optimized productivity.
And you wouldn't believe it.
Today, we are making things even easier.
I'm excited to announce Cloud Run application
canvas, a Gemini-assisted experience
to generate, modify, and deploy Cloud Run applications.
Simply describe what you want to build in natural language
and the application will be generated for you.
The canvas also builds out integration to various Google
Cloud services, including Firebase Hosting, Vertex AI,
Memorystore, and more.
And it's really saving you time and effort.
So we would like to keep things real.
Let's invite Bobby from our product team for the first demo
and see how easy it is.
Woo!
[MUSIC PLAYING]
BOBBY ALLEN: Hello, everybody.
Thank you all for being here today.
So Chen, if I was following backstage,
I think you talked about containers, serverless, and AI
so far.
And I think it's about time to do a demo that combines
all three of those things.
What do you all think?
[CHEERING]
CHEN GOLDBERG: Woo!
BOBBY ALLEN: Excellent, excellent.
Love the enthusiasm.
So the application canvas gives you
a visual way to create an application in two
different ways.
One, you can use natural language
to describe what you want.
And behind-the-scenes, that intent
is translated into potential architecture by Gemini.
So we might say something like this, for example.
"I want a GenAI application with a SQL database
deployed behind example.com."
And so Gemini is going to take this intent
and turn this into the building blocks
to build a real world application that
looks something like this.
So my application components are launched
because Gemini took what I wanted to do and just built it.
And so the thing that's cool about the application canvas
is when I hit this button, it automatically
does an audit of all the APIs that need to be enabled,
and all the role permissions that need to be granted.
I literally have Gemini looking over my shoulder
to see all the things that need to work together
to just make it work.
It's pretty cool, right?
So let's speed ahead to the finish
deployed application architecture.
So these checkboxes tell me that all of these components
are actually live and working.
And to prove it, we're going to bring up a working application
that we deployed to our Cloud Run service.
So we built a simple Python application kind of offline,
and what we're going to do now is bring up
the front end for that that looks something like this.
So we've built an application in Cloud Run,
deploy it to Cloud Run.
It's going to talk to Gemini, and what we're going to do
is bring up pictures of some of the creative folks who've
pushed us in the past to achieve more in the future.
And so one of my personal favorites is Katherine Johnson.
And so what's going to happen is when
I click on this picture, it's going
to send that image to Gemini, and those who don't know
that Katherine Johnson is one of the famous NASA scientists
from the movie "Hidden Figures," Gemini will tell us who this is.
And so with tools a lot less sophisticated
than what we have today, she was part
of the team of women at NASA that took a man to the moon
and brought him back safely.
And so the cool thing about this is with Cloud Run application
canvas, we can build things like this, push our code,
and keep pushing the boundaries of what's possible.
So as we go back to the application canvas one
last time, I want you to think about the application
canvas as another tool in your tool belt
when you want to channel your inner doctor Katherine
Johnson, your inner innovator and make
what we thought was impossible tomorrow a reality today.
CHEN GOLDBERG: That's cool, Bobby.
Thank you so much.
[APPLAUSE]
Don't tell anyone.
This is probably one of my favorite things
we announced this week.
So especially when you want to move fast
and you don't want to worry about anything,
Cloud Run is the right choice.
Today, we are also announcing Cloud Run support
for long chain, which is a powerful, open source framework
for building application.
Now, if you're looking for more control and flexibility,
GKE got you covered.
GKE is fully manageable, but highly customizable.
It has both the flexibility you need to make Kubernetes your own
and to build your platform on ours.
And you have everything that you need
to make it enterprise ready with the built-in control
Google-grade automation and reliability.
It is also available on Google Cloud, other clouds,
or on premises through Google Distributed Cloud.
We have put years of our insights
and sweat and best practices into Kubernetes and GKE.
And their are fundamental capabilities make them
best suited for AI workloads.
Kubernetes really fuels innovation
with its extensive ecosystem of tools,
while seamless infrastructure abstraction
lets teams harness the latest technologies
like GPUs, time sharing, and TPUs for maximum efficiency.
Today, I'm excited to announce GKE support
for Cloud TPU v5p is now GA.
[APPLAUSE]
Cloud TPU v5p is our most powerful and scalable TPU
accelerator to date.
With TPU v5p, you can train your LLMs in weeks,
instead of months with a 2.8 times
improvement in training time over the previous version.
While GPUs and TPUs are great for scale, as AI models grow,
customers face challenges such as cost effectiveness and start
times of massive container images.
To help address both of these issues,
GKE now supports custom container image preloading
and model preloading.
Container image preloading helped
Vertex AI prediction service, which is built on GKE,
pull large container images up to 29 times faster
than their previous method.
And yes, you heard me right.
We at Google, we like to drink our own champagne.
And GKE powers AI products like Vertex AI
and the next generation of AI innovation with DeepMind
So when you run and scale AI workloads,
GKE makes obtaining GPU for AI training workloads
easy by using dynamic workload scheduler.
Together, with an open source project
called Kueue, which is a queue with a K,
you can automatically request resources
that are optimal for your jobs.
This makes sure you can take advantage of available TPU
and GPU capacity.
It's not surprising that thanks to all
of this innovation over the last year, the use of GPUs and TPUs
on GKE has grown more than 900% last year.
I really hope my excitement comes through.
It's not every day there is such an amazing transformation
happening in our industry, and we can all
contribute to that success.
And with new capabilities come new possibilities.
We are honored to partner with all of you on this journey,
and we can't wait to see what we can build together.
Thank you so much for joining me.
[APPLAUSE]
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