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RICHARD SEROTER: Hello, everybody.
Welcome to the Next 2024 Developer Keynote.
I'm Richard Seroter, the chief evangelist here at Google Cloud.
CHLOE CONDON: And I'm Chloe Condon, a chief developer
relations engineer for Google Cloud.
RICHARD SEROTER: At Google Cloud,
we want to meet you where you are, but, frankly, take you
further than anybody else can.
See, the future of development is giving you
smarter tools to build smarter apps
that run on smarter platforms.
I think Gemini is changing the way developers deliver software,
and we are here to prove it to you today.
So we're going to show you how Gemini makes coding better.
We're going to show you how it makes platforms better.
And we're going to show how you make ops better.
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BRAD CALDER: Let's start with Gemini Code Assist.
This is our cornerstone product for developing applications
with AI systems.
RICHARD SEROTER: Yep.
BRAD CALDER: And we're making Gemini Code Assist even better
by integrating the power of Gemini 1.5 Pro.
This upgrade brings a massive 1 billion
token context window, which is the largest in the industry.
RICHARD SEROTER: It is.
Yeah, this, I think, revolutionizes
how we all do coding.
All of a sudden, you can look at your whole code base
and add new features, do version upgrades, even
comprehensive code reviews.
It makes it all much better.
BRAD CALDER: Yesterday, we released Gemini Cloud Assist.
Gemini Cloud Assist makes it easier
to design, operate, troubleshoot, and optimize
your application.
JASON DAVENPORT: What's really cool about this
is that Gemini Cloud Assist knows about all
of our project features here.
It knows about our runtimes.
It knows about the health.
And it knows specifically what's in it.
And all of this is because we're using
App Hub to power this new experience, which
is now generally available.
BRAD CALDER: In Google Cloud, you expect world-class data
services.
And now, we're extending the AI journey to core developers
by bringing new Gemini in BigQuery.
With this, developers can build amazing analytics applications
and data agents with the help of rich multimodal AI.
This provides a new generation of AI assistants
and capabilities for building AI applications and agents
with Google Cloud.
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CHLOE CONDON: Now we're going to dive
deep into how to build, run, and operate with Gemini.
The first stop on our journey is how
to go from a great idea to an immersive, inspirational AI app
in just a few minutes using Google AI and Google Cloud.
FEMI AKINDE: Google AI Studio was great
because we proved that we could use multimodal input to get
place recommendations.
Since we want to build an application,
we need to shift over to Google Cloud to build it out,
which gives us more enterprise-grade controls
and capabilities.
And since we're going to write some code,
we'll need an ID to code it.
So, Chloe, could you please go over
to Cloud Workstations from Google Cloud?
CHLOE CONDON: Sure.
FEMI AKINDE: Here, you can use a hosted and managed development
environment that provides developers their ID of choice.
It's great that in under a minute,
we've moved from validating our idea to being ready in our ID.
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CHLOE CONDON: So let's bring one of the creators of Next.js
on stage to show us how it's done.
Please welcome the CEO of Vercel, Guillermo Rauch.
GUILLERMO RAUCH: Our goal at Vercel
is to make it very easy to create and publish
applications on the web.
We developed the AI SDK to simplify adding AI
to any web application.
All you need to know is React, Svelte, or VUE.
If you're creating an XJS app, you just
need to bring in the AI SDK, your idea,
and your model of choice--
Gemini.
It lays the foundation for the next wave of AI-native user
experiences.
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RICHARD SEROTER: So, Kaslin, look,
I need a production grade generative AI agent or app.
How do I make it?
KASLIN FIELDS: Let's dive in.
RICHARD SEROTER: OK.
KASLIN FIELDS: So a generative AI agent
is essentially an app stack like any other,
but I'll show you the differences as we go.
For this demo, we're going to build a better chat
experience for Cymbal Shops.
RICHARD SEROTER: OK.
KASLIN FIELDS: Using generative AI
enabled Cymbal Shops to provide product recommendations based
on images of the customer's own home.
Let's talk about the components of our system.
RICHARD SEROTER: OK.
KASLIN FIELDS: You can think of these like services
in a modern app stack.
RICHARD SEROTER: But actually the most curious thing I have
is in the database.
How does that really support RAG?
We've heard about it, but can you show this in action?
KASLIN FIELDS: Some of this information
is also the vector embeddings, the vector representations
of our product descriptions.
And these vectors can represent text or image meaning
in the same vector space.
Let's talk about the application framework.
RICHARD SEROTER: OK.
KASLIN FIELDS: For that, I'm going
to tag in someone who's app dev work I really admire.
Please help me welcome Spring developer advocate
from Broadcom, Josh Long.
JOSH LONG: Well, you know, Richard,
I like to talk fast and code fast and make
production containers fast.
So let's do this.
RICHARD SEROTER: This was actually
using modern native Java to talk to a database,
augment a request, and then give us
back a lightning-fast response.
You can use GraalVM images from Cloud Run,
and you have containerized images.
It's amazing.
I love it.
And I think you have some things you want to show us,
things you're excited about when it comes to Gemini
making platforms better.
CHEN GOLDBERG: We think AI can help us create and run
modern apps.
Look at this new Canvas we are now shipping in preview.
Using Gemini Cloud Assist, I can ask for the architecture I want
and get back the right components
with default settings, including the container
image used for Cloud Run.
And do you want to see something even cooler?
RICHARD SEROTER: You know I do.
CHEN GOLDBERG: So we've just enabled multi-region deployments
so that you can deploy this application globally.
And you know what's interesting is that, with AI workloads,
some of the new challenges that our customers are seeing now
can uniquely be addressed through the power of GKE.
For example, we've added new support for the Ray framework
so that you can train and serve AI models more easily with GKE.
CHLOE CONDON: A lot of teams out there are
getting ready to ship their first LLMs to production
but might not know where to start
or what's needed when they get here.
A little help?
STEVE MCGHEE: Sure.
As we build things like the RAG app
we just saw, we now have a new operational control
to consider-- prompts.
We always want to be improving our prompts--
their text, their parameters, or which model they're built for.
These prompts are written right into the code of the app.
They are code.
So we want to help make this better and less toilsome.
That's why we're introducing these
to Vertex AI, prompt management and prompt evaluation.
Now you can see in real time how changes to your prompts
are working-- kind of like a before and after view.
CHLOE CONDON: Welcome to Next, Charity.
CHARITY MAJORS: Thank you.
I'm so happy to be here.
So at Honeycomb, we make next-generation observability
tools to help software engineers understand
the code that they write.
Our systems used to fail in fairly predictable ways,
but this is no longer true.
Our systems are dynamic and chaotic.
Their architectures are far flung and diverse
and constantly changing.
So we wired up real Honeycomb traffic to the Gemini model.
And this right here you can see is
a stream of real user requests in Honeycomb.
We have this tool, called Query Assistant, right here
that uses generative AI to create Honeycomb queries.
So you can ask questions about your code
using natural language, like, which
users have the highest latency?
and get back a relevant query and result.
But when you can use SLOs, identify something
that you find interesting, and then slice and dice
and explore your data like this, you
can really quickly get to the bottom of any issue,
even if it's completely new and unknown to you.
RICHARD SEROTER: Please help me welcome the engineering manager
at Hugging Face, Philipp Schmid.
Good to see you.
You're doing so much to enable our developers, and developers
everywhere, to use GenAI.
Thank you for that.
PHILIPP SCHMID: Yeah, and we are excited to have Google open
develop models available alongside powerful integrations
into Google Cloud.
RICHARD SEROTER: Yeah.
PHILIPP SCHMID: How about we jump in
and I show you how to train and deploy models
directly from Hugging Face to Google Cloud?
Once you find a model you would like to train, such as Gemma,
we can directly do this by clicking on Train Google Cloud.
Choosing this option gives us Colab notebooks
available in Vertex AI, where we can
perform instructions fine tuning or further train our model.
And we are pleased to announce that we are bringing Deploy
on Google Cloud option to thousands of Hugging
Face-available models.
So if you are starting in Vertex,
we have the new deploy from Hugging Face options--
RICHARD SEROTER: There you go.
PHILIPP SCHMID: --with hundreds of available
Hugging Face models to select, deploy,
with tested configurations for Vertex AI and GKE.
RICHARD SEROTER: So, look, if you take away
anything from this keynote today,
it's that, from coding to running platforms to operations,
software is simply better with Gemini.
CHLOE CONDON: And we'll see you in the Cloud.
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