SPEAKER: Vertex AI is Google Cloud's managed machine
learning platform.
It contains a variety of products
that help you across each step of the machine learning
workflow, from data preparation and hyperparameter tuning
to training and experiment tracking
to model management and pipeline orchestration.
And it can be used by practitioners
with varying levels of ML expertise via the UI or SDK.
Generative AI support and Vertex AI
helps developers and data science teams
to access, tune, and deploy foundation models,
like PaLM, in a way that provides them with the most
choice and control.
So let's dive in and check out Generative AI Studio,
our new capability built in Vertex AI, where developers
can prototype quickly with chat and prompt design.
Then we'll take a look at Model Garden, which
allows data scientists to tune and experiment
with foundation models.
Content creation for marketing is an often laborious task
that generative AI can supercharge.
Let's use generative AI to help us
create an omnichannel campaign consisting of blogs, Instagram
posts, tweets, and images for our fictional company, Cymbal.
I'll start off by highlighting the text here
in an existing blog post from Cymbal.
And then I'm going to go ahead and enter into Generative AI
Studio, which, again, is our new capability built in Vertex
AI, which shows me first this familiar chat interface,
as well as prompt design for rapid prototyping,
and a gallery of prompts over here to help me get started.
I'm going to go ahead and enter my prompt into the box
below here to begin the conversation.
You can see that I've just pasted in the blog content
directly into Generative AI Studio.
No special preprocessing was required here.
This is just the exact text that I copied from the website.
And then up at the top here, you can
see that I'm actually asking Generative AI
Studio to summarize the top four value propositions
from this blog via chat.
So let's go ahead and click Submit.
This tool is great for summarization, prototyping,
idea generation.
And it really doesn't require any ML expertise.
And you can see that the generative model takes
the prompt and web page and it generates
these four believable and accurate value
propositions that I can go ahead and use for my campaign.
So next, I'm going to prompt the system.
And this time, I'll ask it to turn that
into a promotional Twitter post with hashtags.
So again, we'll click on Submit.
And you can see that Generative AI Studio is now giving me
a well-crafted promotional post, along with all
of the important Twitter hashtags.
We've got #sustainable, #ecofriendly,
and #healthyeating.
But we can push the boundaries of generative AI even further
and design a prompt that creates a blog headline, a blog post,
Instagram caption, and Instagram hashtags with configurability
for more sophisticated scenarios.
I'm going to go ahead and click on Prompt Design over here.
And we can design a prompt to generate a multi-media campaign
based on the value proposition for a healthy granola bar.
So in addition to being able to provide my prompt here,
I can also adjust model parameters
on the right-hand side, such as temperature.
This basically tunes the randomness and responses
from the model.
So a lower temperature is generally better
for prompts that expect a more true or correct response,
while higher temperatures can result
in more unusual, and you might even say creative, responses.
We're going to use Google's latest PaLM Bison model,
and we'll ask for a blog headline, blog post, Instagram
caption, and Instagram hashtags.
So let me go ahead and enter that prompt right now
over here.
And then we can click Submit.
And there we have a blog headline, a blog post,
and Instagram content for our multi-media campaign.
Note that Generative AI Studio does provide a symmetrical user
experience.
So whatever you can do in the UI, you can also do in code.
And from the UI, if we click over here,
we can even view the code that we'd
use to get this into an application,
extracting an API endpoint through Python
or curl commands.
But of course, generative AI is about
a lot more than chat and text summarization.
Using Vertex's generative vision AI capabilities and our image
generation model, I can contextualize
simple text prompts to generate images that go along
with my marketing campaign.
I'll start off by writing a prompt here
that gives me a visual of a granola bar on a kitchen table.
And once we've done that, we can click Generate.
You can see I now have a number of model-generated images
that I can use for my campaign.
This is the power of multimodality in action.
So let's click on one of them and see what we can do.
The initial image here is powerful.
But what's even more impressive is
that I can edit this image with natural language.
So let's say I wanted to add some honey dripping
from the spoon in the background.
If I add this to my prompt and click Generate,
we can see that we get this new image.
And I now have all the content that I need for my social media
campaign, thanks to the power of Vertex Generative AI Studio.
But this is really only just the beginning.
Along with Generative AI Studio, we're
also introducing Vertex Model Garden, a single environment
to help you search and discover Google's latest
models from Google Research and DeepMind,
along with open source and third-party models.
You can browse models for a variety of different tasks
and data formats, including text, image, video, code,
and audio.
And you can access these models in a variety of ways,
from notebooks to APIs to interactive prompting.
I'll start off by clicking on a foundation model,
like Google's PaLM Bison text model.
In addition to getting model details,
I can also see use cases, documentation, and access
to our SDK and samples.
There are a few different ways that I
could customize this model.
So as we saw earlier, I can prototype rapidly
with different text prompts.
But for even greater customization,
I could fine-tune my model, which results in changes
to the model's weights.
This is a great option for data scientists
who need to produce outputs with specialized results,
like legal or medical vocabulary.
But it's not really necessary in this case.
Instead, we'll click on the Tune option.
This will allow us to customize the model
to produce content that's aligned to our company's brand
voice.
First, I'll need to upload a small amount of data
so that I can tune the model.
But before we do that, let's go ahead
and give the model a name, like Healthy Snacks LLM.
I want to use brand voice examples, like press releases,
tweets, and blog posts.
So I'll go ahead and highlight all of the files
here and click Open.
My uploaded data is stored in my company's environment,
so I know it's protected.
Google will only use my data with my permission
and for the tasks that I specify.
Now, after the data is uploaded, I
get a quick preview over here, which
shows both my input and the corresponding outputs.
And it looks good to me, so let's go ahead and tune.
Note that to save time, I have actually run this previously
just to make sure that the model is ready for us.
Here, I can see all of the tuned models that I have available,
what model they were based on, what type of tuning was used,
and the current status.
You'll notice that my Healthy Snacks LLM has no endpoint.
But that's really no problem because Vertex provides
a fully-integrated platform for model deployment
with just a couple of simple clicks.
So let's go ahead and deploy this as an endpoint.
Vertex AI's managed endpoints make
it easy to build this into my application
with only a few lines of code.
And I don't have to worry about all
of the complexities of setting up serving infrastructure,
especially for large models.
Here, I can create a new endpoint
that can be used by any of my enterprise applications,
like Salesforce or ServiceNow, along
with setting encryption, logging, model monitoring, et
cetera.
So we'll go ahead and click Create.
And with the state-of-the-art infrastructure,
we can create an endpoint quickly and begin using it
almost immediately.
The combination of both Generative AI Studio and Model
Garden means that all of your developers, data scientists,
and machine learning engineers have a full suite of tools
well beyond a simple chat interface that
allow you to take advantage of the full power
of multimodal generative AI built directly
in our end-to-end machine learning platform.