[MUSIC PLAYING]
ANU SRIVASTAVA: Hi, everyone.
Today, I'm excited to share more about how foundation models are
now available on Vertex AI as part of our support
for bringing generative AI to you.
Let's dive in.
Foundation models are large AI models
that can be adapted to a wide range of tasks
and can generate high quality output.
So while AI models are not completely new,
these foundation models have several important
characteristics that represent a change
from the previous generation of AI models.
Foundation models are multitask rather than single task.
One foundation model can perform a wide array
of tasks out of the box, such as summarization, question
and answering, classification.
There are different modalities for data types,
such as images, text, code, and more.
For example, Google's Image foundation model Imagen
can predict and generate high quality images
from natural language input.
These models are typically trained on vast amounts
of diverse data, allowing them to learn general patterns
and representations that can be applied across various domains
and tasks.
And with no or minimal training required,
foundation models can work well out of the box,
and can be adapted for targeted use cases
with very little example data.
Before now, foundation models were
difficult to access, required specialized machine-learning
skills, and compute resources to use in production.
Now, developers can access these foundation models via APIs
in the Vertex AI Model Garden.
What is the Model Garden?
First, the Model Garden is where you
go to discover both Google and external models.
The garden includes models from Google Cloud, Google Research,
and external sources for a variety of data formats,
including chat and dialogue, co-generation and completion,
images and embeddings.
Let's take a tour of these models.
Let's start with text, which allows
you to perform natural language tasks with zero
and few shot prompting.
Our gallery of prompts gets you started
with tasks such as summarization, entity
and information extraction, idea generation, all
in a place for you to experiment with your own prompts
in the studio.
Let's take a look at an example to get you started.
I want my model to tell me good titles for sample text.
Here, I have a demo body of text that
describes how humans and robots interact with each other.
Then in the last line, I asked the model
what the title of this article could be.
This works well out of the box.
But if I wanted the model to respond
with certain specifications, I can
feed in examples in a structured manner.
In this structure table, I can feed several example pairs
that I've come up with, so the model can follow the nuances
that I provide.
Here, we can get a different, more accurate title
for the robot sample text, scaling robot learning.
Next, let's talk about dialogue.
This is also text-based but has been specifically trained
to work in conversation.
Dialogue models allow you to have multi-turn conversations
with preserved context.
This is useful for an in-browser or mobile assistant
to help improve the customer experience for answering
questions, summarization, and tuned for your custom domain.
Now on to code completion and generation.
These models can be used to make super-charged coding
assistance.
You can give a natural language prompt to describe
a piece of code you like written, or use the completion
model via the API, and an extension that
could take a partial code snippet as input,
and then suggest the output.
Now let's explore image generation.
With these models, you can create and edit images
to your specifications.
We also have models for working with media
such as classification, object detection, and more.
And built into the product, we have content moderation
for responsible AI safety practices.
Last but not least, embeddings.
They are vector representations of data.
So for example, a text embedding is a numerical vector
representation of a word or a phrase.
This model allows you to extract semantic information
for unstructured data.
You can use this to power recommendation engines,
ad-targeting systems, complex classification task, search,
and more.
Google's curated set of foundation models
available through the Model Garden and Vertex AI
enables you to quickly find and customize
the right model for your task.
By using and customizing the foundation models
available in Vertex AI, and the Model Garden,
you can access a whole new level of features and functionality
possibilities to help make your apps more
intuitive, personalized, and effective at meeting
your users' needs.
Learn more about getting started with Vertex AI and the Model
Garden by checking out the links below.