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POLONG LIN: Looking to jump-start your ML project?
Don't know where to start or what model is right for you?
Well, why not start your ML project
in Model Garden on Vertex AI?
Watch this video to learn more about Vertex AI Model Garden.
First, just a quick recap.
Vertex AI is a fully managed ML platform on Google Cloud.
Vertex AI allows you to access, build, experiment, deploy,
and manage models.
You may be doing traditional data science, machine learning,
MLOps, or simply building an AI-powered app,
and Vertex AI will support all of those workloads.
More recently, we've announced two major tools for you
to do even more, Model Garden and Generative AI Studio.
So in this video, we'll dive into Model Garden.
So what is Vertex AI Model Garden?
Model Garden is a single place to discover and interact
with Google's industry-leading models and popular open source
models with Google Cloud's enterprise MLOps tooling
support built in.
With all of these different enterprise-ready models
you could use, Model Garden allows
you to choose the right model for your use
case, ML expertise, and budget.
For Model Garden, you can kick off
a variety of workflows, including
using a model directly as an API,
tuning the model in the Generative AI Studio,
using the model directly in a Jupyter notebook
through Vertex AI Workbench, or help you deploy model training
pipelines.
So let's explore this topic a bit.
So as a developer, you may want to start
with pre-trained models that are ready to use out of the box.
Finding relevant models and being able to use them quickly
becomes a way to accelerate your time to getting value.
Let's explore some models on Model Garden
and walk through what the experience might look like.
All right, let's explore how I can use PaLM 2 for text.
Clicking on View Details brings us to the Model
card, which gives us more details about the PaLM 2
model for text.
And there's even a button at the top that shows us
how to use it directly.
And this brings us to the Generative AI Studio.
Generative AI Studio enables you to interact
with the model via a simple UI or tune the model
with your own data.
Let's explore the PaLM 2 model for text.
So I can ask something like, explain large language models
like I'm five.
And it gives me an explanation of LLMs.
With the PaLM family of models, you
can prototype in Generative AI Studio.
But for other models that aren't in Generative AI Studio,
you can interact with them via a notebook.
Let's walk through an example as a data scientist
where, instead of starting from scratch,
I'll start by looking in Model Garden
to see if there is an existing model that fits my use
case around text and images.
So I want to see if Model Garden has computer vision
models that I can explore.
From the Model Garden page, let's filter
for vision-related models.
Next, let's select detection tasks.
Great, there's an OWL Vision Transformer
model, an open source model.
And it says it's a zero-shot, text-conditioned object
detection model that can query an image with one
or more multiple text queries.
Let's dive into this.
So clicking on View Details, here we
see the Model card, where we can see more details.
As a data scientist, I may want to try
using this at this point.
So I'll click on Open Notebook.
This opens up a Colab notebook for the OWL Vision Transformer
model.
I can then walk through this notebook
to learn more about how to get started understanding my image
files.
So this notebook in particular shows
how I can deploy this open source
model to an endpoint on Vertex AI,
then send an image to the endpoint
to get a prediction, which is a text caption describing
what's in the image.
So that flow, from finding a model
to deploying and using it, becomes
super easy with Model Garden.
Here's another way that makes using models
easy on Vertex AI Model Garden.
For some models like BERT, you may
want to fine-tune the model by supplying it
with your own data.
Doing so might take a series of complicated steps on your own,
but Model Garden makes it easier by wrapping it
in a pipeline for you to quickly use
with minimal or zero coding.
Let me click on Open Fine-tuning Pipeline.
This brings me to a pipeline template
that I can use to fine-tune and deploy this model.
You can see the various components of the pipeline
that this template would execute.
To use the pipeline, I can click on Create Run.
Then I proceed to enter in the required parameters,
like where I want to store any outputs of my pipeline
as well as the input parameters, like where my training data is,
and then just click Submit.
Then with that, it will kick start my pipeline.
I didn't have to write any code.
So that's Vertex AI Model Garden,
enabling you to search, discover,
and interact with a wide variety of curated and enterprise-ready
models.
For more information, check out the link below.
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