PRIYANKA VERGADIA: Hi, I'm Priyanka Vergadia,
and this is AI Simplified, where we
learn to make your data useful.
In the last few episodes, we have
been covering different components of Vertex AI
to get you familiar with different features.
In this video, I thought we would help a friend of mine
get into yoga.
So we are building a yoga pose Classification Model
for some simple yoga poses.
This will help her hone her yoga skills.
First of all, let's see how we will make this happen.
We need the data to create our models with.
This would be in the form of images,
and let's say we want to focus on five
poses, the Tree, the Goddess, the Warrior II, Plank,
and Down Dog.
My colleague Sara and I have collected a bunch of images
from a yoga practice that we will
be using to train this model.
Our first step would be to ingest these images using
the Manage Data Sets, and then label them appropriately.
Then comes training the model.
And since this is an image classification problem,
it's a good fit for AutoML.
And as we covered in the last episode,
you can totally create a custom model from scratch
if you wanted to, but AutoML is just
the right fit for this particular Image Classification
problem.
Once our model is trained, we will
evaluate it to understand the quality of our model
and indicate areas of improvement.
Now, AutoML UI provides us with easy
to understand charts for this that we will demystify
when I show you the console.
Then, we will need an endpoint to serve
our models for predictions.
After training our AutoML model, we
will be able to deploy an endpoint with just one click.
Now that we have laid out the data requirements
and how we will train our model, we
are ready to jump into the console and make it happen.
First thing we need is the data.
For this step, my friend Sara and I
collected a bunch of images from a yoga practice
and uploaded them into the Manage Data Sets.
As you see here, the images are labeled in five categories.
I can even upload unlabeled images and label them.
We can analyze our data set to see
that we have enough examples of each categories.
Now, we are ready to train our model.
In here, I'm selecting AutoML for training method.
You can also train a model to deploy on the Edge
or on-premise, or use custom training if you're writing
your own Custom Model Code.
Now, we define our model.
AutoML automatically splits the data
into Training, Validation, and Test,
but we can change it if you want to.
In Compute and pricing, we get to select how many node hours
we want to train the model.
At this point, we wait for the model to train.
Once it is trained, we see it in the Model tab
and can evaluate the model.
We can see our model's Average precision.
It ranges from 0 to 1, where a higher value indicates a higher
quality model.
We can see that the Confusion matrix
helps us understand where our model is confusing to results.
Now that we have evaluated our model,
it's time to deploy it to an endpoint.
We can split traffic between two models deployed
on the same endpoint, if needed.
Allocate the number of nodes needed for this endpoint.
Depending on where your users are,
select a region to deploy your endpoint.
And that's it, hit Deploy, and we have an endpoint.
Now we can test our endpoint and the model
by uploading an image.
And yay, it works.
You can also make predictions using the SDK.
All right, so today we created a yoga pose Classification
Model where we uploaded our data sets in Vertex AI,
labeled our images in the UI, and then used
AutoML to train our model.
Then we evaluated it and then deployed it
for making predictions with just one click.
I know my friend will be extremely
happy to use this model to hone her yoga skills.
Stay tuned for the next episode, which
is all about using what Vertex AI SDK.
In the meantime, give us a like and subscribe,
and share with me your thoughts about this series
in the comments below.