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NATALIE PIUCCO: Hi, I'm Natalie.
And we are in the Intelligence Neighborhood
in the Sounds and Models demo.
And I'm joined here by Yufeng.
Yufeng, tell us a little bit about what you do
and the awesome demo that you've created.
YUFENG GUO: Here, we have station one gathering data.
And we have this kind of instrument, if you will.
But it's actually five instruments.
And so each string represents a different instrument,
and we can pluck our strings to make different sounds.
And what we're going to do is use this
as our kind of data source.
The sounds are going to then go into Dataflow
and get converted into spectrograms.
They're going to get moved into the frequency space.
And those images can then be used to train our models.
The hope is that these images will be distinct enough
to be able to--
NATALIE PIUCCO: Represent each--
YUFENG GUO: Exactly.
That we can tell them apart, that we
can distinguish between instrument one, two, three,
four, and five.
NATALIE PIUCCO: Cool.
OK, so we've got our training data.
Now it's time to start training the model.
YUFENG GUO: So with AutoML, we can train models
using the data that we had.
But then we don't have to do any actual work.
So we can see here that the data is coming in.
And you can see, those are some of the spectrograms.
Those are the real data that attendees have generated
over the course of today.
NATALIE PIUCCO: And they're the images of the sound waves.
YUFENG GUO: That's right.
These are the spectrograms.
And then when you train a model, it just does the training.
There's not much kind of work you have to do.
And when it finishes training, you
can have some evaluation data that
shows you the metrics around how well the model trained
and how it did.
NATALIE PIUCCO: Right.
So if you are new to ML, that's a great space
to get started, AutoML.
YUFENG GUO: Right.
It's an easy tool to get going with.
And if you have some data and you
want to throw it on ML Vision, it'll take care of it for you.
And you can go do something else with your time.
So we have this board here where you
can move these kind of sliders around to different positions.
And they map to different values on the screen, which
change the values of type of parameters
of the actual models, things like kernel width and height,
the layer size, the number of strides,
as well as the kind of number of layers
you have [INAUDIBLE] have to change the amount of code
on the screen.
Once we're happy with the set of settings that we have,
we can go ahead and push the button here
and start a training job.
And doing that, it will assign us
a name, something fun like spinto-cap,
and shoot that off to Kubeflow, where we
have a pipeline to do training.
And when it finishes training, it
will deploy it on the Cloud AI Platform Prediction Service.
NATALIE PIUCCO: Awesome.
So time to move to stage three of training our model.
YUFENG GUO: That's right.
Speaking of prediction.
NATALIE PIUCCO: All right.
So we're at the final stage now, and that's
to predict the notes.
YUFENG GUO: Yes.
And so we're going to try to pull up the model
that we've been training, spinto-cap.
It's there for us.
So you can turn the crank as a selector,
and then press the red button to select it.
NATALIE PIUCCO: Select.
YUFENG GUO: So this is kind of like a customizable music box.
And we can choose which notes to play on which instrument.
NATALIE PIUCCO: More xylophone.
YUFENG GUO: You can hit the button again.
So in this countdown, then we'll be able to turn this crank now.
And so these are the notes that we set up.
And we're going to send those notes off to the Cloud AI
Platform Prediction Service.
Now how did we do, right?
Was our model any good?
We chose those parameters kind of randomly.
And so it might be good.
It might not be so good.
We'll see.
It got some of them.
It seemed like it might have actually missed a bunch.
It did.
And so, you know, this is part of the machine learning
workflow.
In this particular case, a pretty abysmal outcome.
NATALIE PIUCCO: That's all right.
Because that's all part of machine learning, right?
It's all part of the process.
So I think this is a perfect demonstration to show that.
So Yufeng, thank you so much for this demonstration.
I think it was very clear in kind of the three
stages of what to expect when training machine learning
models.
So thank you so much.
YUFENG GUO: My pleasure.
Thanks so much, Natalie.
NATALIE PIUCCO: And back to you.
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