SARA ROBINSON: The best way to see
how AutoML Vision and natural language work
is through live demos.
Let's start with vision.
To show you AutoML Vision, we'll look
at a model I've built for predicting the species
of a leaf given an image.
The data for this is sourced from the Leafsnap dataset,
and our model was trained on images of 10 different types
of leaves.
Here, in the AutoML Vision UI, is where I gather and label
all of the images that will be used to train my model.
I can upload them directly to the UI,
and I only need 10 images per label to start training.
The best part about AutoML is that I
don't need to write any of the model code to train this.
I can simply press the Train button to train my model.
This particular model took exactly one hour to train.
When training completes, I can take a look at metrics
to evaluate accuracy.
This is what's called the confusion matrix.
It tells us the percentage of images for each label
that our model is able to classify correctly.
Ideally, we'll see a strong diagonal down the top left,
since this means our model classified
a high percentage of images in our test set correctly.
And we can click on any cell to see specific images where
our model got confused.
Finally, the most important part of this process
is generating predictions on images
our model hasn't seen before.
Let's try our model out by generating predictions
on two images of leaves.
As someone who's not a plant expert,
I'd probably classify both of these as leaves,
or, if we're being very technical, green leaves.
Let's see what our model says.
Turns out these both belong to different species,
and our model has predicted both varieties correctly
with over 96% confidence.
Once we have a trained model, AutoML
gives us access to a custom REST API endpoint
to make predictions.
If I add more images and train a new version of my model,
I only need to change the model ID in my API request.
I am incredibly excited about AutoML
because it gives anyone the tools
to build custom models, regardless of their machine
learning expertise.
You might even say that AutoML Vision is turning over
a new leaf in the way we build image classification models.
And although I've shown plant species classification here,
you could easily build a model in any other domain.
We've seen our alpha users build AutoML Vision models
for identifying damaged products before they're shipped,
classifying catalog-specific images,
and identifying points in time, like stages of construction.
But what about other kinds of information, like text
documents or news articles?
With AutoML natural language, it's just as easy
to build a custom model to make sense of written content.
Take donorschoose.org, for example,
a nonprofit that receives hundreds of thousands
of proposals each year for classroom projects in need
of funding.
They currently rely on a team of volunteers
to manually screen each submission,
so they published a dataset to Kaggle in hopes
that a community of machine learning experts
could build them an automated solution.
Let's see if AutoML natural language can help.
Similar to AutoML Vision, here I can look at all of the examples
in my dataset and their corresponding label.
The text inputs to our model our needs statements
from the teachers detailing the materials
they need for their classroom.
Each need request has an associated category
to help direct the teacher's requests to a relevant donor.
The labels used to train the model
have been assigned manually by volunteers.
Once the model is trained, it will
be able to predict the category of new requests automatically.
To see how our model performs on new text that
hasn't been categorized yet, we'll
take the following needs statement as an example.
My students need aquarium lighting
for our environmental clownfish aquaculture project.
Donors Choose needs to assign the category Lab Equipment
to this request, which isn't completely
obvious from the text itself.
Let's try it out on our trained model.
We can see that our model was able to correctly classify
this as lab equipment with 98% confidence.
Like AutoML Vision, I now have access to a custom model
that I can use to generate predictions on new texts
with one API request.
That's really all there is to it.
Without writing any model code, you
can build custom machine learning
models that can accurately recognize your own images,
understand the contents of your documents,
and even translate your jargon authentically.
They're ready to use immediately,
accessible through Google Cloud, and they scale automatically
to meet your needs.