PRAKASH: Namaste, Elias.
ELIAS: Oh, hi, there, Prakash.
PRAKASH: [NON-ENGLISH SPEECH]
ELIAS: What?
PRAKASH: I'm sorry.
What I mean is, you seem happy.
How's the day going for you?
ELIAS: I'm doing fine.
But, Prakash, how many languages do you speak?
PRAKASH: Well, I speak Hindi, Konkani, my mother tongue,
Kannada, and a couple more.
ELIAS: Woah, that's impressive.
You know, our customers have users
that speak many languages, and Google has
tools to help translate texts.
But how do we help customers with industry-specific jargon
or technical material that needs to be translated
to a different language?
PRAKASH: You mean like "server" in a restaurant
should be translated differently from "server" in a data center?
ELIAS: Exactly.
PRAKASH: Well, Elias, Google Cloud
has created just the thing for this.
It's called AutoML Translate.
It is a way for our customers to bring
their business-specific translation examples
and easily train a custom machine-learning model
to understand their domain.
Let me show you how it's done.
ELIAS: Let's do it.
[MUSIC PLAYING]
PRAKASH: This is the AutoML Translation user interface,
and you're looking at some of the custom models that
are already trained.
Now let's look at some datasets that are already in place.
These are used to train up the custom models.
Let's create a new dataset.
First we'll give it a name.
Then we'll select the source and target languages--
English and Japanese in this case.
The translation examples can be supplied in two formats--
TSV and TMX files.
And you can manually break this up
into training, testing, and validation datasets,
or you can let the system do it automatically.
You can upload the files from your drive
or drop them on Google Cloud Storage.
For this demo, I'll pick a file on my drive.
Clicking on Create Dataset starts the import process,
and this can take a little while depending
upon the size of the dataset.
For this demo, I already have a dataset imported, so let's
click through on it.
And on the first screen, you'll see some sample sentences
drawn from this dataset.
On the Training tab, there's additional information
about this dataset.
And you can launch the training by clicking on Start Training.
You can pick the base model from Google
and train up your custom model, or you
can continue training a previously created custom model
as well.
And under the covers, the system automatically deploys
the necessary resources and handles the training.
Now, this can take a few hours, so I have pre-trained a model
already for use in this demo.
So let's go access that model.
Again, when you go to the Train tab,
you will see some metrics for this custom model.
The system automatically generates a blue score
for the custom model.
And if the blue score is higher than the base model,
it means that translations done using this model
will be closer to your reference dataset.
Now, this dataset has relatively fewer sentence pairs,
so these numbers are not typical.
In your case, with larger datasets,
you will see much better blue scores.
You can, of course, continue to train this model
and either create a new version or continue to train this up
until you get the quality that you're looking for.
Now, once you have the quality that you need,
you can go to the Predict area.
And in this case, the system will show you
samples for invoking the custom model via REST API or Python
code snippets.
And on this screen, you can quickly
run some translation examples and compare the results
from your custom model with the base model from Google.
[MUSIC PLAYING]
So this is how Google Cloud is making machine learning easily
accessible to everyone by addressing the two major
challenges that slow down machine-learning projects--
one, a need for deep data science skills,
and two, a need to manage and deploy
your own compute resources.
ELIAS: This is really great, Prakash.
Thank you.
If you really like this video, please go ahead and share,
comment, and subscribe.
And also, if you're working on any cool projects
and would like to get in contact with us,
please click on the link in the description.
Thanks for watching.
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