PRIYANKA VERGADIA: Can I get some milk and sugar
with my coffee please?
SPEAKER: [SPEAKING SPANISH]
PRIYANKA VERGADIA: Now that is some awesome coffee.
Translation is fun, isn't it?
Well, stay with me as we explore custom translation models
to power global apps.
Welcome to "AI Adventures" where we explore the art, science,
and tools of machine learning.
I'm Priyanka Vergadia, and today we
are talking about custom translation models.
I was in Peru last year, which is mostly
a Spanish-speaking country.
I do not speak Spanish, so I had to rely
on Google Translate for even minor things such as can I
get milk in my coffee, please?
Now imagine if I was to go live and work in Peru for a year,
helping developers implement machine learning and AI maybe.
The machine-learning field has unique terms and references
to mathematical concepts which might not be captured
in a standard dictionary.
For a custom use case like this, the standard Translation API
may not be sufficient.
To provide the best experience, I
would need to make sure things don't get lost in translation,
and that would need a custom model specific to the ML and AI
terminologies to communicate effectively
with the Spanish-speaking developers.
This is probably true in your field as well.
That's where AutoML Translation comes to rescue.
AutoML Translation lets you create your own custom
translation models so that translation
queries return results specific to your domain.
To understand this further, let's take another example.
Imagine you run a financial-reporting purpose
that has an opportunity to expand to Peru, India,
and France.
Those markets require that your time-sensitive financial
documents are translated in real time.
AutoML Translation can help you automate the translation job
for over a hundred language pairs in a scalable way,
allowing you to enter those newer markets quickly.
Now, you might be asking yourself, when would
I use AutoML over the Translation API?
Translation API does a great job with general-purpose text,
and with advanced glossary features
you can take it further by providing a dictionary of words
or phrases.
I covered this in detail in the previous episode linked below.
But there are times when you don't have a glossary
or know exactly which word translations
you want to control.
AutoML Translation shines in those cases
in that last mile between generic translation
tasks and specific niche vocabularies.
What's amazing is AutoML's custom model
is built on top of the generic Translation API
model for domain-specific content that matters to you.
Now let's say you've established that you need a custom
translation model.
How would you create one?
Well, we will just prepare and source the data
and feed it into AutoML Translation.
It then trains, deploys, and serves the model,
and provides us with an easy interface
to evaluate and test it.
It creates an easy-to-use REST API to make predictions.
High-quality data is critical ingredient
in creating an effective AutoML Translation model.
As a general best practice, make sure your sentence pairs
do the best-possible job of covering the vocabulary, usage,
and grammatical quirks of your industry or the area of focus.
For more details, I've included a link
in the description for some best practices of how
to assess and source your data.
Once we have the data, we head over to the AutoML Translation
UI and create the data set by selecting the source and target
language.
Upload the files from your computer,
and select the folder in Cloud Storage for these files
to be stored.
Then successfully uploaded, we can review our data set
and the sentence pairs.
With the data all set, we are ready to train the model.
But this step can take several hours, depending
on the amount of data.
So let's grab coffee in the meantime.
All right, it looks like our training is complete,
and right here it is showing us the high-level metrics
for our model and its BLEU or Bilingual Evaluation
Understudies score, which takes a ground-truth translation
and compares it to a machine translation.
The more overlap between the human and machine output,
the higher the BLEU score.
You can see the score for Google's base
model and your custom model as well as
the difference between them.
In this case, we see a boost of a few points over the base
model.
Now the moment of truth.
Let's head over to our Predict tab to test our model.
We can even compare the results from our custom model
to the Google NMT, which is the Neural Machine Translation
model, to see the difference.
In this episode, we learned how AutoML Translation
helps you create high-quality, production-ready, custom
translation models quickly.
How are you using the AutoML Translation
model in your use cases?
Share with me in the comments below.
Thank you for watching this episode of "Cloud AI
Adventures."
And if you enjoyed it, click that Like button,
and be sure to subscribe to get all the latest episodes right
when they come up.
I look forward to seeing you next time
on "AI Adventures," where we will
talk about federated learning.