Next, we have Amazon Personalize,
which is a fully machine learning service to build apps
with real-time personalized recommendations.
So what could be a recommendation?
For example, a personalized product recommendation,
or re-ranking, or customized direct marketing.
For example, a user has bought a lot of gardening tools,
and you want to provide recommendations
on the next one to buy based on a personalized service.
So this is the same technology used by Amazon.com.
So when you go and shop on Amazon.com
and after buying a few products, what you will see
is that Amazon.com start recommending products
in the same category or in completely different categories
based on how you've been searching
and how you've been buying
and user interest and that kind of things.
So Personalize is how you access this from within AWS.
So, you read your input data from Amazon S3.
For example, it could be user interactions,
those kind of things.
Also, you can use the Amazon Personalize API
to have real-time data integration
into the Amazon Personalize service.
And then this will expose a customized personalized API
for your websites and applications,
your mobile applications.
Also, you can send SMS or emails
for personalization as well.
So you have all these integrations.
It takes days, not months, to build this model.
So you don't need to build, train, and deploy ML solutions.
You can just use this bundled as is.
And so the use cases is going to be retail stores,
and media, and entertainment.
So from an exam perspective,
anytime you see a machine learning service
to build recommendations and personalized recommendations,
think Amazon Personalize, that's it.
I will see you in the next lecture.