‫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.