Amazon recently unveiled a new fully managed service under the AWS umbrella. Amazon Personalize is a machine learning tool that lets users create private, customized personalization recommendations for applications. The tool is now available to all AWS users. It had been available in preview starting in 2018. If you’re interested in making the most of this service for your organization, here’s what you should know.
About Amazon Personalize
Amazon Personalize makes recommendations to users based on the historical data you have stored in Amazon S3 and the streaming data that are sent in from your applications. It uses machine learning models that provide personalized buying experiences for users. Essentially, it takes the basic idea of the personalized recommendations Amazon already uses on its own platforms and makes it available in a simple form to developers. Amazon says that users don’t need to deal with complex infrastructures or complicated machine learning models to use the service.
The ability to create these personalized experiences for customers gives organizations an edge. You can use your own data to send out timely video recommendations within apps or personalized notification emails. These experiences are designed to be more relevant to each individual, thus more likely to lead to a positive outcome for the business or organization behind it.
Additionally, building a personalization engine from scratch can be quite a challenging and time-consuming process. Amazon Personalize gives you a head start on the tools you need to create these recommendations.
How it works
Amazon Personalize users provide unique signals in activity data and demographic information to let the platform create those personalized recommendations. So you can have it look at page views, sign-ups, purchases, customer age, or location. Then you provide the inventory of items that you want to recommend, which can be anything from articles to products. Amazon Personalize then processes the data to identify what is meaningful and create algorithms to optimize a personalization model that’s customized to your customers and inventory. That model is also accessible via an API.
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