AWS Personalize is a machine learning service that empowers non-machine-learning engineers to easily generate personalize recommendations for their users. It is a powerful yet developer-friendly tool that does not require any prior knowledge in machine learning. All you need to do is providing the data to Personalize via S3, and Personalize will take care of everything from identifying features to training the models.
In one of my previous projects, I had the opportunity to work closely with AWS, as the company that I worked for at the time was a recipient of the AWS Imagine Grant Program. The AWS team recommended that we used Personalize to generate smart recommendations for our users based on their activities on the app. Since then, it has been one of my favorite services on AWS.
Step 1 — Get Started — Preparing Data
Step 2 — Storing data in S3
Step 3 — Creating a dataset group
Step 4 — Create user-item interaction data
Step 5 — Import user-item interaction data
Step 6 — Create Solution
Step 7 — Create Campaign
Step 8 — Get real recommendations
Optional — Generate real recommendations using Node.JS
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