Amazon Personalize is a machine learning service that makes it easy for developers to add individualized recommendations to customers who use their applications. It reflects the vast experience that Amazon has in building personalization systems.
You can use Amazon Personalize in a variety of scenarios, such as giving users recommendations based on their preferences and behavior, personalized re-ranking of results, and personalizing content for emails and notifications.
Amazon Personalize does not require extensive machine learning experience. You can build, train, and deploy a solution version (a trained AmazonPersonalize recommendation model) with the AWS console or programmatically by using the AWS SDK
Amazon Personalize can capture live events from your users to achieve real-time personalization. AmazonPersonalize can blend real-time user activity data with existing user profile and item information to recommend the most relevant items, according to the user’s current session and activity. You can also use AmazonPersonalize to collect data for new properties, such as a brand new website, and after enough data has been collected, AmazonPersonalize can start to make recommendations.
To give recommendations to your users, call one of the recommendation APIs, and then create personalized experiences for them.
Amazon Personalize can improve its recommendations over time as new user activity data is collected. For example, a new movie rental event by a user can result in better movie recommendations.
Amazon Personalize can provide recommendations based on a user’s browsing context. For example, AmazonPersonalize can provide different recommendations when a user is browsing on a mobile device than when that same user is browsing on a desktop.
With Amazon Personalize you can train a solution for different use cases. For example, user personalization, items related to an item, and re-ranking of items. You choose a recipe based on your use case and provide the input data. A recipe performs featurization of your data, and applies a choice of learning algorithms, along with default hyperparameters, and hyperparameter optimization job configuration.
Recipes in AmazonPersonalize allow you to create custom personalization models without needing machine learning expertise. You can choose which recipe to use to train a solution version, or let AmazonPersonalize decide on the best recipe to use for your data. To help you decide which recipe to use, AmazonPersonalize provides extensive metrics on the performance of a trained solution version.
Below are the cmdlets which are available with Amazon Personalize
| CmdletName | ServiceOperation | ServiceName |
| Write-PERSEEvent | PutEvents | Amazon Personalize Events |
| Get-PERSRPersonalizedRanking | GetPersonalizedRanking | Amazon Personalize Runtime |
| Get-PERSRRecommendation | GetRecommendations | Amazon Personalize Runtime |
You can also check other AWS Services, and each services cmdlets we are providing.