Deploy a mechanism to keep personalized recommendations up-to-date - Maintaining Personalized Experiences with Machine Learning

Deploy a mechanism to keep personalized recommendations up-to-date

Publication date: September 2021 (last update: October 2024)

This implementation guide provides an overview of the Maintaining Personalized Experiences with Machine Learning solution, its reference architecture and components, considerations for deployment and configuration steps for deploying this solution to the Amazon Web Services (AWS) Cloud.

Maintaining Personalized Experiences with Machine Learning solution streamlines and accelerates development by providing automated pipeline construction, automated personalization model including: configuration, training, retraining, and deployment, as well as improved visibility into model performance, and advanced error handling mechanisms. It helps you build personalized experiences with Amazon Personalize for your product portfolio and provide real-time, curated experiences across digital channels.

Implementing Maintaining Personalized Experiences with Machine Learning solution aims to increase your user engagement metrics, click-throughs, and conversion rates by providing up-to-date product recommendations, personalized product re-rankings, user segmentation, and customized direct marketing.

Personalization opportunities exist in multiple areas along the customer journey including:

  • Discoverability - Helping consumers easily and quickly discover products and content

  • Acquisition and retention - Attracting and retaining consumers in a digital environment

  • Engagement - Understanding, measuring, and increasing time spent engaging with products and content

  • Efficiencies and revenue - Increasing average revenue per user

This implementation guide provides an overview of the Maintaining Personalized Experiences with Machine Learning solution, its reference architecture and components, considerations for deployment and configuration steps for deploying this solution to the Amazon Web Services (AWS) Cloud.

The guide is intended for IT architects, developers, DevOps, and data analysts who have practical experience architecting in the AWS Cloud and want to implement Maintaining Personalized Experiences with Machine Learning in their environment.

Note

This solution is not recommended for handling regulated data such as PII, HIPAA, and GPDR when deployed in production.

Use this navigation table to quickly find answers to these questions:

If you want to . . . Read . . .
Know the cost for running this solution. Cost
Understand the security considerations for this solution. Security responsibilities are shared between you and AWS. Security
Know how to plan for quotas for this solution.AWS CloudFormation, AWS Lambda and AWS Step Function quotas may apply. Quotas
Know which AWS Regions are supported for this solution. Supported AWS Regions
View or download the AWS CloudFormation template included in this solution to automatically deploy the infrastructure resources (the “stack”) for this solution. AWS CloudFormation template
Access the source code and optionally use the AWS Cloud Development Kit (AWS CDK) to deploy the solution. GitHub repository