next_steps

Amazon Personalize Next Steps

Notebooks and examples on how to onboard and use various features of Amazon Personalize

Amazon Personalize Use Cases examples

The core_use_cases/ folder contains detailed examples of the most typical use cases.

Generative AI

The generative_ai/ folder contains examples of combining foundation models with Amazon Personalize.

Scalable Operations examples for your Amazon Personalize deployments

The operations/ folder contains examples on the following topics:

• Maintaining Personalized Experiences with Machine Learning

  • This AWS Solution allows you to automate the end-to-end process of importing datasets, creating solutions and solution versions, creating and updating campaigns, creating filters, and running batch inference jobs. These processes can be run on-demand or triggered based on a schedule that you define.

• MLOps (legacy)

  • This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Step Functions. To get started navigate to the ml_ops folder and follow the README instructions. This example has been replaced by the Maintaining Personalized Experiences with Machine Learning solution.

• MLOps Data Science SDK

  • This is a project to showcase how to quickly deploy a Personalize Campaign in a fully automated fashion using AWS Data Science SDK. To get started navigate to the ml_ops_ds_sdk folder and follow the README instructions.

• Personalization APIs

  • Real-time low latency API framework that sits between your applications and recommender systems such as Amazon Personalize. Provides best practice implementations of response caching, API gateway configurations, A/B testing with Amazon CloudWatch Evidently, inference-time item metadata, automatic contextual recommendations, and more.

• Streaming Events

  • This is a project to showcase how to quickly deploy an API Layer infront of your Amazon Personalize Campaign and your Event Tracker endpoint. To get started navigate to the streaming_events folder and follow the README instructions.

• Lambda Examples

  • This folder starts with a basic example of integrating put_events into your Personalize Campaigns by using Lambda functions processing new data from S3. To get started navigate to the lambda_examples folder and follow the README instructions.

• Personalize Monitor

  • This project adds monitoring, alerting, a dashboard, and optimization tools for running Amazon Personalize across your AWS environments.

Reference Architectures

The following reference architectures provide examples of how to apply Amazon Personalize across industries:

• Retail - the Retail Demo Store is a full stack web application that implements personalization using Personalize in a web application, messaging, and conversation AI interfaces. There are hands-on workshops
• Media and Entertainment
• Travel and Hospitality

Workshops

The workshops/ folder contains a list of our most current workshops:

• POC in a Box
• Re:invent 2019

Data Science Tools

The data_science/ folder contains an example on how to approach visualization of the key properties of your input datasets.

The key components we look out for include:

• Missing data, duplicated events, and repeated item consumptions
• Power-law distribution of categorical fields
• Temporal drift analysis for cold-start applicability
• Analysis on user-session distribution

License Summary

This sample code is made available under a modified MIT license. See the LICENSE file.