Notebooks and examples on how to onboard and use various features of Amazon Personalize
The getting_started/ folder contains a CloudFormation template that will deploy all the resources you need to build your first campaign with Amazon Personalize.
The notebooks provided can also serve as a template to building your own models with your own data. This repository is cloned into the environment so you can explore the more advanced notebooks with this approach as well.
The next_steps/ folder contains detailed examples of the following typical next steps in your Amazon Personalize journey. This folder contains the following advanced content:
-
Core Use Cases
-
Generative AI
-
Scalable Operations examples for your Amazon Personalize deployments
- 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 Step function (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 This example has been replaced by the Maintaining Personalized Experiences with Machine Learning solution.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.
- Lambda Examples
- This folder starts with a basic example of integrating
put_eventsinto 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.
- This folder starts with a basic example of integrating
- Personalize Monitor
- This project adds monitoring, alerting, a dashboard, and optimization tools for running Amazon Personalize across your AWS environments.
- Streaming Events
- This is a project to showcase how to quickly deploy an API Layer in front of your Amazon Personalize Campaign and your Event Tracker endpoint. To get started navigate to the streaming_events folder and follow the README instructions.
- Maintaining Personalized Experiences with Machine Learning
-
Workshops
- Workshops/ folder contains a list of our most current workshops:
- Partner Integrations
- Explore workshops demonstrating how to use Personalize with partners such as Amplitude, Braze, Optimizely, and Segment.
-
Data Science Tools
- The data_science/ folder contains an example on how to approach visualization of the key properties of your input datasets.
- 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
- The data_science/ folder contains an example on how to approach visualization of the key properties of your input datasets.
-
Demos/Reference Architectures
- Retail Demo Store
- Sample retail web application and workshop platform demonstrating how to deliver omnichannel personalized customer experiences using Amazon Personalize.
- Live Event Contextualization
- This is a sample code base to illustrate the concept of personalization and contextualization for real-time streaming events. This blog illustrates the concept
- Retail Demo Store
This sample code is made available under a modified MIT license. See the LICENSE file.