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.
-
Scalable Operations examples for your Amazon Personalize deployments
- MLOps
- 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.
- 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
- MLOps
-
Workshops
- Workshops/ folder contains a list of our most current workshops:
- POC in a Box
- Re:invent 2019
- Immersion Days
- Workshops/ folder contains a list of our most current workshops:
-
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.
This sample code is made available under a modified MIT license. See the LICENSE file.