Design Considerations - Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker

Design Considerations


By default, Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker uses a simple, example dataset to train the machine learning (ML) model. However, you can customize the solution to use your own dataset. To train the model on your own dataset, you must modify the included notebook to point the model to your dataset. You must also create your own Amazon Athena query, and modify the solution’s AWS Lambda function to point to that query. For more information, see the appendix.

Amazon Pinpoint

Before you deploy this solution, you must have a configured Amazon Pinpoint project in the same AWS Region where you plan to deploy the solution. If you do not already have an existing project, you can create one. For more information, see Create a New Amazon Pinpoint Project in the Amazon Pinpoint User Guide.


If you have used the Amazon Pinpoint API, you might have seen references to applications. In Amazon Pinpoint, an application is the same as a project.

Regional Deployment

This solution uses Amazon Pinpoint which is currently available in specific AWS Regions only. Therefore, you must launch this solution in a region where Amazon Pinpoint is available. For the most current service availability by region, see AWS service offerings by region.