Best practices
This article lists best practices that can help you get started selecting your target attribute, readying your data, and using the models that you create with Predict ML.
Get started
- Define the problem you want to solve and goals to achieve resolution.
Work with your data
- Ensure that your AudienceStream profile has high-quality attributes to signal target behaviors, such as badges or booleans. If none are present, create the attributes as soon as possible to allow more time for data to accumulate.
- Establish best practices regarding collecting and cleaning your data before you begin. For more information on preparing your data before creating a model, see Prepare your data.
- During your data readiness stage, join siloed datasets and consider other characteristics of your organization’s data that can be refined.
Train and deploy models
- Where possible, use longer date ranges for training.
- Deploy your model in a production environment or real-world application.
- Evaluate how well your model is working in production and return to the ’training and testing’ stages as needed for improvements.
Create audiences
- Consider ways to use Tealium Predict to improve or augment existing audiences and make them more efficient.
This page was last updated: January 7, 2023