---
title: Best practices
description: 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.
url: https://docs.tealium.com/server-side/predict/getting-started/best-practices/
---
## 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.