This article describes the Tealium Predict ML product and how it is used to create, train, and deploy machine learning models to make predictions about visitor behavior.
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This article describes data compliance as it pertains to General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) and how data is used, shared, and stored when using Te...
This article provides detailed descriptions of model strength quality scores.
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The quality of any machine learning model created in Tealium Predict or an...
This article describes how a Tealium Predict ML data model works and the components of a model.
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Tealium Predict works with the Tealium AudienceStream&nb...
This article describes the model overview screen and how to use the sparkline graph as a guideline to the ongoing health of a deployed model.
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The dashbo...
This article provides best practices and recommendations for retraining a model.
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Although machine learning and statistics tools are not designed to guar...
This article describes optional review steps and how to initiate the first training for your model:
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Use the following sections as a guide to review your...
This article describes how to select add a model, select a target attribute, an output attribute, and then name your model and output attribute for future use.
To learn more about the differences betw...
This article describes how to delete a model. You cannot delete a version of a model, only an entire model.
Use the following steps to delete a model:
Go to the Model Explorer screen.
Click the model...
This article describes how to deploy or undeploy one or more versions of a trained model.
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You are now ready to deploy your trained model. Use the steps ...
This article describes how to retrain a model after evaluating the model and determining changes are required to improve predictions.
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Use the following ...
Visitor stitching does not impact modeling results in Tealium Predict.
Models are trained on visitor profiles, not visits. The actual and predicted values that display in the Confusion Matrix re...
This article describes Machine Learning technology concepts, goals, audiences, and technological advances.
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Machine learning is a subfield of artificial ...
This article provides a generic overview of the differences between machine learning and artificial intelligence.
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Artificial intelligence and machine le...
This article provides detailed information about model scoring techniques and formulas used to assign scores and rating to deployed models in the Tealium Predict ML product.
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This article serves as a guideline of items to consider when creating audiences using results from Tealium Predict.
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After a model is deployed and making...
This article provides an overview of how to evaluate your trained version before deploying your model.
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Use the following sections as a guide to view mod...
This article describes data wellness concepts and actionable steps you can take to examine and optimize the readiness of your data layer before starting with Tealium Predict.
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This article provides a descriptions of various approaches you can use to define your strategy and goals before you begin creating and training models.
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This article describes what is required to use the Tealium Predict ML product, suggested steps to take before you begin to ensure ideal results, and available services.
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This article describes the Tealium Predict ML product tiers and feature availability based on the number of models deployed and trained.
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The trial tier of Tealium Predict is availab...
This article defines general statistical modeling terminology, terms specific to Tealium products, and terms used in the Tealium Predict ML interface.
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A...