How to build and deploy a recommendation system with BigQuery ML

Learn how to use BigQuery ML to train and deploy a recommendation system.

The majority of consumers today expect personalization, but how do you create a recommendation system and use the predicted recommendations for marketing activations, such as personalized emails or ad retargeting campaigns?

This is a step-by-step video that explores the e-commerce recommendation system referenced at and as well as in this notebook ( environment and helps walk you through the entire process of building such a system in your organization.

You will learn how to:
– Process sample data into a format suitable for training a matrix factorization model
– Create, train, and deploy a matrix factorization model.
– Get predictions from the deployed model about what products your customers are most likely to be interested in.
– Export prediction data from BigQuery to Google Analytics 360, Cloud Storage, or programmatically reading it from the BigQuery table.

Solutions guide →
Notebook here →
More Smart Analytics Reference Patterns →

Duration: 00:10:29
Publisher: Google Cloud
You can watch this video also at the source.