From sample dataset to activation, these componentized patterns are designed to help you get the most out of BigQuery ML and other Google Cloud products in production.

• Recommendation systems:
  • How to build a recommendation system on e-commerce data using BigQuery ML. ([Code][#recomm_code] | [Blogpost][#recomm_blog] | Video)
  • How to build an item-item real-time recommendation system on song playlists data using BigQuery ML. ([Code][#bqml_scann_code] | [Reference Guide][#bqml_scann_guide])
• Propensity to purchase model:
  • How to build an end-to-end propensity to purchase solution using BigQuery ML and Kubeflow Pipelines. ([Code][#propen_code] | [Blogpost][#propen_blog])
• Activate on Lifetime Value predictions:
  • How to predict the monetary value of your customers and extract emails of the top customers to use in Adwords for example to create similar audiences. Automation is done by a combination of BigQuery Scripting, Stored Procedure and bash script. ([Code][#ltv_code])
• Clustering:
  • How to build customer segmentation through k-means clustering using BigQuery ML. ([Code][#clustering_code] | Blogpost)
• Demand Forecasting:
  • How to build a time series demand forecasting model using BigQuery ML ([Code][#demandforecasting_code] | Blogpost | Video)

• Propensity to churn model:
  • Churn prediction for game developers using Google Analytics 4 (GA4) and BigQuery ML. (Code | Blogpost(TBA)) | Video(TBA))

This is not an officially supported Google product.

All files in this repository are under the Apache License, Version 2.0 unless noted otherwise.