Reference patterns

All reference patterns that use BigQuery ML

• Building a propensity to purchase solution

  Learn how to build and deploy a propensity to purchase model, use it to get predictions about customer purchasing behavior, and then build a pipeline to automate the workflow.

• Building new audiences based on current customer lifetime value

  Learn how to identify your most valuable current customers and then use them to develop similar audiences in Google Ads.

• Propensity modeling for gaming applications

  Learn how to use BigQuery ML to train, evaluate, and get predictions from several different types of propensity models. Propensity models can help you to determine the likelihood of specific users returning to your app, so you can use that information in marketing decisions.

• Building a k-means clustering model for market segmentation

  Learn how to segment Google Analytics 360 audience data for marketing purposes by creating k-means clusters with BigQuery ML.

• Building an e-commerce recommendation system

  Learn how to build a recommendation system by using BigQuery ML to generate product or service recommendations from customer data in BigQuery. Then, learn how to make that data available to other production systems by exporting it to Google Analytics 360 or Cloud Storage, or programmatically reading it from the BigQuery table.

• Creating and serving embeddings for near real-time recommendations

  Learn how to create and serve embeddings to make real-time similar item recommendations. Use BigQuery ML to create a matrix factorization model to predict the embeddings and the open-source ScaNN framework to build a nearest neighbour index, then deploy the model to AI Platform Prediction for real-time similar items matching.

• Build demand forecasting models

  Learn how to build a time series model that you can use to forecast retail demand for multiple products.

• Building a time series demand forecasting model

  Learn to build an end-to-end solution for forecasting demand for retail products. Use historical sales data to train a demand forecasting model using BigQuery ML, and then visualize the forecasted values in a dashboard.

• Forecasting from Sheets using BigQuery ML

  Learn how to operationalize machine learning with your business processes by combining Connected Sheets with a forecasting model in BigQuery ML. In this specific example, we'll walk through the process for building a forecasting model for website traffic using Google Analytics data. This pattern can be extended to work with other data types and other machine learning models.

• Building a Telecom network anomaly detection application using k-means clustering

  This solution shows you how to build an ML-based network anomaly detection application for telecom networks to identify cyber security threats by using Dataflow, BigQuery ML and Cloud Data Loss Prevention.

• Real-time credit card fraud detection

  Learn how to use transactions and customer data to train machine learning models in BigQuery ML that can be used in a real-time data pipeline to identify, analyze, and trigger alerts for potential credit card fraud.