BigQuery ML

Create a classification model

This example shows how to create a pipeline to train a classification model using BigQuery ML, evaluate the model and use it for prediction. In particular, we will create a classification model to estimate customer churn for a telecom company in California.

This example workflow will help you see how telco companies can detect high-risk customers, uncover the reasons behind customer departures, and develop targeted strategies to boost retention and satisfaction by training a classification model.

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Create a regression model

This example shows how to create a pipeline to train a regression model using BigQuery ML, evaluate the model and use it for prediction. In particular, we will create a regression model to predict the average network speed in the LA area.

This example workflow will help you see how telco companies can improve network planning use by training a regression model to estimate the network speed in areas where no measurements are available.

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Forecast

This template shows how to create a forecast model using the BigQuery ML extension package for Workflows. There are three main stages involved:

• Training a model, using some input data and adjusting to the desired parameters,

• Evaluating and understanding the model and its performance,

• Predicting to a given horizon and saving the results.

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Import a model

This example shows how to create a pipeline to import a pre-trained model using BigQuery ML and use it for prediction. In particular, we will import a regression model to predict the ration of crime counts per 1000 population in the Chicago area.

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