> For the complete documentation index, see [llms.txt](https://academy.carto.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://academy.carto.com/creating-workflows/workflow-templates/bigquery-ml.md).
# BigQuery ML
{% hint style="success" %}
For this templates, you will need to install the [**BigQuery ML extension package**](https://docs.carto.com/carto-user-manual/workflows/components/bigquery-ml).
{% endhint %}
### Create a classification model
CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL
CARTO DW
✅
✅
This example shows how to create a pipeline to train a classification model using [BigQuery ML](https://cloud.google.com/bigquery/docs/e2e-journey), 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.
[**Download example**](https://storage.googleapis.com/carto-workflows-examples/files/bq_ml_create_classification_model.sql)
### Create a regression model
CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL
CARTO DW
✅
✅
This example shows how to create a pipeline to train a regression model using [BigQuery ML](https://cloud.google.com/bigquery/docs/e2e-journey), 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.
[**Download example**](https://storage.googleapis.com/carto-workflows-examples/files/bq_ml_create_regression_model.sql)
### Forecast
CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL
CARTO DW
✅
✅
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.
[**Download example**](https://storage.googleapis.com/carto-workflows-examples/files/bq_ml_forecast.sql)
### Import a model
CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL
CARTO DW
✅
✅
This example shows how to create a pipeline to import a pre-trained model using [BigQuery ML](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-onnx) 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.
[**Download example**](https://storage.googleapis.com/carto-workflows-examples/files/bq_ml_import.sql)
---
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