> 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/snowflake-ml.md).
# Snowflake ML
{% hint style="success" %}
For these templates, you will need to install the [**SnowflakeML**](https://docs.carto.com/carto-user-manual/workflows/components/snowflake-ml) extension package.
{% endhint %}
## Create a classification model
| Snowflake |
| -------------------- |
| :white\_check\_mark: |
This example shows how to create a pipeline to train a classification model using [Snowflake ML](https://docs.snowflake.com/en/user-guide/ml-functions/classification), 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 this example**](https://storage.googleapis.com/carto-workflows-examples/files/sf_ml_classification.sql)
## Create a forecasting model
| Snowflake |
| -------------------- |
| :white\_check\_mark: |
This template shows how to create a forecast model using [Snowflake ML](https://docs.snowflake.com/en/user-guide/ml-functions/classification) through the [extension package](https://docs.carto.com/carto-user-manual/workflows/components/snowflake-ml) 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,
* **Forecasting** and saving the results.
[**Download this example**](https://storage.googleapis.com/carto-workflows-examples/files/sf_ml_forecasting.sql)
---
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