# 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)

<figure><img src="/files/71jxvS4wqvDJfGUOgOxM" alt=""><figcaption></figcaption></figure>

## 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)

<figure><img src="/files/6RKb5mtDosrmShYl4I9v" alt=""><figcaption></figcaption></figure>


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