> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Spell

> Integrating Arize with model serving and tooling platform, Spell

Arize helps you visualize your model performance, understand drift & data quality issues, and share insights learned from your models. Spell is an end-to-end ML platform that provides infrastructure for company to deploy and train models.

<Frame caption="Read more about the platforms on our partnership announcement">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/328cf992-image.jpeg" />
</Frame>

<Info>
  You can either work through on Colab, or follow the steps below for your own model!
</Info>

<Tabs>
  <Tab title="Terminal Only">
    **Step 1:** Logging into `spell` via command line.

    ```ruby theme={null}
    $ spell login
    ```

    **Step 2:** Train and create model with spell.

    ```ruby theme={null}
    $ spell run \
        --github-url https://github.com/spellml/examples \
        --machine-type cpu \
        --mount public/tutorial/churn_data/:/mnt/churn_prediction/ \
        --pip arize --pip lightgbm \
        -- python arize/train.py
    ```

    **Step 3:** Add your Arize `API_KEY` and `SPACE_ID` to `serve_async.py` and `server_sync.py`. You can find your Arize credential details [**here**](https://app.arize.com/admin)

    **Step 4:** Creating your model your model and serving it.

    ```ruby theme={null}
    $ spell model create churn-prediction 'runs/$RUN_ID'
    $ spell server serve \
        --node-group default \
        --min-pods 1 --max-pods 3 \
        --target-requests-per-second 100 \
        --pip lightgbm --pip arize \
        --env ARIZE_SPACE_ID=$ARIZE_SPACE_ID \
        --env ARIZE_API_KEY=$ARIZE_API_KEY \
        churn-prediction:v1 serve_sync.py  # or serve_async.py
    ```

    **Step 5:** Test your working instance, send in some data, and see that your model is observable on Arize.

    ```powershell theme={null}
    $ curl -X POST -d '@test_payload.txt' \
        https://$REGION.$CLUSTER.spell.services/$SPACE/churn-prediction/predict
    ```
  </Tab>

  <Tab title="Colab Notebook">
    |                            |                                                                                                                                                |
    | -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
    | Spell Integration Tutorial | [Colab Link](https://colab.research.google.com/github/Arize-ai/tutorials_python/blob/main/Partnerships/Arize_Tutorial_Spell_Integration.ipynb) |
  </Tab>
</Tabs>