> ## 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.

# Batch

This tutorial implements the following AWS architecture for handling a SageMaker Batch Transformer.

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  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/96e80461-image.jpeg" />
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The transformer generates an event that kicks off a Lambda function. The transformer generates the model inputs and predictions used by the lambda function.

A separate process is assumed to process actuals as they are received in the system.

The below file is a Jupyter notebook file that should be uploaded to the Sagemaker Notebook Instance.

<Card title="Google Colaboratory" href="https://colab.research.google.com/github/Arize-ai/tutorials_python/blob/main/Partnerships/SageMaker/BatchSageMaker/BatchSageMaker.ipynb" icon="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/cookbooks/gc.png" horizontal />

The Notebook file builds a lambda package in a Gzip to upload to AWS. The below file is the Lambda python function, this should be uploaded to a SageMaker Notebook Instance along with the above notebook.

<Card title="tutorials_python/lambda_function.py at main · Arize-ai/tutorials_python" href="https://github.com/Arize-ai/tutorials_python/blob/main/Partnerships/SageMaker/BatchSageMaker/lambda_function.py" icon="github" horizontal>
  GitHub
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