---
title: End-to-end ML workflow with AWS
description: Work with AWS and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions.

---

# End-to-end ML workflow with AWS {: #end-to-end-ml-workflow-with-aws}

Being one of the largest cloud providers in the world, AWS has multiple ways of storing data within its cloud.

You can use either of two AI accelerators that allow you to source data from S3 or Athena, build and evaluate a model using DataRobot, and send predictions from that model back to S3.

[Access the AI accelerator for S3 on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_S3_template/Amazon_S3_End_to_End.ipynb){ .md-button }

[Access the AI accelerator for AWS Athena on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_Athena_template/AWS_Athena_End_to_End.ipynb){ .md-button }

Each AI accelerator will perform the following steps to help you integrate DataRobot with your data in AWS:

* **Import data for training**:

    * In the S3 AI accelerator, you will be able to take data in the parquet file format, assemble it, and upload it to the AI Catalog in DataRobot.

    * In the Athena AI Accelerator, you will create a JDBC data source within DataRobot to connect to Athena and then pull data in via a SQL query.

* Using the DataRobot Python API, you will have DataRobot build up to 50 different machine learning models while also evaluating how those models perform on this dataset.
