# AWS workflow
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-aws.html

> 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.

# AWS workflow

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](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_S3_template/Amazon_S3_End_to_End.ipynb)

[Access the AI accelerator for AWS Athena on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_Athena_template/AWS_Athena_End_to_End.ipynb)

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

- Import data for training:
- 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.

---
