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

> Take an ML model that has been built with DataRobot and deploy it to run within AWS SageMaker.

# SageMaker workflow

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_sagemaker_deployment/dr_model_sagemaker.ipynb)

If you already use SageMaker for hosting models, you can still make use of the powerful features of DataRobot, including AutoML and time series modeling. You can integrate DataRobot into your existing deployment processes. Likewise, you can use this accelerator to deploy a DataRobot-built model in another environment. In this accelerator, you will take an ML model that has been built and refined within DataRobot and deploy it to run within AWS SageMaker.

To help with the setup of AWS services to run the model, this code will also help provision any extra items that you may not have set up:

### AWS

- ECR Repository
- S3 Bucket
- IAM Role for SageMaker
- SageMaker inference model
- SageMaker endpoint configuration
- SageMaker endpoint (for real time predictions)
- SageMaker batch transform job (for batch predictions)

### DataRobot

- DataRobot AutoML Project
- DataRobot AutoML Models
- Scoring Code JAR file of AutoML Model

## What you will learn

- Programmatically go through the end-to-end steps of building a model with DataRobot
- Export and host the model in AWS SageMaker

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