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

> Work with Azure and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions.

# Azure workflow

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

DataRobot offers an in-depth API that allows you to produce fully automated workflows in your coding environment of choice. This accelerator shows how to enable end-to-end processing of data stored natively in Azure.

In this notebook you'll see how data stored in Azure can be used to train a collection of models on DataRobot. You'll then deploy a recommended model and use DataRobot's batch prediction API to produce predictions and write them back to the source Azure container.

This accelerator notebook covers the following activities:

- Acquire a training dataset from an Azure storage container
- Build a new DataRobot project
- Deploy a recommended model
- Score via DataRobot's batch prediction API
- Write results back to the source Azure container

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