# Azure workflow

> Azure workflow - 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.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.573403+00:00` (UTC).

## Primary page

- [Azure workflow](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-azure.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [AI integrations and platforms](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/index.html): Linked from this page.

## Documentation content

[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