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

> Build models in DataRobot with data acquired and prepared in a Spark-backed notebook environment provided by Databricks.

# Databricks workflow

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

DataRobot features an in-depth API that allows data scientists to produce fully automated workflows in their coding environment of choice. This accelerator shows how to pair the power of DataRobot with the Spark-backed notebook environment provided by Databricks.

In this notebook you'll see how data acquired and prepared in a Databricks notebook can be used to train a collection of models on DataRobot. You'll then deploy a recommended model and use DataRobot's exportable Scoring Code to generate predictions on the Databricks Spark cluster.

This accelerator notebook covers the following activities:

- Acquiring a training dataset.
- Building a new DataRobot project.
- Deploying a recommended model.
- Scoring via Spark using DataRobot's exportable Java Scoring Code.
- Scoring via DataRobot's Prediction API.
- Reporting monitoring data to the MLOps agent framework in DataRobot.
- Writing results back to a new table.

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