End-to-end workflows with DataRobot and Databricks
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 DataRobot's MLOps agent framework.