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# Batch scoring of SPARK machine learning models
# Batch scoring of Spark machine learning models
## Overview
This scenario demonstrates batch scoring of a SPARK machine learning model on Azure Databricks. We use a predictive maintenance scenario where we classify machine sensor readings to classify a set of four machine components into _healthy_ or _unhealthy requiring maintenance_ states. The resulting supervised multi-class classifier model scores batches of new observations through a regularly scheduled Azure Databricks notebook jobs.
This scenario demonstrates batch scoring of a Spark machine learning model on Azure Databricks. We use a predictive maintenance scenario where we classify machine sensor readings to classify a set of four machine components into _healthy_ or _unhealthy requiring maintenance_ states. The resulting supervised multi-class classifier model scores batches of new observations through a regularly scheduled Azure Databricks notebook jobs.
The solution uses methods from the PySpark MLlib machine learning library but the scoring process can be generalized to use any Python or R model hosted on Azure Databricks to make real-time predictions.
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## Azure Databricks
This example is designed to run on Azure Databricks. You can provision the service through the Azure portal at:
This example is designed to run on [Azure Databricks](https://azure.microsoft.com/en-us/services/databricks/) . You can provision the service through the Azure portal at:
https://ms.portal.azure.com/#create/Microsoft.Databricks
If this link does not get you to create an Azure databricks service, you can search for `databricks` in the https://portal.azure.com.
This example will run on the Standard pricing tier.
This example will run on the `Standard pricing tier`. See https://azure.microsoft.com/en-us/pricing/details/databricks/ for information on different pricing tiers.
See https://docs.azuredatabricks.net/getting-started/index.html for detailed documentation on using Azure Databricks.
## Databricks cluster