Databricks ML Evaluator (deprecated)

Supported pipeline types:

  • Data Collector
The Databricks ML Evaluator processor uses a machine learning model exported with Databricks ML Model Export to generate evaluations, scoring, or classifications of data.
Important: This stage is deprecated and may be removed in a future release.

With the Databricks ML Evaluator processor, you can create pipelines that produce data-driven insights in real time. For example, you can design pipelines that detect fraudulent transactions or that perform natural language processing as data passes through the pipeline.

To use the Databricks ML Evaluator processor, you first build and train the model with Apache Spark MLlib. You then export the trained model with Databricks ML Model Export and save the exported model directory on the Data Collector machine that runs the pipeline.

When you configure the Databricks ML Evaluator processor, you specify the path to the exported model saved on the Data Collector machine. You also specify the root field in the input data to send the model, the output columns to return from the model, and the record field to store the model output.