ogdendc / parallel_models_blog

Accompanying solution accelerator notebook for the Databricks blog on parallel training and inference

Training many machine learning models in parallel using Databricks and PandasUDFs

The Databricks Notebook within this repository provides a detailed, step-by-step example of training multiple machine learning models in parallel on different datasets. It includes the following steps.

• Configuring the Databricks Cluster
• Leveraging PandasUDFs to train machine learning models in parallel on different groups of a dataset.
• Tuning model parameters using Hyperopt
• Logging multiple models to a single MLflow Experiment Run
• Applying multiple models for inference to different groups of data in parallel

This repository can be cloned into a Databricks Repo; the code is self contained and can be run in any Databricks environment. The most recent testing of this notebook leveraged the Databricks ML Runtime version 10.5.

basic change for demo