Tips for Production

Manage Dependencies

Finn continues to get updated on a regular basis. A best practice is to ensure you are using a specific version of Finn for your production forecast. This can be done through the use of the renv package while using Finn on your local machine, or using docker containers for running Finn in the cloud.

Azure ML Pipelines

Finn was built to run at scale in Azure, leveraging spark as the parallel back end. Check out the parallel processing vignette to learn how to get Finn running on Azure services like Databricks. The best way to run Finn in production is through the use of Azure Machine Learning, specifically Azure ML Pipelines.

Below are a few tips for leveraging Azure ML Pipelines