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Research with R and Production with Python

Matt Dancho and Jarrell Chalmers lay out an argument:

The decision can be challenging because they both Python and R have clear strengths.

R is exceptional for Research – Making visualizations, telling the story, producing reports, and making MVP apps with Shiny. From concept (idea) to execution (code), R users tend to be able to accomplish these tasks 3X to 5X faster than Python users, making them very productive for research.

Python is exceptional for Production ML – Integrating machine learning models into production systems where your IT infrastructure relies on automation tools like Airflow or Luigi.

They make a pretty solid argument. I’ve launched success R-based projects using SQL Server Machine Learning Services, but outside of ML Services, my team’s much more likely to deploy APIs in Python, and we’re split between Dash and Shiny for visualization. H/T R-Bloggers

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