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Moving from Jupyter notebooks to scripted pipelines with configuration files enables data scientists to scale experiments efficiently. The approach involves creating Python scripts controlled by YAML configuration files, implementing automation for parameter sweeps, and leveraging parallel execution on external compute resources. Adding logging and experiment tracking tools provides oversight and easy comparison of results across hundreds of parallel experiments.

Reducing Time to Value for Data Science Projects: Part 2

Embrace Scripting To Create Your Experimental Pipeline

Configure Your Experiments With a Separate File

Embed Loggers and Experiment Trackers for Easy Oversight