Community Call - Reproducible Workflows at Scale with drake

This 1-hour Community Call on September 24, 2019 will include a presentation by drake developer, Will Landau, and at least 20 minutes for Q & A.

Ambitious workflows in R, such as machine learning analyses, can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the drake R package. drake resolves the dependency structure of your analysis pipeline, skips tasks that are already up to date, executes the rest with optional distributed computing, and organizes the output so you rarely have to think about data files. This talk demonstrates how to create and maintain a realistic machine learning project using drake-powered automation.

Find resources and more details in the post:

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Resources from this Community Call are posted at

  • video
  • @wlandau’s slides
  • collaborative notes from Q & A, with answers from Will
  • official drake, and community-developed resources (@aedobbyn Matt Dray, Kirill Muller, Garrick Aden-Buie)
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