This 1-hour Community Call will include a presentation by drake developer, Will Landau, and at least 20 minutes for Q & A. All are welcome to join. No RSVP needed.
*rOpenSci Community Call on Reproducible Workflows at Scale with drake* Tuesday September 24th, 9-10am PDT. (_Find your local time <https://bit.ly/2TaBfCo>_) *Details* and resources: _https://ropensci.org/blog/2019/08/08/commcall-sep2019/_ Tweet to share and invite your friends: <https://twitter.com/rOpenSci/status/1151690383065112577>https://twitter.com/rOpenSci/status/1159479380617228288 *Abstract*: 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. Cheers, Stefanie Butland rOpenSci Community Manager ------------------------------------------ The Carpentries: discuss Permalink: https://carpentries.topicbox.com/groups/discuss/T0496d7176229fcc1-M95650ef66940f7d3862ad4c9 Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription
