targets-package: targets.tutorial
In kenshuri/targets-tuto: Targets Tutorial

Data science can be slow. A single round of statistical computation can take several minutes, hours, or even days to complete. The 'targets' R package keeps results up to date and reproducible while minimizing the number of expensive tasks that actually run. 'targets' learns how your pipeline fits together, skips costly runtime for steps that are already up to date, runs the rest with optional implicit parallel computing, abstracts files as R objects, and shows tangible evidence that the output matches the underlying code and data. In other words, the package saves time while increasing our ability to trust the conclusions of the research. This hands-on workshop teaches 'targets' using a realistic machine learning case study. Participants begin with the R implementation of a machine learning project, convert the workflow into a 'targets'-powered pipeline, and efficiently maintain the output as the code and data change. The case study comes from an 2018 RStudio AI Blog post by Matt Dancho: https://blogs.rstudio.com/ai/posts/2018-01-11-keras-customer-churn.

kenshuri/targets-tuto documentation built on April 19, 2021, 9:58 a.m.