Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE, and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale.
With ever-increasing data volume being generated, datasets used for ML and statistical analysis are growing in tandem. With this brings the challenges of increased development time and compute infrastructure management. To solve these challenges, data scientists have looked to implement parallel data processing techniques. Parallel data processing, or data parallelization, takes large existing datasets and distributes them across multiple processors or nodes to operate on the data simultaneously. This can allow for faster processing time of larger datasets, along with optimized usage on compute. This can help ML

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