The deployment adheres to best practices for following an AWS Multi-Account strategy using AWS Organizations.įigure 1. This is illustrated in the diagram that follows. We show how to deploy a Open Source RStudio Server and a Shiny Server in a serverless architecture from an automated deployment pipeline built with AWS Developer Tools. The goal is to build a shiny application to surface breast cancer prediction insights against a set of parameters to users. The use case discussed involves pre-processing a dataset, and training a machine learning model in RStudio. We will then demonstrate a Data Science use case in RStudio and create an application on Shiny. We use these services: AWS Fargate, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic File System (Amazon EFS), AWS DataSync, and Amazon Simple Storage Service (Amazon S3). In this post, we describe and deliver the infrastructure code to run a secure, scalable and highly available RStudio and Shiny Server installation on AWS. In this previous blog, we provided a solution architecture to run Data Science use cases for medium to large enterprises across industry verticals. These visualizations are traditionally hosted on legacy unix servers along with Shiny Server to support analytics. RStudio Server is a popular Integrated Development Environment (IDE) for R, which is used to render analytics visualizations for faster decision making. Data scientists continuously look for ways to accelerate time to value for analytics projects.
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