Render htmlwidgets inside a Vue.js application, then replace those widgets with native JavaScript libraries for performance async Shiny cannot reach.

Implement the async feature of R Shiny and find its limits, measured against an alternative app with a JavaScript frontend and an RServe backend.

Deploy plumber, RServe and rApache APIs in Docker containers, then compare the three R API options on example requests and response performance.

Serve an R function as an API three ways, with plumber, RServe and rApache, and see what each option asks of the developer to set up.

Host the web application that calls an R machine learning model on Amazon S3, so both the front end and the Lambda backend run without a server.

Expose an R machine learning model packaged in AWS Lambda through Amazon API Gateway, giving the model a callable HTTP endpoint on AWS.

In the previous post, it is discuss how to develop and package an R machine learning model. In this post, I'll illustrate how to deploy the model via AWS Lambda.

In this post, I'll demonstrate how to test and develop a logistic regression model developed in R. Also the model will be packaged for AWS Lambda.

Render multiple pages in an open source R Shiny application with htmlOutput and renderUI, including login and registration backed by a SQLite database.

In this post, a simple way of internal load balancing is demonstrated by redirecting multiple same applications, depending on the number of processes binded to them