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.

Deploy an R machine learning model to AWS Lambda and call it without managing a server.

Package a logistic regression model built in R so it can run on AWS Lambda, after testing it locally.

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

Spread users of an open source Shiny Server app across copies of the same app, by sending each new session to the copy with the fewest users.

Keep an R session responsive while long computations run in the background, by queuing them with the jobqueue package.

Run SparkR in Hive Context to reach the Hive UDFs and window functions the SQL Context lacks, compared against dplyr, plus building Spark with Hive.

Run SparkR in local and cluster mode from an R project, setting environment variables and the library path, then reading JSON and CSV files.

Set up a two node Spark standalone cluster on VirtualBox Ubuntu guests, covering machine preparation, copying the VDI image and password-less SSH.