In the previous posts, it is discussed how to package/deploy an R machine learning model with AWS Lambda and to expose the Lambda function via Amazon API Gateway. In this post, I'll demonstrate how to host a web application that services the backend API in a serverless environment.

In previous posts, we discussed how to package and deploy an R machine learning model via Lambda. In this post, I'll demonstrate how to expose the model via Amazon API Gateway.

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.

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

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

In this post, a way to overcome one of R's limitations of lack of multi-threading is discussed by job queuing using the jobqueue package

One option to boost SparkR's performance as a data processing engine is manipulating data in Hive Context rather than in limited SQL Context. In this post, we discuss how to run SparkR in Hive Context.

In this post, we discuss how to execute SparkR in a local and cluster mode.

We discuss how to set up a Spark cluser between 2 Ubuntu guests. Firstly it begins with machine preparation.