In this post, I'll illustrate how a web service is created using FastAPI framework where tasks are sent to multiple workers. The workers are built with Celery and Rserve. Redis is used as a message broker/result backend for Celery and a key-value store for Rserve. Demos can be run in both Docker Compose and Kubernetes.

In this post, I'll demonstrate how to create a Linux development environment on Windows using WSL. Also an example app (Rserve web service with a sidecar container) on Minikube will be demonstrated.

LocalStack provides an easy-to-use test/mocking framework for developing AWS applications. In this post, I'll demonstrate how to utilize LocalStack for development using a web service.

Cronicle is a multi-server task scheduler and runner. In this post, multi-server configuration of Cronicle will be demonstrated with Docker and Nginx as load balancer.

Although R Shiny added async features but it has limitation when compared to Javascript. In this post, I'll demonstrate how to render htmlwigets in a Vue application in a more performant way as well as how to replace those widgets with native Javascript libraries.

In this post, it'll be demonstrated how to implement the async feature of Shiny. Then its limitation will be discussed with an alternative app, which is built by JavaScript for the frontend and RServe for the backend.

In part I, it is discussed how to serve an R function with plumber, Rserve and rApache. In this post, the APIs are deployed in a Docker container and, after showing example requests, their performance is compared.

API is an effective way of distributing analysis outputs to external clients. When it comes to API development with R, however, there are not many choices. In this post, serving an R function with plumber, Rserve and rApache is discussed.

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.