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 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.
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