Dockerizing R and Python Web Services – Microsoft Machine Learning Server

Containerization is an approach to software development in which an application or service, its dependencies, and its configuration (abstracted as deployment manifest files) are packaged together as a container image. The containerized application can be tested as a unit and deployed as a container image instance to the host operating system (OS). Docker is an open-source project for automating the deployment of applications as portable, self-sufficient containers that can run on the cloud or on-premises. Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Operationalization refers to the process of deploying R and Python models and code to Machine Learning Server in the form of web services and the subsequent consumption of these services within client applications to affect business results. In this article, We will look into how to build a docker image containing Machine Learning Server 9.3 using Dockerfiles and how-to-perform the following operations using the docker image: Any Linux VM with docker community edition installed.