Microsoft Machine Learning Server is an enterprise server for hosting and managing parallel and distributed workloads of R and Python processes. Microsoft Machine Learning Server runs on Linux, Windows, Hadoop, and Apache Spark, and it is also available on HDInsight. It provides an execution engine for solutions built using RevoScaleR, revoscalepy, and MicrosoftML packages, and extends open-source R and Python with support for high-performance analytics, statistical analysis, machine learning, and massively large datasets. This functionality is provided through proprietary packages that install with the server. For development, you can use IDEs such as R Tools for Visual Studio and Python Tools for Visual Studio.

When you buy an item on a favored website, does the site show you pictures of what others have bought? Retailers have been building such systems for years, many built using the programming language R. For older implementations of recommender systems, it's time to consider improving performance and scalability by moving these systems to the cloud --the Azure cloud. Recently, we were asked to help a customer improve the performance and process surrounding the R implementation of their recommender solution and host the model in Azure. Many of their early analytic products were built in R, and they wanted to preserve that investment. After a review of their solution, we identified bottlenecks that could be vanquished.

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.

To benefit from Machine Learning Server's web service deployment and remote execution features, you must first configure the server after installation to act as a deployment server and host analytic web services. We will use ARM Template Custom Script Extensions to automate One-Box/Enterprise Configuration. One-box configuration: As the name suggests, one web node and one compute node run on a single machine. This configuration is useful when you want to explore what it is to operationalize R analytics using R Server. It is perfect for testing, proof-of-concepts, and small-scale prototyping, but might not be appropriate for production usage.

Azure Machine Learning is an integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments, and deploy models at cloud scale. Together, these applications and services help significantly accelerate your data science project development and deployment. Azure Machine Learning fully supports open source technologies. You can execute your experiments in managed environments such as Docker containers and Spark clusters.