Data science, machine learning, and analytics have re-defined how we look at the world. The R community plays a vital role in that transformation and the R language continues to be the de-facto choice for statistical computing, data analysis, and many machine learning scenarios. The importance of R was first recognized by the SQL Server team back in 2016 with the launch of SQL ML Services and R Server. Over the years we have added Python to SQL ML Services in 2017 and Java support through our language extensions in 2019. Earlier this year we also announced the general availability of SQL ML Services into Azure SQL Managed Instance.

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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 connect with potential customers, our marketers and sellers at Microsoft depend on good-quality leads. But sometimes people fill out online forms with fake names, gibberish, or even profanity. We distinguish fake company names from legitimate names in our data using the programming language R, the Naive Bayes classifier algorithm, Microsoft Machine Learning Server, and a data quality service that we built. This solution helps us weed out fake names and prioritize good leads for our sales and marketing teams.

In September 2017, Microsoft R Server was released under the new name of Microsoft Machine Learning Server. In version 9.2.1, Machine Learning Server added support for the full data science lifecycle of Python-based analytics to its list of machine learning and AI capabilities enhancements. The R capabilities have also been enhanced. Microsoft continues its commitment and development in R -- not only in the latest Machine Learning Server release, but also in the newest Microsoft R Client and Microsoft R Open releases. Moving from R Server to Machine Learning Server is as easy as ever.

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.

In September 2017, Microsoft R Server was released under the new name of Microsoft Machine Learning Server. In 9.2.1, Machine Learning Server added support for the full data science lifecycle of Python-based analytics to its list of machine learning and AI capabilities enhancements. The R capabilities have also been enhanced. Microsoft continues its commitment and development in R -- not only in the latest Machine Learning Server release, but also in the newest Microsoft R Client and Microsoft R Open releases. Moving from R Server to Machine Learning Server is as easy as ever.