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Insights on Deploying R Models

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Insights on Deploying R Models

Many data scientists don't know how to deploy R models in a production environment or how to make it available to users who don't have R installed. Here are some insights based on my experience and knowledge!

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R is one of the most preferred languages for creating machine learning models. There are various articles around the internet that teach you how to use R and develop models. However, most of us lack the knowledge on deploying R models in the production environment of an organization and on making it available to users who can use the model without having R installed on their system. Almost all data-related software tools have an enterprise version of R, or they have at least an inbuilt integration with R. R can also be deployed stand-alone as web applications or web services.

Here are some insights based on my experience and knowledge of R model deployments.

  • R is engineered to be enterprise-ready by various product development organizations, such as Oracle R Enterprise and Microsoft R Server. Any R code created in these environments is production-ready and easy to deploy. We just need to follow a few steps that are readily available in their documentation for production deployment.

  • R has integrations with various visual analytics and BI tools such as Tableau, Power BI, and Microstrategy. If you are creating an R model and plan on bringing advanced analytics capabilities for your BI dashboards, then deploy the R models using these integrations.

  • If you use SAP HANA as your data warehouse or ERP backend, then deploying the R models using SAP HANA—R integration is a good idea.

  • If you are already using advanced analytics tools such as KNIME, Rapid Miner, IBM SPSS, or SAS, but want to leverage the benefits of R capabilities, we can use the inbuilt integration mechanism that these tools offer.

  • R models that use H2O's algorithms can be converted to POJO classes and can be deployed in a Java environment.

  • We can also create a shiny application to implement R as a web application. There is a live R web application demo at DZone. Check it out!

  • You can also use Plumber to convert your R code into a web API efficiently using some individual one-line comments.

These are some of the possible ways through which we can deploy R models — and I understand that there are many more options! If you know any other techniques, please share in the comments section. It will help me and others to learn.

Learn how taking a DataOps approach will help you speed up processes and increase data quality by providing streamlined analytics pipelines via automation and testing. Learn More.

Topics:
big data ,data science ,r ,data analytics

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