A DevOps Process for Deploying R to Production
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I've been at the EARL Conference in London this week, and as always it's been inspiring to see so many examples of R being used in production at companies like Sainsbury's, BMW, Austria Post, PartnerRe, Royal Free Hospital, the BBC, the Financial Times, and many others. My own talk, A DevOps Process for Deploying R to Production, presented one process for automating the process of building and deploying R-based applications using Azure Pipelines and Azure Machine Learning Service. The talk at EARL wasn't recorded, but you can see the slides here, and also watch a slightly shorter version of the talk as it was presented at the useR!2019 conference in Toulouse, below:
If you'd like to try setting up a build process for R yourself with Azure Pipelines, this GitHub repository is a good place to start. It provides a simple example of a model built with R, which gets triggered on check-in to the repository (you can see the builds in Pipelines, here). The README.md file also includes links to useful resources on setting up an end-to-end workflow for machine learning.
GitHub (revodavid): MLOps with R and Azure Pipelines
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