A DevOps Process for Deploying R to Production

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

Comments

[Digital Shlok]

The Model Development comprised several steps from the extraction of predictor variables, linear regression analysis, to cross-validation.

After a long time, I have heard something related to model development. Explained each point very appropriately and easy to understand language. Thanks for sharing this video.