MLOps with R: An End-to-End Process for Building Machine Learning Applications by David Smith. Visit https://rstats.ai/nyr/ to learn more. Abstract: As predictive models and machine learning become key components of production applications in every industry, an end-to-end Machine Learning Operations (MLOPS) process becomes critical for reliable and efficient deployment of applications that depend on R-based models. In this talk, I’ll outline the basics of the DevOps process and focus on the areas where MLOPS diverges. The talk will show the complete process of building and deploying an application driven by a machine learning model implemented with R. We will show the process of developing models, triggering model training on code changes, and triggering the CI/CD process for an application when a new version of a model is registered. We will use the Azure Machine Learning service and the “azuremlsdk” package to orchestrate the model training and management process, but the principles will apply to MLOPS processes generally, especially for applications that involve large amounts of data or require significant computing resources. Bio: David Smith is a developer advocate at Microsoft, with a focus on data science and the R community. With a background in Statistics, he writes regularly about applications of R at the Revolutions blog (blog.revolutionanalytics.com), and is a co-author of “Introduction to R”, the R manual. He is one of the founding members of the R Consortium, where he serves on the board as Microsoft's representative. Twitter: https://twitter.com/revodavid Presented at the 2020 R Conference | New York (August 14th, 2020)