The talk begins with a general discussion of MLOps (Machine Learning Operations) and how it differs from DevOps as applied to traditional (non-ML-based) applications. This is a theme I plan to develop further in upcoming talks, but this slide provides a summary of the main differences:
The second half of the talk describes an MLOPS workflow for building a Shiny application, based on a model trained using the caret package. I used the Azure ML service and the azuremlsdk R package to coordinate the training process and provide a cluster of machines to train multiple models simultaneously and track accuracy to choose the best model to deploy. You can find the complete code behind the demonstration shown in this vignette, included with the azuremlsdk package. (If you haven’t used azuremlsdk before, start with this vignette which sets up some prerequisites.)
The slides used in the talk, along with links to other resources, are also available at the link below or at aka.ms/mlops-r.
GitHub (revodavid): Resources for Machine Learning Operations with R