MLOps for R with Azure Machine Learning David Smith Cloud Advocate, Microsoft @revodavid What is DevOps? People. Process. Products. Build & Test “ DevOps is the union of people, process, and products to enable continuous delivery of value to your end users. Continuous Delivery Develop ” http://bit.ly/WhatIs-DevOps Deploy Plan & Track Operate Monitor & Learn DevOps Process 1 Plan 4 Monitor + Learn Development Production 2 Develop + Test 3 Release Machine Learning Process Prepare data Evaluate Build and train Production DEVOPS MLOPS Manage code (source files) Manage code (source files) Manage data files, notebooks, Rmd docs Manage infrastructure (as code) Manage infrastructure (as code) Manage environments (as code) Source code control Source code control Track experiment outcomes Manage data sets Build executables Builds take hours (mostly) commodity compute Train models Model training may take weeks or months GPU compute Manage build versions Manage model versions Manage reproducible environments Tests (deterministic) Fix bugs with code Tests (probabilistic) Fix bugs with code and/or data Model drift / model retraining Azure Machine Learning Azure ML service SDK for R Open-source R package for use with CRAN R: azuremlsdk • Create Workspaces, Experiments, Compute, Models, and other artifacts with R commands Use any R function/package (and track requirements for deployment) HyperDrive support: smart hyperparameter search with parallel compute Publish models as web services (in Azure or your own infra) Trigger training / deployment pipelines from CI/CD services Accident fatality prediction app Manage Data Train Model Import flat CSV file and clean data Export to datastore Create training cluster Run experiments: GLM, KNN, GLMNET Deploy model Select model by accuracy Deploy R function as container Integrate model Shiny application Call R function via REST endpoint Create compute instance for