revodavid / mlops-r

Resources for Machine Learning Operations with R

Resources for Machine Learning Operations with R

This repository contains resources for the talk "Machine Learning Operations with R".

Azure Machine Learning service (Azure ML) is Microsoft’s cloud-based machine learning platform that enables data scientists and their teams to carry out end-to-end machine learning workflows at scale. With Azure ML's new open-source R SDK and R capabilities, you can take advantage of the platform’s enterprise-grade features to train, tune, manage and deploy R-based machine learning models and applications.

Presentations

Jan 2020: RStudioConf, San Francisco.
MLOPS for R with Azure Machine Learning: slides (PPTx) | slides (PDF) | Video Recording

Resources

Azure Machine Learning service: Documentation

Azure DevOps: Documentation

azuremlsdk R package: CRAN, GitHub Repository, Documentation. New to the package? Start with these vignettes:

• Tutorial: Create a logistic regression model in R with Azure Machine Learning

• A Deeper Dive into Experiments with R (this is also provided as a vignette in the azuremlsdk package)

Free azure credits: register here. (Credit card required, but won't be charged until you remove limits to allow it.)

Code from presentation

Accidents model, trained and deployed with Azure ML, and shiny app: GitHub
(included in vignettes of azuremlsdk package).

Related Presentations

Machine learning operations: Applying DevOps to data science
AIML50: Slides, code and recording on GitHub

Feedback

If you have comments, suggestions, or questions, feel free to leave an issue in this repository.

David Smith
Cloud Advocate, Microsoft
Twitter: @revodavid

mlops-r's Issues

RShiny deploy options

Hi revodavid,

Thanks for the slides!

I have been looking for different Shiny deployment solution on AzureML. Basically, I am developing an app which shows the model predicitons. Do you know how you could deploy a shiny app on AzureML and whether the app can be easily shared internally?

I know you can do it through databricks, but I am wondering if there is anyway you can deploy it through AzureML computes or AKS services?

Thanks,
Jack