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:
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Tutorial: Create a logistic regression model in R with Azure Machine Learning
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A Deeper Dive into Experiments with R (this is also provided as a vignette in the
azuremlsdkpackage)
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