The premier IDE for R
RStudio anywhere using a web browser
Put Shiny applications online
Shiny, R Markdown, Tidyverse and more
Do, share, teach and learn data science
An easy way to access R packages
Let us host your Shiny applications
A single home for R & Python Data Science Teams
Scale, develop, and collaborate across R & Python
Easily share your insights
Control and distribute packages
RStudio
RStudio Server
Shiny Server
R Packages
RStudio Cloud
RStudio Public Package Manager
shinyapps.io
RStudio Team
RStudio Workbench
RStudio Connect
RStudio Package Manager
rstudio::conf 2018 Interoperabilityerability
Deploying TensorFlow models with tfdeploy
March 4, 2018
This talk presents tools and packages now available to the R community to test and deploy TensorFlow models at scale across services like: TensorFlow Serving, Google Cloud and RStudio Connect. This talks gives an overview on how to train a model in TensorFlow, Keras or TensorFlow Estimators, then explains how to export models with a common interface across all packages, covers testing the exported models locally and explains different deployment services available and use cases for each of them. This talk closes with a walkthrough in RStudio covering training, testing and deployment. It also briefly covers an additional deployment alternative using kerasjs and answers a few questions from the audience.
Javier is the author of “Mastering Spark with R”, pins, sparklyr, mlflow and torch. He holds a double degree in Math and Software Engineer and decades of industry experience with a focus on data analysis. Javier is currently working on a project of his own; and previously worked in RStudio, Microsoft Research and SAP.