This is a companion discussion topic for the original entry at:
Deploying TensorFlow models with tfdeploy
https://www.rstudio.com/resources/videos/deploying-tensorflow-models-with-tfdeploy/
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.
Deploying TensorFlow models with tfdeploy, Javier Luraschi, @javierluraschi
Javier Luraschi - Software Engineer
Javier is a Software Engineer with experience in technologies ranging from desktop, web, mobile and backend; to augmented reality and deep learning applications. He previously worked for Microsoft Research and SAP and holds a double degree in Mathematics and Software Engineering.