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While TensorFlow models are typically defined and trained using R or Python code, it is possible to deploy TensorFlow models in a wide variety of environments without any runtime dependency on R or Python:
TensorFlow Serving is an open-source software library for serving TensorFlow models using a gRPC interface.
CloudML is a managed cloud service that serves TensorFlow models using a REST interface.
RStudio Connect provides support for serving models using the same REST API as CloudML, but on a server within your own organization.
TensorFlow models can also be deployed to mobile and embedded devices including iOS and Android mobile phones and Raspberry Pi computers. The tfdeploy package includes a variety of tools designed to make exporting and serving TensorFlow models straightforward. For documentation on using tfdeploy, see the package website at https://tensorflow.rstudio.com/tools/tfdeploy/.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.