The final step of the machine learning workflow is the
deployment to production. In this phase, we want the trained model
to be deployed on a device, but more than often the device has an
entirely different runtime environment with respect to the one used
TensorFlow, thanks to its SavedModel serialization format, allows
deploying a trained model to several “deployment platforms”. Your
model can run on a browser, in a Java application, in a Python
script, and last but not least on every device that can run a C
program. There is, in fact, a TensorFlow C API that can also be
used for generating language bindings – and that is where Go, with
its FFI for the C language, jumps in. In this talk, we will learn
the basics of the TensorFlow Go bindings, their limitations, and
how the tfgo library simplifies their usage.
Moreover, the flexibility of the SavedModel serialization format is
presented, and we will be able to design a deployment environment
for incremental learning – in Go!