I wish the diagrams were bigger, they are hard to read and a bit blurry.
One of the interesting points, that is often overlooked in ML is model deployment. They mention tensorflow, which has a model export feature that you can use as long as your client can run the tensorflow runtime. But they don't seem to be using that b/c they said they just exported the weights and are using it go which would seem to imply you did some type of agnostic export of raw weight values. The nice part of the TF export feature is that it can be used to recreate your architecture on the client. Bu they did mention Keras too which allows you to export your architecture in a more agnostic way as it can work on many platform such as Apples new CoreML which can run Keras models.
One of the interesting points, that is often overlooked in ML is model deployment. They mention tensorflow, which has a model export feature that you can use as long as your client can run the tensorflow runtime. But they don't seem to be using that b/c they said they just exported the weights and are using it go which would seem to imply you did some type of agnostic export of raw weight values. The nice part of the TF export feature is that it can be used to recreate your architecture on the client. Bu they did mention Keras too which allows you to export your architecture in a more agnostic way as it can work on many platform such as Apples new CoreML which can run Keras models.