Evento 29 Aprile


Train and deploy TensorFlow models in Go

Start time: 18:00 CEST

Duration: 50min

Format: webinar

Where: online

Language: English

Price: free


Paolo Galeone

ML & CV Researcher


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 during training. 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!


  • Machine learning workflow
  • Tensorflow program
  • Exporting training
  • Go binding
  • Simulating learning

Who it is for

Go developers approaching Machine Learning.


Very basic knowledge of machine learning, some Python and some Go.

Required materials


Certificate of attendance

A certificate of participation will be issued at the end of the session

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