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Let’s imagine that you’ve just finished training your new TensorFlow model and want to start using it in your application(s). One obvious way to do so is to simply import it in the source code of every application that uses it. However, it might be more versatile to keep your model in one place as standalone and simply have applications exchange data with it through API calls. This article will go through the steps of building such a system and deploy the result to Kubernetes.

Here are the basics steps necessary to write TensorFlow code.

This is an example .gitlab-ci.yml file which can be used to containerize and deploy a tensorflow model

A Keras model can be created in various ways, for example using the <a href="https://keras.io/getting-started/sequential-model-guide/" rel="noopener noreferrer nofollow">sequential model</a>: