Building Deep Learning Pipelines with Tensorflow Extended

You can check the code for this tutorial here. Once you finish your model experimentation it is time to roll things to production. Rolling Machine Learning to production is not just a question of wrapping the model binaries with a REST API and starting to serve it, but and making it possible to re-create (or update) and re-deploy your model. That means the steps from preprocessing data to training the model to roll it to production (we call this a Machine Learning Pipeline) should be deployed and able to be run as easily as possible while making it possible to track it and parameterize it (to use different data, for example). In this post, we will see how to build a Machine Learning Pipeline for a Deep Learning model using Tensorflow Extended (TFx), how to run and deploy it to Google Vertex AI and why should we use it.