TensorFlow in Production tutorials

The best way to learn TensorFlow Extended (TFX) is to learn by doing. These tutorials are focused examples of the key parts of TFX. They include beginner tutorials to get started, and more advanced tutorials for when you really want to dive into more advanced parts of TFX.

Beginner tutorials

A component-by-component introduction to TensorFlow Extended, using the Keras API and running in a Google Colab notebook. Click the Run in Google Colab button.
A step-by-step tutorial building a TFX pipeline in your local development environment, showing integration with TensorBoard and Jupyter noteboooks.
An introduction to using TensorFlow Extended and Cloud AI Platform Pipelines, to help you learn how to create machine learning pipelines on Google Cloud.

Next steps

Once you have a basic understanding of TFX, check these additional tutorials and guides. And don't forget to read the TFX User Guide.
This Google Colab notebook demonstrates how TensorFlow Data Validation (TFDV) can be used to investigate and visualize a dataset, including generating descriptive statistics, inferring a schema, and finding anomalies.
This Google Colab notebook demonstrates how TensorFlow Model Analysis (TFMA) can be used to investigate and visualize the characteristics of a dataset and evaluate the performance of a model along several axes of accuracy.
This tutorial demonstrates how TensorFlow Serving can be used to serve a model using a simple REST API.