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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.

Getting started tutorials

Probably the simplest pipeline you can build, to help you get started. Click the Run in Google Colab button.
Building on the simple pipeline to add data validation components.
Building on the data validation pipeline to add a feature engineering component.
Building on the simple pipeline to add a model analysis component.

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.
A component-by-component introduction to TFX, including the interactive context, a very useful development tool. Click the Run in Google Colab button.
A tutorial showing how to develop your own custom TFX components.
An introduction to using TFX and Cloud AI Platform Pipelines, to help you learn how to create machine learning pipelines on Google Cloud.
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.