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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.

TFX 1.0

We are happy to announce the availability of the TFX 1.0.0. This is the initial post-beta release of TFX, which provides stable public APIs and artifacts. You can be assured that your future TFX pipelines will keep working after an upgrade within the compatibility scope defined in this RFC.

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.

TFX on Google Cloud

Google Cloud provides various products like BigQuery, Vertex AI to make your ML workflow cost-effective and scalable. You will learn how to use those products in your TFX pipeline.
Running pipelines on a managed pipeline service, Cloud AI Platform Pipelines.
Using BigQuery as a data source of ML pipelines.
Using cloud resources for ML training with Vertex AI Training.
An introduction to using TFX and Cloud AI Platform Pipelines.

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.
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.