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TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines

When you're ready to move your models from research to production, use TFX to create and manage a production pipeline.

Run Colab

This interactive tutorial walks through each built-in component of TFX.

See tutorials

Tutorials show you how to use TFX with complete, end-to-end examples.

See the guide

Guides explain the concepts and components of TFX.

How it works

A TFX pipeline is a sequence of components that implement an ML pipeline which is specifically designed for scalable, high-performance machine learning tasks. Components are built using TFX libraries which can also be used individually.

Solutions to common problems

Explore step-by-step tutorials to help you with your projects.

Intermediate
Train and serve a TensorFlow model with TensorFlow Serving

This guide trains a neural network model to classify images of clothing, like sneakers and shirts, saves the trained model, and then serves it with TensorFlow Serving. The focus is on TensorFlow Serving, rather than the modeling and training in TensorFlow.

Intermediate
Create TFX pipelines hosted on Google Cloud

An introduction to TensorFlow Extended (TFX) and Cloud AI Platform Pipelines to create your own machine learning pipelines on Google Cloud. Follow a typical ML development process, starting by examining the dataset, and ending up with a complete working pipeline.

Intermediate
Use TFX with TensorFlow Lite for on-device inference

Learn how TensorFlow Extended (TFX) can create and evaluate machine learning models that will be deployed on-device. TFX now provides native support for TFLite, which makes it possible to perform highly efficient inference on mobile devices.

News & announcements

Check out our blog and YouTube playlist for additional TFX content,
and subscribe to our monthly TensorFlow newsletter to get the
latest announcements sent directly to your inbox.