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.
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.
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.
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.
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.
OpenX leveraged several products in the TensorFlow ecosystem & Google Cloud, including TF Serving and Kubeflow Pipelines, to build a service that prioritizes traffic to demand side platforms in the adtech space.
In this update we’ll cover TFX basics and highlight what's new this year to help you get started. We'll also show you a hands-on look at how to put together a production pipeline system with TFX.
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