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.
Learn how Google creates ML products using TFX. TFX runs just about anywhere, including in Cloud AI Pipelines. Training your model is just the beginning, but you can go from zero to hero with Production ML by using TFX, and make your amazing application ready for the world!
Improve website user experience by training a custom machine learning model with site navigation data to predict next pages, and use an Angular app to prefetch the content and improve site speed.
Learn how you can efficiently deploy a model using TFX RunInference API with Google Cloud Dataflow. This post provides a walkthrough of common scenarios, from standard inference, to post processing and the use of RunInference API in multiple locations in the pipeline.
See more ways to participate in the TensorFlow community.