# TFX | ML Production Pipelines # 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](https://colab.sandbox.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/tfx/penguin_simple.ipynb) Get started by exploring each built-in component of TFX. [View tutorials](https://www.tensorflow.org/tfx/tutorials) Learn how to use TFX with end-to-end examples. [View the guide](https://www.tensorflow.org/tfx/guide) Guides explain the concepts and components of TFX. [Explore addons](https://www.tensorflow.org/tfx/addons) Additional TFX components contributed by the community. ![](https://www.tensorflow.org/static/site-assets/images/marketing/learn/tfx-hero.svg) ### 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. Ingest \& validate data *** ** * ** *** ExampleGen *** ** * ** *** Ingests data into TFX pipelines and optionally splits the input dataset. [See guide](http://www.tensorflow.org/tfx/guide/examplegen) [![ML Metadata icon](http://www.tensorflow.org/static/site-assets/images/marketing/learn/tfx-ml-metadata.svg) ML Metadata](http://www.tensorflow.org/tfx/guide/mlmd) StatisticsGen *** ** * ** *** Generates features statistics over both training and serving data. [See guide](http://www.tensorflow.org/tfx/guide/statsgen) SchemaGen *** ** * ** *** Creates schema by inferring types, categories, and ranges from the training data. [See guide](http://www.tensorflow.org/tfx/guide/schemagen) ExampleValidator *** ** * ** *** Identifies anomalies in training and serving data. [See guide](http://www.tensorflow.org/tfx/guide/exampleval) [![TensorFlow Data Validation icon](http://www.tensorflow.org/static/site-assets/images/marketing/learn/tfx-data-validation.svg) TensorFlow Data Validation](http://www.tensorflow.org/tfx/guide/tfdv) Train \& analyze model *** ** * ** *** Transform *** ** * ** *** Performs feature engineering on the dataset. [See guide](http://www.tensorflow.org/tfx/guide/transform) [![TensorFlow Transform icon](http://www.tensorflow.org/static/site-assets/images/marketing/learn/tfx-transform.svg) TensorFlow Transform](http://www.tensorflow.org/tfx/transform/get_started) Tuner *** ** * ** *** Tunes the hyperparameters of the model. [See guide](http://www.tensorflow.org/tfx/guide/tuner) Trainer *** ** * ** *** Trains a TensorFlow model. [See guide](http://www.tensorflow.org/tfx/guide/trainer) [![TensorFlow icon](http://www.tensorflow.org/static/site-assets/images/marketing/learn/tfx-tensorflow.svg) TensorFlow](http://www.tensorflow.org/tfx/guide/train) Evaluator *** ** * ** *** Performs deep analysis of training results and helps validate exported models. [See guide](http://www.tensorflow.org/tfx/guide/evaluator) InfraValidator *** ** * ** *** Checks the model is actually servable from the infrastructure, and prevents bad models from being pushed. [See guide](http://www.tensorflow.org/tfx/guide/infra_validator) [![TensorFlow Model Analysis icon](http://www.tensorflow.org/static/site-assets/images/marketing/learn/tfx-model-analysis.svg) TensorFlow Model Analysis](http://www.tensorflow.org/tfx/guide/tfma) Deploy in production *** ** * ** *** Pusher *** ** * ** *** Deploys the model on a serving infrastructure. [See guide](http://www.tensorflow.org/tfx/guide/pusher) [![TensorFlow Serving, TF Lite & TFJS icon](http://www.tensorflow.org/static/site-assets/images/marketing/learn/tfx-serving-lite-tjs.svg) TensorFlow Serving, TF Lite \& TFJS](http://www.tensorflow.org/tfx/guide#deployment_targets) ## How companies are using TFX [See case studies](https://www.tensorflow.org/about/case-studies?filter=TFX) [Spotify](https://labs.spotify.com/2020/01/16/for-your-ears-only-personalizing-spotify-home-with-machine-learning/) [Airbus](https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html) [Gmail](https://security.googleblog.com/2020/02/improving-malicious-document-detection.html) [OpenX](https://blog.tensorflow.org/2021/02/how-openx-trains-and-serves-for-million-queries-per-second.html) ## Solutions to common problems Explore step-by-step tutorials to help you with your projects. ![](https://www.tensorflow.org/static/site-assets/images/marketing/cards/tile_tensorflow_serving.jpg) Intermediate [Train and serve a TensorFlow model with TensorFlow Serving](https://www.tensorflow.org/tfx/tutorials/serving/rest_simple) 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. ![](https://www.tensorflow.org/static/site-assets/images/marketing/cards/tfx_platform_pipelines.jpg) Intermediate [Create TFX pipelines hosted on Google Cloud](https://www.tensorflow.org/tfx/tutorials/tfx/cloud-ai-platform-pipelines) An introduction to 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. ![](https://www.tensorflow.org/static/site-assets/images/marketing/cards/tfx_for_mobile.jpg) Intermediate [Use TFX with TensorFlow Lite for on-device inference](https://www.tensorflow.org/tfx/tutorials/tfx/tfx_for_mobile) Learn how 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](https://blog.tensorflow.org/search?label=TFX&max-results=20) and [YouTube playlist](https://goo.gle/tfx-youtube) for additional TFX content, and subscribe to our TensorFlow newsletter to get the latest announcements sent directly to your inbox. [Sign up](https://www.tensorflow.org/subscribe) ## Community participation See more ways to participate in the TensorFlow community. [Community](https://www.tensorflow.org/community) [TFX on GitHub](https://github.com/tensorflow/tfx) [ML Metadata](https://github.com/google/ml-metadata) [TensorFlow Data Validation](https://github.com/tensorflow/data-validation) [TensorFlow Transform](https://github.com/tensorflow/transform) [TensorFlow Model Analysis](https://github.com/tensorflow/model-analysis) [TensorFlow Serving](https://github.com/tensorflow/serving) [Stack Overflow](https://stackoverflow.com/questions/tagged/tfx) [ML Metadata](https://stackoverflow.com/questions/tagged/mlmd) [TensorFlow Data Validation](https://stackoverflow.com/questions/tagged/tensorflow-data-validation) [TensorFlow Transform](https://stackoverflow.com/questions/tagged/tensorflow-transform) [TensorFlow Model Analysis](https://stackoverflow.com/questions/tagged/tensorflow-model-analysis) [TensorFlow Serving](https://stackoverflow.com/questions/tagged/tensorflow-serving) [Issues, bug reports, and feature requests](https://github.com/tensorflow/tfx/issues) [ML Metadata](https://github.com/google/ml-metadata/issues) [TensorFlow Data Validation](https://github.com/tensorflow/data-validation/issues) [TensorFlow Transform](https://github.com/tensorflow/transform/issues) [TensorFlow Model Analysis](https://github.com/tensorflow/model-analysis/issues) [TensorFlow Serving](https://github.com/tensorflow/serving/issues) [Ask a question on TensorFlow Forum](https://discuss.tensorflow.org/tag/tfx) [Join the TFX-Addons Special Interest Group](https://github.com/tensorflow/tfx-addons) [Explore Dev Library community projects](https://devlibrary.withgoogle.com/products/ml) ## Get started with TFX [Explore tutorials](https://www.tensorflow.org/tfx/tutorials)