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
When you’re ready to go beyond training a single model, or ready to put your amazing model to work and move it to production, TFX is there to help you build a complete ML pipeline.
The pipeline components are built using TFX libraries which can also be used individually. Below is an overview of those underlying libraries.
TensorFlow Data Validation
TensorFlow Data Validation (TFDV) helps developers understand, validate, and monitor their ML data at scale. TFDV is used to analyze and validate petabytes of data at Google every day, and has a proven track record in helping TFX users maintain the health of their ML pipelines.
When applying machine learning to real world datasets, a lot of effort is required to preprocess data into a suitable format. This includes converting between formats, tokenizing and stemming text and forming vocabularies, and performing a variety of numerical operations such as normalization. You can do it all with tf.Transform.
TensorFlow Model Analysis
TensorFlow Model Analysis (TFMA) enables developers to compute and visualize evaluation metrics for their models. Before deploying any machine learning (ML) model, ML developers need to evaluate model performance to ensure that it meets specific quality thresholds and behaves as expected for all relevant slices of data. For example, a model may have an acceptable AUC over the entire eval dataset, but underperform on specific slices. TFMA gives developers the tools to create a deep understanding of their model performance.
Machine Learning (ML) serving systems need to support model versioning (for model updates with a rollback option) and multiple models (for experimentation via A/B testing), while ensuring that concurrent models achieve high throughput on hardware accelerators (GPUs and TPUs) with low latency. TensorFlow Serving has proven performance handling tens of millions of inferences per second at Google.
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 SAP’s Concur Labs simplified the deployment of BERT models through TensorFlow libraries and extensions in this two-part blog.
Learn how the Google production ML platform, TFX, is changing in 2020. View an exciting case of how Airbus uses TFX.
See more ways to participate in the TensorFlow community.