TensorFlow in Production tutorials
The best way to learn TensorFlow Extended (TFX) is to learn by doing. These tutorials are focused examples of the key parts of TFX. They include beginner tutorials to get started, and more advanced tutorials for when you really want to dive into more advanced parts of TFX.
Getting started tutorials
1. Starter PipelineProbably the simplest pipeline you can build, to help you get started. Click the Run in Google Colab button.
3. Adding Feature EngineeringBuilding on the data validation pipeline to add a feature engineering component.
TFX on Google Cloud
Google Cloud provides various products like BigQuery, Vertex AI to make your ML workflow cost-effective and scalable. You will learn how to use those products in your TFX pipeline.
Once you have a basic understanding of TFX, check these additional tutorials and guides. And don't forget to read the TFX User Guide.
Complete Pipeline TutorialA component-by-component introduction to TFX, including the interactive context, a very useful development tool. Click the Run in Google Colab button.
Data ValidationThis Google Colab notebook demonstrates how TensorFlow Data Validation (TFDV) can be used to investigate and visualize a dataset, including generating descriptive statistics, inferring a schema, and finding anomalies.
Model AnalysisThis Google Colab notebook demonstrates how TensorFlow Model Analysis (TFMA) can be used to investigate and visualize the characteristics of a dataset and evaluate the performance of a model along several axes of accuracy.
Serve a ModelThis tutorial demonstrates how TensorFlow Serving can be used to serve a model using a simple REST API.
Videos and updates
Subscribe to the TFX YouTube Playlist and blog for the latest videos and updates.