TensorFlow in Production Tutorials
These tutorials will get you started, and help you learn a few different ways of working with TFX for production workflows and deployments. In particular, you'll learn the two main styles of developing a TFX pipeline:
- Using the
InteractiveContextto develop a pipeline in a notebook, working with one component at a time. This style makes development easier and more Pythonic. - Defining an entire pipeline and executing it with a runner. This is what your pipelines will look like when you deploy them.
Getting started tutorials
1. Starter Pipeline
Probably the simplest pipeline you can build, to help you get started. Click the Run in Google Colab button.2. Adding Data Validation
Building on the simple pipeline to add data validation components.3. Adding Feature Engineering
Building on the data validation pipeline to add a feature engineering component.4. Adding Model Analysis
Building on the simple pipeline to add a model analysis 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.
Running on Vertex Pipelines
Running pipelines on a managed pipeline service, Vertex Pipelines.Read data from BigQuery
Using BigQuery as a data source of ML pipelines.Vertex AI Training and Serving
Using cloud resources for ML training and serving with Vertex AI.TFX on Cloud AI Platform Pipelines
An introduction to using TFX and Cloud AI Platform Pipelines.Next steps
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 Tutorial
A component-by-component introduction to TFX, including the interactive context, a very useful development tool. Click the Run in Google Colab button.Custom Component Tutorial
A tutorial showing how to develop your own custom TFX components.Data Validation
This 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 Analysis
This 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 Model
This 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.