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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 InteractiveContext to 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.
[:octicons-arrow-right-24: Starter Pipeline](tutorials/tfx/penguin_simple)
- __2. Adding Data Validation__
---
Building on the simple pipeline to add data validation components.
[:octicons-arrow-right-24: Data Validation](tutorials/tfx/penguin_tfdv)
- __3. Adding Feature Engineering__
---
Building on the data validation pipeline to add a feature engineering component.
[:octicons-arrow-right-24: Feature Engineering](tutorials/tfx/penguin_tft)
- __4. Adding Model Analysis__
---
Building on the simple pipeline to add a model analysis component.
[:octicons-arrow-right-24: Model Analysis](tutorials/tfx/penguin_tfma)
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.
[:octicons-arrow-right-24: Vertex Pipelines](tutorials/tfx/gcp/vertex_pipelines_simple)
- __Read data from BigQuery__
---
Using BigQuery as a data source of ML pipelines.
[:octicons-arrow-right-24: BigQuery](tutorials/tfx/gcp/vertex_pipelines_bq)
- __Vertex AI Training and Serving__
---
Using cloud resources for ML training and serving with Vertex AI.
[:octicons-arrow-right-24: Vertex Training and Serving](tutorials/tfx/gcp/vertex_pipelines_vertex_training)
- __TFX on Cloud AI Platform Pipelines__
---
An introduction to using TFX and Cloud AI Platform Pipelines.
[:octicons-arrow-right-24: Cloud Pipelines](tutorials/tfx/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.
[:octicons-arrow-right-24: Keras](tutorials/tfx/components_keras)
- __Custom Component Tutorial__
---
A tutorial showing how to develop your own custom TFX components.
[:octicons-arrow-right-24: Custom Component](tutorials/tfx/python_function_component)
- __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.
[:octicons-arrow-right-24: Data Validation](tutorials/data_validation/tfdv_basic)
- __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.
[:octicons-arrow-right-24: Model Analysis](tutorials/model_analysis/tfma_basic)
- __Serve a Model__
---
This tutorial demonstrates how TensorFlow Serving can be used to serve a
model using a simple REST API.
[:octicons-arrow-right-24: Model Analysis](tutorials/serving/rest_simple)