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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 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.

    Starter Pipeline

  • 2. Adding Data Validation

    Building on the simple pipeline to add data validation components.

    Data Validation

  • 3. Adding Feature Engineering

    Building on the data validation pipeline to add a feature engineering component.

    Feature Engineering

  • 4. Adding Model Analysis

    Building on the simple pipeline to add a model analysis component.

    Model Analysis

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.

    Vertex Pipelines

  • Read data from BigQuery

    Using BigQuery as a data source of ML pipelines.

    BigQuery

  • Vertex AI Training and Serving

    Using cloud resources for ML training and serving with Vertex AI.

    Vertex Training and Serving

  • TFX on Cloud AI Platform Pipelines

    An introduction to using TFX and Cloud AI Platform Pipelines.

    Cloud 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.

    Keras

  • Custom Component Tutorial

    A tutorial showing how to develop your own custom TFX components.

    Custom 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.

    Data Validation

  • 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.

    Model Analysis

  • Serve a Model

    This tutorial demonstrates how TensorFlow Serving can be used to serve a model using a simple REST API.

    Model Analysis

Videos and Updates

Subscribe to the TFX YouTube Playlist and blog for the latest videos and updates.