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. [: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)

Videos and Updates

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