Implementation using TF and TFX

The testing and debugging guidelines in this course can be complex to implement. You can implement some of the guidelines using TensorFlow and TensorFlow Extended (TFX). TFX is an end-to-end ML pipeline based on TensorFlow. For a demo, view this end-to-end TFX example.

To complement the end-to-end example, the following table lists available resources in TF and TFX by guideline. Only guidelines supported by TF or TFX are listed.

Guideline TF/TFX Implementation
Guidelines for debugging your ML model
Exploring your data to understand it Explore your data using Pandas or Facets.
Validating input data using a data schema Use TensorFlow Data Validation.
Implementing tests for ML code First, debug your TF models with Eager Execution. Then write tests with Tensorflow Testing.
Metrics
Generating model metrics TensorBoard visualizes your TF graph and plots metrics. See Tensorboard: Graph Visualization.
Deployment to Pipeline
Testing model quality in production Use Tensorflow Model Analysis.
Checking for training-serving skew Avoid feature skew by sharing feature engineering code across training and serving by using TFX Transform.
Tracking model staleness --