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.
|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.|
|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||--|