A TensorFlow Modeling Pipeline using TensorFlow Datasets and TensorBoard

This article investigates TensorFlow components for building a toolset to make modeling evaluation more efficient. Specifically, TensorFlow Datasets (TFDS) and TensorBoard (TB) can be quite helpful in this task. While completing a highly informative AICamp online class taught by Tyler Elliot Bettilyon (TEB) called Deep Learning for Developers, I got interested in creating a more structured way for machine-learning model builders -- like me as the student -- to understand and evaluate various models and observe their performance when applied to new datasets. Since this particular class focused on TensorFlow (TF), I started to investigate TF components for building a toolset to make this type of modeling evaluation more efficient. In doing so, I learned about two components, TensorFlow Datasets (TFDS) and TensorBoard (TB), that can be quite helpful and this blog post discusses their application in this task.