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 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. See the References section for links to AICamp, TEB and other useful resources.

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