Explore the intersection of machine learning interpretability and model optimization in this 18-minute conference talk by Jinen Setpal, Machine Learning Engineer at DagsHub. Delve into the concept of using interpretability techniques as feedback loops to enhance training effectiveness without significantly increasing computational resources or time. Learn how to bridge the gap between mathematical optimization functions and intuitive understanding of model behavior. Discover practical approaches to incorporate interpretability tools into the training process, potentially leading to more effective and comprehensible models. Gain insights into applying these techniques to complex architectures and follow along with a TensorFlow-based case study demonstration that illustrates the implementation of interpretability-driven feedback loops in machine learning workflows.