When developing ML models, defining and selecting the model architecture will be fundamental to ensure the best possible outcomes.  Parameters that define the model architecture are referred to as hyperparameters and the process of searching for the ideal model architecture is referred to as hyperparameter tuning.  Today on the show, Ben and Michael discuss hyperparameter tuning and how to implement this into your ML modeling.