A lively debate unfolds around the role of notebooks in data science, with contrasting views from advocates and critics. While some prefer traditional coding practices, others are exploring innovative ways to integrate notebooks into production workflows. Dillon shares insights on how his company evolved from a notebook service to embracing traditional engineering approaches, reflecting a deep understanding of the machine learning landscape.
