Jupyter notebooks play a crucial role in the development stage for data analytics, machine learning, and other data-driven applications. However, moving them into a production environment often leads to a complex set of challenges.  Challenges related to production readiness, version control, testing, reproducibility, and modularity are common.     In this talk, we will delve into the specifics of these challenges and demonstrate how our tool, Versatile Data Kit (VDK), effectively addresses them. From deploying production-relevant code to ensuring linear execution for reproducibility, we will provide insights into practical solutions that could enhance efficiency in your data analytics and machine learning workflows. This session is aimed at those looking to understand the complexities of productionizing notebooks and explore potential methods to overcome these challenges. Benefits to the Ecosystem - solutions to several specific pain points of Productionizing Jupyter notebooks. For this talk, some prior knowledge of Jupyter Notebooks is necessary.