Jupyter Notebooks have gained in popularity among data scientists who use them for exploratory analyses, early-stage development and knowledge sharing. This rise in popularity has led to the trend of productionizing Jupyter Notebooks, by providing the tools and support to execute them at scale. Although we wouldn't want to discourage anyone from using their tools of choice, we don't recommend using Jupyter Notebooks for building scalable, maintainable and long-lived production code — they lack effective version control, error handling, modularity and extensibility among other basic capabilities required for building scalable, production-ready code. Instead, we encourage developers and data scientists to work together to find solutions that empower data scientists to build production-ready machine learning models using continuous delivery practices with the right programming frameworks. We caution against productionization of Jupyter Notebooks to overcome inefficiencies in continuous delivery pipelines for machine learning, or inadequate automated testing.