Computational notebooks — such as Jupyter and Databricks — have soared in popularity with data scientists thanks to the ease with which text, visualizations and code can be combined on a living document. But what works for the data scientist doesn’t always fit with developers’ needs. Productionizing notebooks is fraught with perils. Our podcast team explores how to use computational notebooks most effectively.