Strong Typing and Workflow Solutions

This chapter explores the impact of strong typing in programming languages on user experience, comparing dynamic languages like Python with strongly typed languages such as Go and Java. It addresses the construction of robust type systems for machine learning workflows and details practical implementations at Lyft, focusing on cross-language dependencies and the integration of protobuf. The discussion also includes the evolution of data workflow systems and the introduction of Jupyter Notebooks, aimed at enhancing data handling and accessibility for engineers and data scientists alike.