Explore a streamlined approach to machine learning model development in this JuliaCon 2024 conference talk. Learn how to reimagine ML workflows through the lens of package development, enabling test-driven practices and git-level reproducibility within Julia. Discover techniques to minimize hidden states and dependencies commonly found in traditional ML environments. Delve into the integration of MLflow, an open-source model lifecycle management tool, through MLFlowClient.jl, and understand how its graphical user interface enhances model configuration management and provides valuable insights. Gain knowledge on applying these methodologies to create more efficient, transparent, and robust model development processes, facilitating mass experimentation for machine learning workflows in the Julia ecosystem.