Building a mature machine learning (ML) development process involves more than creating effective models; it requires a workflow that supports continuous iteration and improvement, which can be challenging due to data scientists' limited focus on software engineering principles. Albin Sundqvist emphasizes the importance of overcoming poor code quality and manual workflows by establishing a standard repository structure, designing idempotent scripts and pipelines, and treating pipelines as artifacts. A mature ML process enables confident and rapid deployment by integrating software engineering best practices, such as shift-left testing, loosely coupled code, and workflow automation. Sundqvist also highlights the need for a mindset shift, focusing on deploying entire pipelines rather than individual models, and designing idempotent workflows to ensure consistency and reliability. While transitioning to a mature ML process can be complex, starting with small, pragmatic steps and iterating over time can help teams achieve scalable and resilient systems, eventually transforming team culture and enhancing productivity.