Building Systems That Survive Real Life

AI Engineer Sara Nobrega discusses the transition from data science, highlighting the mindset shift from optimizing models to building dependable, real-world systems. She advocates for balancing speed and optimization by shipping small, reliable product versions and focusing on a solid baseline. LLMs can be used as a practical bridge between data science and DevOps by helping to generate tests, documentation, and error messages. For junior data scientists looking to stay competitive, the most crucial engineering skill is learning to deploy work in a repeatable way that others can use.