Shipping AI systems that actually work

The author outlines a practical pipeline for moving AI systems from Jupyter notebooks to production. Key steps include treating models as software artifacts with version control, adding automated testing for data drift and model performance, and deploying with containerized microservices. The approach emphasizes reproducibility and monitoring to catch failures before they affect users.