From Jupyter Notebook to production: How to ship AI systems that actually work

Moving from experimentation to production in AI requires a shift in mindset, architecture, and engineering discipline. This involves transforming Jupyter Notebooks into deployable systems. No API wrappers are mentioned as a solution. AI systems need to be production-ready for real-world applications. Engineers must adapt their approach to ensure successful deployment.