Discover how to transform experimental data science code into production-ready ML pipelines in this 26-minute PyCon US talk by Yetunde Dada. Learn about Kedro, an open-source Python framework designed to create reproducible, maintainable, and modular data science code. Explore a workflow that deconstructs the experimentation process typically done in Jupyter notebooks, addressing the growing demand for production-level code in data science. Gain insights into balancing the need for experimentation with the requirements of creating robust, maintainable code that can adapt to changing business objectives. Access additional resources including Kedro's documentation, GitHub repository, and presentation slides to further enhance your understanding of this powerful framework.