Polars is mature, production ready, intuitive to write and pleasant to read. And it is fast. Thanks to Rust and Rayon, you can achieve speeds greater than numba's. If you combine it with top-of-the-class evaluation methods, not only can you get speedups of about 1-2x order of magnitude in feature engineering and cross-validation, but also dramatically improve your development workflow. That's what we set out to demonstrate with functime. We chose to write a time-series library first, because forecasting can be a costly undertaking, with significant problems of scale. Making predictions with big panel datasets usually required fitting thousands of univariate models, one at a time, using distributed systems. On the other hand, functime unlocks an efficient forecasting workflow, from your laptop. This talk is a hands-on demonstration for forecasting practitioners and data scientists alike. It will showcase how to build clean and performant forecasting pipelines with rich feature-engineering capabilities, enabling a seamless and more efficient modelling workflow. Nevertheless, the principles behind functime can be grasped by every machine learning practitioner: forecasting is just a use-case to show off Polars' potential. With Polars, we can improve the current state of machine learning modelling and raise the ceiling for what reasonable scale means.