➀ Researchers from the Leibniz Institute for Astrophysics Potsdam (AIP) and the Institute of Cosmos Sciences at the University of Barcelona (ICCUB) have used a novel machine learning model to process observation data from 217 million stars of the Gaia mission efficiently. The results are comparable to conventional methods for determining stellar parameters. The new approach opens up exciting possibilities for mapping properties like interstellar extinction and metallicity across the Milky Way, contributing to understanding the stellar populations and the structure of our galaxy. ➁ The third data release of the Gaia satellite by the European Space Agency ESA provided access to improved measurements for 1.8 billion stars, a vast amount of data for studying the Milky Way. Efficient analysis of such a large dataset, however, presents a challenge. The study published now investigates the use of machine learning to determine important stellar properties based on Gaia's spectrophotometric data. The model was trained on high-quality data from 8 million stars and achieved reliable predictions with low uncertainties. ➂ The machine learning technique, 'Extreme Gradient-Boosted Trees,' enables the determination of precise stellar properties like temperature, chemical composition, and interstellar dust extinction with unprecedented efficiency. The developed machine learning model, SHBoost, completes its tasks, including model training and prediction, within four hours on a single graphics processor - a process that previously required two weeks and 3000 high-performance processors.