  The Gaia Data Release 3 (DR3), published in June 2022, delivers a diverse set
of astrometric, photometric, and spectroscopic measurements for more than a
billion stars. The wealth and complexity of the data makes traditional
approaches for estimating stellar parameters for the full Gaia dataset almost
prohibitive. We have explored different supervised learning methods for
extracting basic stellar parameters as well as distances and line-of-sight
extinctions, given spectro-photo-astrometric data (including also the new Gaia
XP spectra). For training we use an enhanced high-quality dataset compiled from
Gaia DR3 and ground-based spectroscopic survey data covering the whole sky and
all Galactic components. We show that even with a simple neural-network
architecture or tree-based algorithm (and in the absence of Gaia XP spectra),
we succeed in predicting competitive results (compared to Bayesian isochrone
fitting) down to faint magnitudes. We will present a new Gaia DR3
stellar-parameter catalogue obtained using the currently best-performing
machine-learning algorithm for tabular data, XGBoost, in the near future.
