  The next generation of spectroscopic surveys is expected to provide spectra
for hundreds of thousands of white dwarf (WD) candidates in the upcoming years.
Currently, spectroscopic classification of white dwarfs is mostly done by
visual inspection, requiring substantial amounts of expert attention. We
propose a data-driven pipeline for fast, automatic selection and spectroscopic
classification of WD candidates, trained using spectroscopically confirmed
objects with available Gaia astrometry, photometry, and Sloan Digital Sky
Survey (SDSS) spectra with signal-to-noise ratios $\geq9$. The pipeline selects
WD candidates with improved accuracy and completeness over existing algorithms,
classifies their primary spectroscopic type with $\gtrsim 90\%$ accuracy, and
spectroscopically detects main sequence companions with similar performance. We
apply our pipeline to the Gaia Data Release 3 cross-matched with the SDSS Data
Release 17 (DR17), identifying 424 096 high-confidence WD candidates and
providing the first catalogue of automated and quantifiable classification for
36 523 WD spectra. Both the catalogue and pipeline are made available online.
Such a tool will prove particularly useful for the undergoing SDSS-V survey,
allowing for rapid classification of thousands of spectra at every data
release.
