  The Sloan Digital Sky Survey has recently initiated its 5th survey generation
(SDSS-V), with a central focus on stellar spectroscopy. In particular, SDSS-V
Milky Way Mapper program will deliver multi-epoch optical and near-infrared
spectra for more than 5 million stars across the entire sky, covering a large
range in stellar mass, surface temperature, evolutionary stage, and age. About
10% of those spectra will be of hot stars of OBAF spectral types, for whose
analysis no established survey pipelines exist. Here we present the spectral
analysis algorithm, Zeta-Payne, developed specifically to obtain stellar labels
from SDSS-V spectra of stars with these spectral types and drawing on machine
learning tools. We provide details of the algorithm training, its test on
artificial spectra, and its validation on two control samples of real stars.
Analysis with Zeta-Payne leads to only modest internal uncertainties in the
near-IR with APOGEE (optical with BOSS): 3-10% (1-2%) for Teff, 5-30% (5-25%)
for v*sin(i), 1.7-6.3 km/s(0.7-2.2 km/s) for RV, $<0.1$ dex ($<0.05$ dex) for
log(g), and 0.4-0.5 dex (0.1 dex) for [M/H] of the star, respectively. We find
a good agreement between atmospheric parameters of OBAF-type stars when
inferred from their high- and low-resolution optical spectra. For most stellar
labels the APOGEE spectra are (far) less informative than the BOSS spectra of
these stars, while log(g), v*sin(i), and [M/H] are in most cases too uncertain
for meaningful astrophysical interpretation. This makes BOSS low-resolution
optical spectra better for stellar labels of OBAF-type stars, unless the latter
are subject to high levels of extinction.
