  The Pan-STARRS1 (PS1) $3\pi$ survey is a comprehensive optical imaging survey
of three quarters of the sky in the $grizy$ broad-band photometric filters. We
present the methodology used in assembling the source classification and
photometric redshift (photo-z) catalogue for PS1 $3\pi$ Data Release 1, titled
Pan-STARRS1 Source Types and Redshifts with Machine learning (PS1-STRM).
  For both main data products, we use neural network architectures, trained on
a compilation of public spectroscopic measurements that has been cross-matched
with PS1 sources.
  We quantify the parameter space coverage of our training data set, and flag
extrapolation using self-organizing maps. We perform a Monte-Carlo sampling of
the photometry to estimate photo-z uncertainty.
  The final catalogue contains $2,902,054,648$ objects. On our validation data
set, for non-extrapolated sources, we achieve an overall classification
accuracy of $98.1\%$ for galaxies, $97.8\%$ for stars, and $96.6\%$ for
quasars.
  Regarding the galaxy photo-z estimation, we attain an overall bias of
$\left<\Delta z_{\mathrm{norm}}\right>=0.0005$, a standard deviation of
$\sigma(\Delta z_{\mathrm{norm}})=0.0322$, a median absolute deviation of
$\mathrm{MAD}(\Delta z_{\mathrm{norm}})=0.0161$, and an outlier fraction of
$O=1.89\%$.
  The catalogue will be made available as a high-level science product via the
Mikulski Archive for Space Telescopes at
https://doi.org/10.17909//t9-rnk7-gr88.
