Turbulent mixing at centimetre scales is an essential component of the
ocean’s meridional overturning circulation and its associated global
redistribution of heat, carbon, nutrients, pollutants and other tracers.
Whereas direct turbulence observations in the ocean interior are limited
to a modest collection of field programs, basic information such as
temperature, salinity and depth ($T,S,Z$) is available globally. Here,
we show that supervised (deep) machine learning algorithms, informed by
physical understanding, can be trained on the existing turbulence data
to develop skillful predictions of the key properties of turbulence from
$T,S,Z$ and topographic data. This constitutes a promising first step
toward a hybrid physics - artificial intelligence approach to
parameterize turbulent mixing in climate-scale ocean models.