  Monitoring the carrier-envelope phase (CEP) is of paramount importance for
experiments involving few cycle intense laser fields. Common measurement
techniques include f-2f interferometry or stereo-ATI setups. These approaches
are adequate, but are challenging to implement on demand, at different
locations as additional metrology tools, in intense few cycle laser-matter
interaction experiments, such as those prevalent in sophisticated user
beamlines. In addition there are inherent difficulties for CEP measured at
non-conventional laser wavelengths (like e.g. mid infrared) and measurements
above 10 kHz laser repetition rates, on single shot basis. Here we demonstrate
both by simulations and by experiments a machine learning (ML) driven method
for CEP estimation in the mid infrared, which is readily generalizable for any
laser wavelength and possibly up to MHz repetition rates. The concept relies on
the observation of the spectrum of high harmonic generation (HHG) in bulk
material and the use of ML techniques to estimate the CEP of the laser. Once
the ML model is trained, the method provides a way for cheap and compact
real-time CEP tagging. This technique can complement the otherwise
sophisticated monitoring of CEP, and is able to capture the complex correlation
between the CEP and the observable HHG spectra.
