  The current role of data-driven science is constantly increasing its
importance within Astrophysics, due to the huge amount of multi-wavelength data
collected every day, characterized by complex and high-volume information
requiring efficient and as much as possible automated exploration tools.
Furthermore, to accomplish main and legacy science objectives of future or
incoming large and deep survey projects, such as JWST, LSST and Euclid, a
crucial role is played by an accurate estimation of photometric redshifts,
whose knowledge would permit the detection and analysis of extended and
peculiar sources by disentangling low-z from high-z sources and would
contribute to solve the modern cosmological discrepancies. The recent
photometric redshift data challenges, organized within several survey projects,
like LSST and Euclid, pushed the exploitation of multi-wavelength and
multi-dimensional data observed or ad hoc simulated to improve and optimize the
photometric redshifts prediction and statistical characterization based on both
SED template fitting and machine learning methodologies. But they also provided
a new impetus in the investigation on hybrid and deep learning techniques,
aimed at conjugating the positive peculiarities of different methodologies,
thus optimizing the estimation accuracy and maximizing the photometric range
coverage, particularly important in the high-z regime, where the spectroscopic
ground truth is poorly available. In such a context we summarize what learned
and proposed in more than a decade of research.
