With the advent of deep and wide multi-band photometric surveys, like DES, there has been a resurgence of interest in photometric redshifts as a means of estimating the distance to a range of astrophysical objects. As consequence, the use of photometric redshifts in cosmology probes such correlation functions is increasing. Often, however these photo-zs are treated like spectroscopic observation, using one value estimate rather the full probability mass function (PMF). In this talk, I will focus the discussion mainly on the new methods and approaches we have been developing to obtain robust and accurate redshift probability mass function by efficiently combining Bayesian template fitting techniques with the powerful tools of active machine learning. I will also discuss how spatial information can be incorporated to this estimator, and how the full information encoded in a redshift PMF can be used to carry out spatial distribution of galaxies analysis as probe of current cosmology models.