  Predictive and real-time inference capability for the upstream separatrix
electron density, $n_\text{e, sep}$, is essential for design and control of
core-edge integrated plasma scenarios. In this study, both supervised and
semi-supervised machine learning algorithms are explored to establish direct
mapping as well as indirect compressed representation of the pedestal profiles
for predictions and inference of $n_{\text{e, sep}}$. Based on the EUROfusion
pedestal database for JET, a tabular dataset was created, consisting of machine
parameters, fraction of ELM cycle, high resolution Thomson scattering profiles
of electron density and temperature, and $n_{\text{e, sep}}$ for 608 JET shots.
Using the tabular dataset, the direct mapping approach provides a mapping of
machine parameters and ELM percentage to $n_{\text{e, sep}}$. Through
representation learning, a compressed representation of the experimental
pedestal electron density and temperature profiles is established. By
conditioning the representation with machine control parameters, a
probabilistic generative predictive model is established. For prediction, the
machine parameters can be used to establish a conditional distribution of the
compressed pedestal profiles, and the decoder that is trained as part of the
algorithm can be used to decode the compressed representation back to full
pedestal profiles. Although, in this work, a proof-of-principle for predicting
and inferring $n_{\text{e, sep}}$ is given, such a representation learning can
be used also for many other applications as the full pedestal profile is
predicted. An implementation of this work can be found at
https://github.com/fusionby2030/moxie.
