  Extracting information from raw data is probably one of the central
activities of experimental scientific enterprises. This work is about a
pipeline in which a specific model is trained to provide a compact, essential
representation of the training data, useful as a starting point for
visualization and analyses aimed at detecting patterns, regularities among
data. To enable researchers exploiting this approach, a cloud-based system is
being developed and tested in the NEANIAS project as one of the ML-tools of a
thematic service to be offered to the EOSC. Here, we describe the architecture
of the system and introduce two example use cases in the astronomical context.
