AbstractMassive spectroscopic surveys targeting tens of millions of galaxies are starting to dominate the observational landscape in the 2020 decade. For instance, a night of observation with the Dark Energy Spectroscopic Instrument (DESI) can measure around of 100,000 spectra, with each spectrum sampled over 2,000 wavelength points. Assessing the quality of such a massive data flow requires new approaches to complement visual inspection by humans. In this work, we explore the Uniform Manifold Approximation and Projection (UMAP) as a technique to assess the data quality of DESI. We use UMAP to project DESI data into a 2-dimensional space. In this space, we are able to find outliers that correspond to instrument fluctuations that can be fully diagnosed by inspecting the raw data. These results pave the way for to use machine learning to monitor the health of massive spectroscopic surveys automatically.