<jats:p>We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name <jats:italic>SalaciaML</jats:italic> according to <jats:italic>Salacia</jats:italic> the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, <jats:italic>bad</jats:italic> data flagged as <jats:italic>good</jats:italic> and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm <jats:italic>SalaciaML</jats:italic> is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. <jats:italic>SalaciaML</jats:italic> has been able to detect correctly more than 90% of all <jats:italic>good</jats:italic> and/or <jats:italic>bad</jats:italic> data in 11 out of 16 Mediterranean regions.</jats:p>