Data Quality Monitoring (DQM) is an important aspect of every high-energy physics experiment needed to avoid taking low-quality data. The goal of DQM is to track important information about the detector and the data and catch problems in realtime. This monitoring happens both online and offline to ensure optimal operation of the experiment. The goal of the ML4DQML project is to use machine learning to aid human shifters with identification of anomalies to help make better decisions about the quality of the data.