Abstract: Systems and methods for monitoring data center metrics using machine learning are disclosed. A system can obtain a first set of sensor readings from data center components during a first time period. The system can generate a dataset using the first set of sensor readings and a negative sampling function. The dataset can include labels generated according to a learned distribution derived from the first set of sensor readings. The system can update a machine learning model using the dataset to predict likelihoods of future anomalies in the plurality of data center components. The system can obtain a second set of sensor readings from the data center components during a second time period. The system can generate, using the machine learning model and the second set of sensor readings, a prediction of a future anomaly that may occur in at least one of the data center components.