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Anomaly detection based on Machine Learning can be a powerful tool for understanding the behavior of large, complex computer systems in the wild. The set of anomalies seen, however, can change over time: as the system evolves, is put to different uses, and encounters different workloads, both its ‘typical’ behavior and the anomalies that it encounters can change as well. This naturally raises two questions: how effective is automated anomaly detection in this setting, and how much does anomalous behavior change over time? In this paper, we examine these question for a dataset taken from a system that manages the lifecycle of servers in datacenters. We look at logs from one year of operation of a datacenter of about 500 servers. Applying state-of-the art techniques for finding anomalous events, we find that there are a ‘core’ set of anomaly patterns that persist over the entire period studied, but that in to track the evolution of the system, we must re-train the detector periodically. Working with the administrators of this system, we find that, despite these changes in patterns, they still contain actionable insights.