Elastic machine learning provides two primary methods for detecting anomalies: temporal and population-based analysis. Temporal anomaly detection focuses on comparing the behavior of an entity with its past behavior over time and is the default mode, but it struggles with high cardinality or sparse data elements. Population anomaly detection, activated through specific configurations, compares individual entities against a collective model of all peers, making it more suitable for high cardinality or sparse data. The text illustrates these concepts using a hypothetical scenario involving document downloads from a company's server. Temporal analysis might fail to flag a new user's unusual download activity due to lack of historical data, whereas population analysis would highlight the same activity as anomalous by comparing it to typical collective behavior. This approach is more memory-efficient and adept at handling sparse data, and users are advised to construct homogenous populations for accurate analysis. The text encourages trying Elastic Stack for machine learning and suggests setting up trials to explore these features.