This end-to-end ML pipeline detects anomalies by ingesting real-time, streaming data from various network edge field devices, performing transformation jobs to continuously run daily predictions/inferences, and retraining the ML models based on the incoming newer time series data on a daily basis. Note that Random Cut Forest (RCF) is one of the machine learning algorithms for detecting anomalous data points within a data set and is designed to work with arbitrary-dimensional input.