Use Case Documentation
Event Anomaly Detection

anomaly detection (also known as outlier detection) is the identification of rare events or observations that might be of interest as they differ significantly from the majority of the data.

Dataset and Feature Group Requirements

This section specifies the Datasets / Feature Groups requirements to successfully train a Event Anomaly Detection model. Feature requirements include recommendations on additional datasets that might enhance model performance.

Training Models - Training Options and Metrics

This section describes all the available model training options that can be used to create a Event Anomaly Detection model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.

Prediction API

This section discusses the prediction API method so that you could properly generate predictions from the model you deployed.