Isolation Forest Anomaly Detection Model

This Digital Twin provides a  Custom Function  to retrieve  data from Maximo asset Monitor and invoke  an external  Model service running Isolation Forest Python Model. Once the data is analyzed by the service, the score can then be persisted in the Monitor database for predicting asset behaviour and models
The use case here is to detect anomalies within a Maximo Asset Monitor dataset using a scikit-learn model, which is externally hosted in a Watson Machine Learning service. Anomalies are able to be visualized and metric  correlations between anomalies via time-series graphs.
When the data scientist  has trained and tested this model with their asset , they will understand how to:

The intended audience for this Model  are developers / data scientists who would like to analyze their data  Monitor via customized machine learning models that are hosted externally.