Machine Learning
Leverage streaming online predictions to drive insights to action
Last updated
Leverage streaming online predictions to drive insights to action
Last updated
For cutting-edge stream-based use cases, the incorporation of machine learning models is integral for next best action processing. Out-of-the-box, Joule offers the capability to seamlessly deploy JPMML models within a processing pipeline using a predictive processor.
Joule provides the capability to perform near-real-time predictions to enable best next action use cases
Online customer segmentation
Predictive service maintenance
Behaviour anomaly detection
Best next action
Customer conversation scoring
The Joule architecture seamlessly integrates core features such as feature engineering, prediction, model auditing, and model management. Below, we showcase a predictive use case, leveraging the robust features that Joule brings to the forefront.
Online predictions
The prediction processor evaluates event feature vectors in near-real-time
Feature engineering
Decorate a feature vector with enriched features specific to the deployed model
Audit
Monitor and track model performance for prediction explainability and model retraining
Model Management
Deployed models are activity managed using an in-place replacement algorithm
