The TrustyAI‘s explainability library is primarily aimed at the Java Virtual Machine (JVM) and designed to be integrated seamlessly with the remaining TrustyAI services, adding explainability capabilities (such as feature importance and counterfactual explanations) to business automation workflows that integrate predictive models. Many of these capabilities are useful on their own. However, in the dataRead more →
Modern enterprises are increasingly integrated with Machine Learning (ML) algorithms in their business workflows as a means of leveraging existing data, optimising decision making processes, detecting anomalies or simply reducing repetitive tasks. One of the challenges with ML methods, especially with internally complex predictive models, is being able to provide non-technical explanations on why aRead more →
In this post, we’ll introduce a new jBPM API which allows for predictive models to be trained with Human Tasks (HT) data and for HT to incorporate model predictions as outputs and complete HT without user interaction. This API will allow you to add Machine Learning capabilities to your jBPM project by being able toRead more →
