Last 6 months

Event-driven predictions with Kogito

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This blogpost introduces the event-driven predictions addon and is the third of my series of event-driven with Kogito posts, after the event-driven decisions and event-driven rules addons. It is available since Kogito v1.12.0 and its behavior resembles what the previous two addons already do for decisions and rules. Key concepts The new addon enables theRead more →

TrustyAI SHAP: Overview and Examples

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SHAP is soon to be the newest addition to the TrustyAI explanation suite. To properly introduce it, let’s briefly explore what SHAP is, why it’s useful, and go over some tips about how to get the best performance out it. A Brief Overview Shapley Values The core idea of a SHAP explanation is that ofRead more →

Counterfactuals; getting the right answer

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Sometimes the result of an automated decision may be neither desired or that which was required. What if there was a tool to find a way to overturn those decisions, maybe changing some of the figures that were provided to the system, and achieve a different outcome? That’s what we’ve been working on lately withinRead more →

Last Year

Local to global – Using LIME for feature importance

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In a previous blog post we have discussed how to leverage LIME to get more insights about specific predictions generated by a black box decision service. In fact LIME is mostly used to find out which input features where most important for the generation of a particular output, according to that decision service. Such explanationsRead more →

Introducing process operational monitoring for Kogito

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Monitoring is a well known concept in Kogito: the support for decisions was available since Kogito 0.11 through the Prometheus monitoring add-on. Today we announce that, starting from Kogito 1.11.0, this addon is enhanced to enable monitoring of processes. Unlike decisions, however, the feature is currently limited to operational metrics. The domain metrics section isRead more →

Shopping recommendations in PMML – Kogito at Work

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In a previous post (Shopping recommendations in PMML) we built the main infrastructure for making personalized shopping recommendations depending on the customer shopping history. The companion project was meant to demonstrate a standalone implementation of the Trusty-PMML engine. In this post, we will show how to invoke the engine on a remote Kogito instance. KogitoRead more →

Using TrustyAI’s explainability from Python

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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 →

Shopping recommendations in PMML.

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In previous posts (PMML revisited and Predictions in Kogito) we had a glance at how a PMML engine has been implemented inside Drools/Kogito ecosystem.This time we will start looking at a concrete example of a recommendation engine based on top of PMML.The first part of this post will deal with the ML aspect of it,Read more →