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 →
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 →
Tuning algorithms, especially when machine learning is involved is often a tricky business. In this post we present an optimization based technique to automatically tune LIME in order to obtain more stable explanations. LIME (aka Local Interpretable Model agnostic explanations) is one of the most commonly used algorithms for generating explanations for AI based models.Read more →
Hi Kogito folks, In the previous blogposts we demonstrated how to deploy a Kogito service together with the TrustyAI infrastructure on an OpenShift cluster https://blog.kie.org/2020/12/how-to-integrate-your-kogito-application-with-trustyai-part-1.html.If you are new to TrustyAI, we suggest you read this introduction: https://blog.kie.org/2020/06/trusty-ai-introduction.html In this blogpost, we’d like to demonstrate how to get started with TrustyAI in ~15 minutes. In orderRead more →
A quick guide on how to leverage partial dependence plots to visualize whether an ML model is fair with respect to different groups of people. As machine learning models, and decision services in general, are used more and more as aiding tools in making decisions that impact human lives, a common concern that is oftenRead more →
Recently, I’ve stumbled upon this interesting article and paired project about a Genetic Algorithm. Then, I’ve asked myself if somehow the features of Trusty PMML could be meaningfully used inside such context. I won’t go deep into technical details, but basically, the Genetic Algorithm classifies features as "genes", a set of genes is a "genoma",Read more →
Introduction PMML is an XML standard whose scope is to define different kinds of predictive models (Regression, Scorecard, Tree, Neural Network, etc) in a system-agnostic way, so that it may be used and shared by different systems/implementations. The OpenAPI Specification (OAS) defines a standard, language-agnostic interface to RESTful APIs which allows both humans and computersRead more →
In 2021 it’s almost undeniable that modern application development needs to target the cloud, given the requirements of flexibility, scalability and availability imposed by today’s world. Event-driven architectures have proven to be well suited models for this purpose. As a result, we’re adopting these principles in several components of Kogito, which aims to be theRead more →
In the second part of the blog series https://blog.kie.org/2020/12/how-to-integrate-your-kogito-application-with-trustyai-part-2.html we showed how to setup the OpenShift cluster that will host the TrustyAI infrastructure and the Kogito application we created in the first part https://blog.kie.org/2020/12/how-to-integrate-your-kogito-application-with-trustyai-part-1.html . In this third and last part of our journey, we are going to demonstrate how to deploy the TrustyAI infrastructureRead more →
How can you audit a decision out of your new Kogito application? It’s pretty simple: in this series of articles, we are going to demonstrate how to create a new Kogito application and how to deploy the TrustyAI infrastructure on an OpenShift cluster.If you are new to TrustyAI, we suggest you read this introduction: https://blog.kie.org/2020/06/trusty-ai-introduction.htmlWithRead more →