Featured Posts: AI

A Genetic Algorithm with Trusty PMML

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 →

Predictions in Kogito: PMML endpoints with OpenAPI

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 →

Event-driven decisions with Kogito

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 →

How to integrate your Kogito application with TrustyAI – Part 3

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 to integrate your Kogito application with TrustyAI – Part 1

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 →

TrustyAI meets Kogito: the decision tracing addon

New to Kogito? Check out our “get started” page and get up to speed! 😉 This post presents the decision tracing addon: a component of the Kogito runtime quite relevant for the TrustyAI initiative (introduced here and here). One of the key goals of TrustyAI is to enable advanced auditing capabilities, which, as written inRead more →

An introduction to TrustyAI Explainability capabilities

In this blog post you’ll learn about the TrustyAI explainability library and how to use it in order to provide explanations of “predictions” generated by decision services and plain machine learning models. The need for explainability Nowadays AI based systems and decision services are widely used in industry in a wide range of domains, likeRead more →

Finding counterfactuals with OptaPlanner

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 →

TrustyAI meets Kogito: decision monitoring

In this article, we introduce the metrics monitoring add-on for Kogito. This add-on is part of the TrustyAI initiative already introduced in the previous article https://blog.kie.org/2020/06/trusty-ai-introduction.html . Like Quarkus extensions, the Kogito add-ons are modules that can be imported as dependencies and add capabilities to the application. For example, another add-on is the infinispan-persistence-addon thatRead more →

TrustyAI Aspects

As mentioned in the previous blog post we have three aspects that we are implementing: explainability, runtime and accountability. However we need to see how these are connected with the use cases and personas. The first aspect we will consider is runtime tracing and monitoring. The term monitoring refers to the system overseeing performance orRead more →