In this blog post I want to share the results of a technical exploration in bridging, bringing together and integrating a diverse set of technologies and platforms, ranging from classic spreadsheet applications (Excel) to serverless platforms (Knative on OpenShift) to technical rules executed by our rule engine Drools! Introduction This content has been inspired byRead more →
We put Drools inside Kubernetes’ control plane …you wouldn’t believe what happened next! 😉 This is a longer post on the current status of explorations with RuleOps; you may want to skim or skip to different sections, depending on your interests. RuleOps in a nutshell Read time: ~ 1 minuteKubernetes is an orchestration platform, veryRead more →
Following up from a previous blog post about integrating Drools with the Open Prediction Service, in this new post we want to share the current results from another exploration work: this time integrating Drools with research on Transparent Machine Learning by IBM. Introduction Transparency is a key requirement in many business sectors, from FSI (FinancialRead more →
In this blog post we’re going to explore an integration between the Drools DMN Engine and another open source project from IBM: "Open Prediction Service" (OPS). Introduction Integrating symbolic AIs (rule engines, KRR, etc) with Machine Learning predictive models is an effective strategy to achieve pragmatical, and often more eXplainable, AI solutions. We have alsoRead more →
