Abstract
News editors obtained the following quote from the background information suppli ed by the inventors: “Recently, organizations have been leveraging artificial in telligence (AI), machine learning (ML), and optimization (OPT) models to improve organizational decision-making. Many model-building tools allow for building, t raining, and tuning models, as well as utilize out-of-the-box models to fit agai nst specific datasets. However, the journey from development sandbox to impactfu l operational tool for users remains precarious. One-off AI/ML/OPT models may be developed to address specific business needs via a pointsolution or made avail able on an API endpoint, but many organizations lack the overarching framework t o ensure that data, models, and decisions can be captured and deployed across us e cases-resulting in fragmentation and limited learning. Another problem is to c apture and integrate the expert know-how that exists in the heads of experts in the field. The present disclosure shows technical approaches to address these pr oblems. Furthermore, the approaches disclosed therein allow machine learning mod els to continuously learn and improve.”
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