Even as large language models have been making a splash with ChatGPT and its competitors, another incoming AI wave has been quietly emerging: large database models. Even as large language models have ...

An enterprise conceptual data model is often seen as a high mountain to be climbed, a journey that will last a lifetime. People have visions of 10 feet or more of wall in the corporate offices ...

Data platforms make decisions all day. An SQL query keeps the rows over a threshold, matches a keyword, and joins two tables on a shared ID. A pipeline sends a failed job down a retry path. Rules like ...

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an open community data standard, designed to standardize the structure and content of observational data and to enable ...

Large language model pretraining optimizes a neural network on vast token sequences so it learns reusable linguistic, factual ...

Database provider Couchbase has unveiled a comprehensive suite of model hosting and data processing capabilities for building, deploying and governing agentic AI applications. By bringing data and ...

Occasionally one may hear that a data model is “over-normalized,” but just what does that mean? Normalization is intended to analyze the functional dependencies across a set of data. The goal is to ...

Developing reliable machine learning models for health care can be challenging when only small datasets are available. This challenge motivates research into whether quantum computing approaches can ...