As agentic and RAG systems move into production, retrieval quality is emerging as a quiet failure point — one that can ...

Tabular foundation models are the next major unlock for AI adoption, especially in industries sitting on massive databases of ...

For financial institutions, threat modeling must shift away from diagrams focused purely on code to a life cycle view ...

Learn how this new standard connects AI to your data, enhances Web3 decision-making, and enables modular AI systems.

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...

Training artificial intelligence models is costly. Researchers estimate that training costs for the largest frontier models ...

At its heart, data modeling is about understanding how data flows through a system. Just as a map can help us understand a city’s layout, data modeling can help us understand the complexities of a ...

Count data modelling comprises a suite of statistical techniques dedicated to analysing non-negative integer-valued observations. Such data often arise in a variety of contexts including epidemiology, ...

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 ...

Just as machine learning, artificial intelligence, data modeling and analytics platforms have transformed manufacturing, drug discovery, health care and operations in a host of other industries, these ...

Social media posts about unemployment can predict official jobless claims up to two weeks before government data is released, according to a study. Unemployment can be tough, and people often post ...