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 ...

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...

Learn how data governance and quality checks in data pipelines can ensure reliable AI systems and prevent costly errors.

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 ...

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, ...

Back in the 1970s, the ANSI SPARC three-tiered model arose, foreshadowing a smooth intertwining of data and architectural design. The three tiers concept isolated the physical storage needs of data ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

LFM2.5-230M proves that while 3-billion-parameter models like VibeThinker are solving advanced calculus, a 230-million-parameter model is the superior, highly optimized choice for executing structured ...

EY's Alexy Thomas says connected, trustworthy data—not AI models alone—will determine India's long-term AI innovation and ...

Data models are used to represent real-world entities, but they often have limitations. Avoid these common data modeling mistakes to keep data integrity. Data modeling is the process through which we ...