AI

How do you operationalize AI for the next 3+ years - across multiple tools, providers, and capabilities - not just the next deployment?

One that exposes a well-modeled, cohesive schema or ontology in a machine-readable, easy-to-consume format. This article explains why, and outlines the other capabilities of your knowledge graph you need to expose.

Using Data Graphs GraphRAG AI to analyze and explore a knowledge graph of wines

Patterns in GraphRAG and delving into the capabilities of Data Graphs AI and its architecture.

Large Language Models are not displacing advanced specialist models (yet) .

BOTT is a data ethics framework for the practical assessment of AI and predictive analytics models within their ecosystems.

It is paramount we have proper discussions around AI and its social impact. Shutting down the argument and dismissing it as snake oil is not the best way forward.

Tagmatic goes head to head with a number of standard multi-label classification implementations. The result: don't build, buy.

A discussion of AI and Data Science delivery anti-patterns, their impacts on data platform technical architectures, and strategies for reducing complexity and mitigation.

For Data Science to truly live up to expectations, it must embrace software craftsmanship.