The overwhelming contributor to energy consumption in AI processors is not arithmetic; it’s the movement of data.

This article provides a retrospective on one such case: the TRIPS project at the University of Texas at Austin. This project started with early funding by the National Science Foundation (NSF) of ...

The data engineering trends clearly show a move toward maturity. The emphasis is on building reliable, repeatable, and ...

Their exits may strain Microsoft’s push to expand AI capacity as energy constraints and infrastructure bottlenecks ...

Learn how to build production-ready AI agents using Azure Functions, Microsoft Foundry, and the MCP in this deep-dive with ...

For enterprises, proprietary data is a source of competitive advantage. Take these four steps to ready it for AI-powered ...

The shift is inevitable. The question is no longer whether AI can manage infrastructure, but whether we can trust it to do so.

For the last few years, most of the progress in AI has been tied to size. Bigger models, bigger datasets, bigger everything.

TCS is looking to help businesses more easily take advantage of agentic AI to do more in Azure AI environments.