A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
By Jiayan Yin
Towards Data Science has released a video showcase titled "Introducing ShipAI," which highlights real‑world AI work. The post announces this new visual resource and its focus on practical AI applications. It is positioned as a first look into the platform’s capabilities.
By TDS Editors
The article titled "5 AI Skills That Will Keep Data Scientists Relevant in 2027" outlines five specific AI competencies, explaining what each skill addresses and providing runnable code snippets that readers can directly paste into a notebook. It serves as a practical guide for data scientists aiming to stay current with emerging AI technologies.
By Sara Nobrega
The article argues that for small‑to‑medium enterprises, the most disruptive yet essential step toward data maturity is to rebuild or strengthen a solid knowledge foundation layer. It stresses that this initiative must be evidence‑backed and minimally disruptive to current processes, and it proposes a low‑impact data strategy that adapts to evolving data flows. The authors emphasize that knowledge graph techniques will become indispensable in AI‑powered enterprises if designed modularly, dynamically, and cross‑functionally.
By Valentina Carapella, Ernesto Jimenez-Ruiz
The article "How to Design Architectural Guardrails Around AI Agents" discusses essential agent design patterns that data engineers should understand. It emphasizes the importance of establishing clear architectural boundaries to ensure AI agents operate safely and effectively within larger systems. The post was originally published on Towards Data Science.
By Thuwarakesh Murallie
Giving an AI agent access to a data warehouse doesn't automatically make it agent-ready. The real challenge lies in teaching the agent what the data means and when it's reliable enough to use.
By Shafeeq Ur Rahaman