Towards Data Science
Jul 18
Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture
What data teams need to build with AI to make self-healing data architecture a practical reality The post 7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture appeared first on Towards Data Science .
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Towards Data Science
Sep 8
Introducing ShipAI
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.
Towards Data Science
Sep 1
5 AI Skills That Will Keep Data Scientists Relevant in 2027
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.
arXiv AI
Sep 1
Reviving our data foundations is the most disruptive step to data maturity
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.
Towards Data Science
4d ago
How to Design Architectural Guardrails Around AI Agents
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.
Towards Data Science
Aug 10
Building an Agent-Ready Data Warehouse: What Traditional Architectures Do Wrong
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.
Towards Data Science
Jul 10
The Big Con of Agentic AI
What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .
arXiv AI
Jun 9
Can Data Work be Reparative?
arXiv:2606. 09408v1 Announce Type: cross Abstract: We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and benchmarking online safety systems.
Towards Data Science
Aug 14
A Day in the Life of a Data Scientist in 2026
How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science .
Towards Data Science
Aug 18
From Prototype to Production: The Architecture Behind Secure & Governed AI Agents
The article discusses how to transition AI agents from prototype to production by implementing responsible AI practices, security measures, and governance frameworks suitable for enterprise use. It outlines the necessary architectural layers that ensure these agents operate safely and comply with organizational policies. The focus is on building robust, secure, and governed AI systems that can be reliably deployed in business environments.
OpenAI Blog
Nov 9, 2023
OpenAI Data Partnerships
Working together to create open-source and private datasets for AI training.
arXiv AI
Aug 5
On the missing data layer and a potential solution
arXiv:2608. 02949v1 Announce Type: new Abstract: Latin America is missing two foundational layers of AI infrastructure: the dataset layer and the benchmark layer.