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This presentation addresses the critical infrastructure challenges of scaling GenAI by proposing autonomous data products that encapsulate complex pipelines to prevent context rot and ensure governance. By treating data as containerized assets accessible via protocols like MCP, organizations can transition from fragile prototypes to production-ready systems capable of supporting fleets of autonomous agents. The core insight is that operational complexity around data access remains the primary failure mode for AI initiatives, necessitating a fundamental rethink of traditional architecture.
Surse care acoperăInfoQ, Quantum Zeitgeist, SiliconANGLE, The Quantum Insider
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Presentation: Autonomous Data Products for the Autonomous Era: Rethinking Data Architecture for GenAI

InfoQ38 min de cititArticolul și perspectiva sunt în engleză.

Jörg Schad explains how to tame the complex "data management hairball" to build scalable, safe architectures for AI. He shares how autonomous data products act like containers for data, encapsulating pipelines, schemas, and metadata. Discover how progressive tool discovery via protocols like MCP limits context rot, enforces governance policies, and ensures reliable, multi-modal access. By Jörg Schad

Jorg Schad: We're going to be talking about actually two of my favorite topics, and that is data meets AI. We'll be talking a little bit about, what do we need? What do we need to actually make data accessible to GenAI? In my experience, most reasons why GenAI or even before whenever I feel we say GenAI nowadays, the same was true to deep learning, to analytics before. Why most of those projects fail, it's not because of I have a bad model. It's not because I'm choosing the wrong thing. That might happen at times. The most consistent failure mode I've seen is because we underestimate the economy around it or the operations around it. I think the probably most important piece to that is, how do I get access to data? I've seen a lot of prototypes. People prepared it either internally in the company or even external consultants. They come in, have this really cool presentation. Once we really want to turn that into production code and want to connect it to our real data, not just this little cleaned up sample data we had before, this is when actually things break apart. This is, I…

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