

Data for AI: One AI, Every Data Model
Last time, we made the case for unifying data so agents can actually use it. This session goes a level deeper, into the structures themselves.
A vector index, an Iceberg table, an object store full of audio and video, a JSON document, a knowledge graph, a stream of events. Each one answers a different question well and fumbles the rest. Force every modality through a single structure, and you pay for it in latency, storage cost, or meaning you can't get back. Choose well, and the same agent gets faster and cheaper.
We'll walk through the various data structures behind multi-modal AI. What each is good at, where each one breaks down, and how to combine them so you don't end up with six disconnected stores nobody can govern.
Built for data engineers, AI/ML engineers, and platform architects building for agents and multi-modal workloads.
Want to speak?
We're still building the lineup, and your war story is exactly what makes this event worth showing up for. If you've forced a modality through the wrong structure and paid for it, or landed on a combination that actually works, that's the talk. Lightning talks and full sessions both welcome!
Submit by July 15: https://forms.gle/6Hf9JrYedJvmDtjZ6
Required for entry
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About Data for AI
Data for AI is a community of founders, engineers, executives, and innovators building the data infrastructure behind generative AI, multi-modal models, and whatever comes next. We get together in person to swap hard-won lessons, meet the people solving the same problems, and hang out. Come join us.
Sponsors
This event is made possible by our sponsors, who keep the Data for AI community running.
Datastrato is the company behind Apache Gravitino, the open-source metadata lake that puts tables, models, and files under a single catalog, lineage graph, and access layer. They are building the open data fabric platform to accelerate trusted AI., and host the Data for AI community. More at datastrato.ai.
Neo4j is the graph database behind many of the world's knowledge graphs. Its native property-graph engine and Cypher query language make relationships a first-class citizen, which is why teams reach for it on connected-data problems and, increasingly, GraphRAG for grounding LLMs. More at neo4j.com.
Want to reach the Data for AI community with your brand? E-mail [email protected]