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Title & Speakers	Event
Event AI-Powered Data & Search: Unlocking Intelligence Across Systems 2025-07-02
Decoding Search: Understanding Keyword, Semantic, Vector and Hybrid Approach 2025-07-02 · 20:10 Gregor Bauer – Manager Solutions Engineering (Central Europe) @ Couchbase Learning the different search techniques is essential for developers aiming to implement effective search functionality. In this talk we’ll break down keyword, semantic, vector and hybrid search approaches. We will explore how each method works, their advantages and disadvantages, and practical use cases. This talk is for developers created by a developer and will break down what can be overly complex concepts into practical takeaways for our everyday work. By the end of the session, you’ll have a better understanding of when and how to use each search technique to optimize your user experience. keyword search semantic search vector search hybrid search
How to Decipher User Uncertainty with GenAI and Vector Search 2025-07-02 · 19:10 Alexander Krasilnikov – Solutions Engineer @ Couchbase Unlock the power of generative AI and vector search to transform vague queries into precise results. Discover practical Python examples and see how advanced search revolutionizes user interaction and business outcomes. genai vector search Python
Foundation Models for In-Context Learning on Relational Data 2025-07-02 · 18:35 Matthias Fey – Founding Engineer @ Kumo.AI , Vid Kocijan – Applied ML Engineer @ Kumo.AI This talk explores how foundation models, originally developed for unstructured data such as text and images, are now enabling in-context learning on structured relational data. We will examine how recent developments allow these models to generalize across diverse tabular prediction tasks without retraining, by leveraging schema-aware representations and attention mechanisms over multi-table structures. The session will highlight emerging research directions at the intersection of deep learning, graph-based transformer architectures, and multi-modal relational datasets. Throughout the presentation, we will learn how these recent innovations allow an expert practitioner to reduce the time to prediction from months to seconds by introducing predictive models that operate directly on the raw database. foundation models in-context learning relational data multi-table data graph-based transformer pytorch geometric

Decoding Search: Understanding Keyword, Semantic, Vector and Hybrid Approach 2025-07-02 · 20:10

Gregor Bauer – Manager Solutions Engineering (Central Europe) @ Couchbase

Learning the different search techniques is essential for developers aiming to implement effective search functionality. In this talk we’ll break down keyword, semantic, vector and hybrid search approaches. We will explore how each method works, their advantages and disadvantages, and practical use cases. This talk is for developers created by a developer and will break down what can be overly complex concepts into practical takeaways for our everyday work. By the end of the session, you’ll have a better understanding of when and how to use each search technique to optimize your user experience.

keyword search semantic search vector search hybrid search

How to Decipher User Uncertainty with GenAI and Vector Search 2025-07-02 · 19:10

Alexander Krasilnikov – Solutions Engineer @ Couchbase

Unlock the power of generative AI and vector search to transform vague queries into precise results. Discover practical Python examples and see how advanced search revolutionizes user interaction and business outcomes.

genai vector search Python

Foundation Models for In-Context Learning on Relational Data 2025-07-02 · 18:35

Matthias Fey – Founding Engineer @ Kumo.AI , Vid Kocijan – Applied ML Engineer @ Kumo.AI

This talk explores how foundation models, originally developed for unstructured data such as text and images, are now enabling in-context learning on structured relational data. We will examine how recent developments allow these models to generalize across diverse tabular prediction tasks without retraining, by leveraging schema-aware representations and attention mechanisms over multi-table structures. The session will highlight emerging research directions at the intersection of deep learning, graph-based transformer architectures, and multi-modal relational datasets. Throughout the presentation, we will learn how these recent innovations allow an expert practitioner to reduce the time to prediction from months to seconds by introducing predictive models that operate directly on the raw database.

foundation models in-context learning relational data multi-table data graph-based transformer pytorch geometric

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