# Announcing the Vector Space Day 2025 Speaker Lineup

DevFeed: [Announcing the Vector Space Day 2025 Speaker Lineup](<https://devfeed.tech/articles/announcing-the-vector-space-day-2025-speaker-lineup-46751.md>)

Original publisher: [Read original article](<https://qdrant.tech/blog/vector-space-day-lineup-2025/>)

Author: info@qdrant.tech (Andrey Vasnetsov)

Published: 2025-09-15T00:00:00Z

Content type: news

Language: en

Sources: [Qdrant Blog on Qdrant - Vector Search Engine](<https://devfeed.tech/sources/qdrant-blog-on-qdrant-vector-search-engine.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [Neo4j](<https://devfeed.tech/topics/neo4j.md>), [n8n](<https://devfeed.tech/topics/n8n.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [approximate-nearest-neighbor-search](<https://devfeed.tech/tags/approximate-nearest-neighbor-search.md>), [baseten](<https://devfeed.tech/tags/baseten.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [bert](<https://devfeed.tech/tags/bert.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [fasttext](<https://devfeed.tech/tags/fasttext.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [hnsw](<https://devfeed.tech/tags/hnsw.md>), [image-search](<https://devfeed.tech/tags/image-search.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [knn-algorithm](<https://devfeed.tech/tags/knn-algorithm.md>), [latency](<https://devfeed.tech/tags/latency.md>), [matching](<https://devfeed.tech/tags/matching.md>), [memory](<https://devfeed.tech/tags/memory.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [qdrant](<https://devfeed.tech/tags/qdrant.md>), [recommender-system](<https://devfeed.tech/tags/recommender-system.md>), [saas](<https://devfeed.tech/tags/saas.md>), [simaes-networks](<https://devfeed.tech/tags/simaes-networks.md>), [similarity](<https://devfeed.tech/tags/similarity.md>), [transformer](<https://devfeed.tech/tags/transformer.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>), [vector-search-engine](<https://devfeed.tech/tags/vector-search-engine.md>), [vectors](<https://devfeed.tech/tags/vectors.md>), [word2vec](<https://devfeed.tech/tags/word2vec.md>)

## AI overview

Qdrant announces the speaker lineup for Vector Space Day 2025 in Berlin, featuring technical sessions on vector search, benchmarking, scalable AI memory, multimodal embeddings, GraphRAG, agent evaluation, and production embedding infrastructure.

## Source excerpt

Announcing the Vector Space Day 2025 Speaker Lineup We are just days away from Vector Space Day in Berlin, and the full speaker lineup is here! This year's program spans keynotes, deep-dive technical sessions, and lightning talks, covering everything from benchmarking search engines to scalable AI memory and multimodal embeddings. Here's what to expect. Opening Keynotes The day begins with perspectives from across the ecosystem: Andre Zayarni, Andrey Vasnetsov, and Neil Kanungo sharing Qdrant's vision for the future of vector search and how devs can engage with the Qdrant Community. Robert Eichenseer (Microsoft), Kevin Cochrane (Vultr), and Inaam Syed (AWS) offering insights on how cloud, infrastructure, and developer communities are reshaping AI systems. Breakout Sessions Track A: Milky Way - Architectures, Infrastructure and Multimodal Retrieval AskNews - Building a News Sleuth for the Deep Research Paradigm: How high-performance hybrid retrieval can support investigative journalism and geopolitical risk monitoring. Delivery Hero - How to Cheat at Benchmarking Search Engines: Lessons from building reproducible benchmarking harnesses and public leaderboards. Neo4j - Hands-On GraphRAG: Practical guidance on combining knowledge graphs with RAG for more explainable retrieval. Superlinked - Beyond Text-Only: How mixture of encoders unlocks advanced retrieval using Google DeepMind's latest embeddings. Jina AI - Vision-Language Models for Embedding: Training insights for multimodal embeddings that span text, diagrams, and UI screenshots. TwelveLabs - Practical Multimodal Embeddings: Real workflows for cross-modal video search and recommendations. Baseten - High Throughput, Low Latency Embedding Pipelines: Patterns and open-source tools for production-ready embedding inference. Google DeepMind - Vector Search with Gemini and EmbeddingGemma: Deploying cutting-edge embeddings with the right indexing strategies. Track B: Andromeda - AI Workflows, Agents and Applications Link