Solving Multi-Tenancy In Vector Search Requires A Paradigm Shift, Etienne Dilocker, CTO, Weaviate скачать в хорошем качестве

Solving Multi-Tenancy In Vector Search Requires A Paradigm Shift, Etienne Dilocker, CTO, Weaviate

Solving Multi-Tenancy In Vector Search Requires A Paradigm Shift Vector-based retrieval has become a foundation piece of generative AI. From an engineering perspective vector search differs from traditional search which led to the uprising of new vector-native databases and vector search integrations into existing databases. Brain icon for The AI Conference 2023, a groundbreaking two-day event on AGI, LLMs, Infrastructure, Alignment, AI Startups, and Neural Architectures. When AI startups and AI-native larger companies use vector search they typically have multi-tenancy cases; they have users that have data that needs to be isolated from that of other users. In the recent past it was only possible to solve this with workarounds: Using filtered vector search or isolating tenants by putting them in dedicated collections or namespaces. But those approaches were full of disadvantages and didn’t scale to millions of tenants with billions of vectors. To solve multi-tenancy in vector search – and enable the generative AI startup of the future – a paradigm shift is needed. About Etienne Dilocker Etienne co-founded Weaviate where he is currently the CTO and oversees building the Weaviate Vector Database. Co-Founding Weaviate was Etienne's entry point into the AI world, where he could combine his passion for cloud-native engineering with the new challenges of the AI age. Prior to Weaviate, Etienne lead engineering teams building cloud-native applications for multi-national enterprise companies in the finance, retail, and wholesale industry. Etienne is passionate about deep tech and modern architectures made for the ever-changing demands of today and tomorrow. Video Recorded at The AI Conference. Copyright, The AI Conference, All Rights Reserved