🎤 Building AI/ML Pipelines on Kubernetes 📅 Thursday, November 13 | 11:45 AM Kubernetes is now the backbone of AI infrastructure, powering workloads that extract insights, parse unstructured data, and enrich multimodal inputs across images, text, audio, and video. Lu will be

🎤 Highly Scalable AI Search Engine and AI Data Lake with Kubernetes and LanceDB 📅 Tuesday, November 11 | 5:45 PM AI applications face retrieval challenges, driving the rise of vector databases. However, AI workflows demand more—feature store retrieval and analytical queries

Lu Qiu and ChanChan Mao are heading to KubeCon + CloudNativeCon in Atlanta, GA! Don’t miss our 2 sessions this year!

@changhiskhan RSVP 👉

We’re heading to the Open Lakehouse + AI Mini Summit (Nov 13, Mountain View)! Join Chang She @changhiskhan for Scaling Multimodal AI Lakehouse with Lance & LanceDB — how the LanceDB Multimodal Lakehouse powers petabyte-scale multimodal apps with low-latency search, schema

@netflix @Uber @ByteDanceOSS @changhiskhan @ChanChan_Mao 🔗 Check out this month’s newsletter →

@netflix @Uber @ByteDanceOSS @changhiskhan @ChanChan_Mao 🎨 Project Highlight https://t.co/LgZzfVZ8qd, a new multi-index art discovery engine that lets you *search with intent*, powered by LanceDB’s hybrid vector + FTS retrieval. 🧑‍💻 Plus: Lance File 2.1 is stable: Compression added *without* sacrificing random access performance.

@netflix @Uber @ByteDanceOSS @changhiskhan @ChanChan_Mao 🧠 Product Updates • New web UI + REST APIs for Geneva clusters and manifests. • Optimized filtered reads with limit pushdown and reduced index cache footprint. • Smaller Docker images, better concurrent request monitoring, and new Geneva metrics dashboards. • lancedb-cli now

@netflix @Uber @ByteDanceOSS 📅 Upcoming Talks • Lei Xu & Pablo Delgado at Ray Summit in SF (Nov 3-5) • @changhiskhan, Weston Pace, and Jack Ye at PyData Seattle (Nov 7-9) • Lu Qiu & @chanchan_mao at KubeCon + CloudNativeCon in Atlanta, GA (Nov 10-14) • Chang She at Open Lakehouse + AI Meetup in Mountain

💫 What’s New in the LanceDB Universe (October 2025 Edition) 🫶 Community Spotlight Huge thanks to contributors from @Netflix, @Uber, and @ByteDanceOSS for advancing Lance, LanceDB, Spark, and Ray integrations.

@netflix Our session is on Day 2, Nov 5 at 4pm. Check out the agenda here:

@netflix We’ll walk through how Ray enables large-scale processing across hundreds of GPUs, while LanceDB’s columnar design provides efficient, intelligent curation and sampling. Together, they’re producing smaller, more diverse, and higher-quality datasets for cutting-edge text-to-image

@netflix In this session, we’ll share how Netflix is transforming multimodal data curation using Ray for distributed ingestion, filtering, and inference across massive video and image corpora. All powered by LanceDB as the high-performance storage and query layer.

Building and curating large-scale multimodal datasets has long been a complex, resource-heavy challenge. But that’s changing fast. Lei Xu of LanceDB and Pablo Delgado of @netflix will be speaking at Ray Summit 2025 — Scaling Multimodal Data Curation with Ray and LanceDB

I’ve been a huge fan of @pacoid for over a decade. Really excited to see the mention of @lancedb for entity resolution and linking in critical investigative applications (also, @tech_optimist !)

@netflix 5/5 The Multimodal Lakehouse lets developers focus on data and experimentation, not infrastructure. No more brittle DAGs or duplicated pipelines — just unified multimodal data at scale. For a full deep dive into the Multimodal Lakehouse, read the blog 👉

@netflix 4/5 It enables teams to: • Store + query structured & unstructured data side by side • Run feature engineering + training on multimodal datasets • Scale distributed compute using Python + Ray • Use hybrid search (vector + full-text + SQL) across captions, frames, and

@netflix 3/5 The LanceDB Multimodal Lakehouse is an AI-native architecture built to handle text, images, video, audio, and structured metadata all in one system. It merges the scalability of a lakehouse with the low-latency performance of a vector database.

@netflix 2/5 Why the Multimodal Lakehouse? Traditional data lakes were built for structured metrics tables. They break down when you add multimodal workloads: embeddings, videos, audio, captions, annotations.

1/5 @netflix leverages the Multimodal Lakehouse to power its Media Data Lake — unifying video, audio, subtitles, and metadata into one foundation for workloads like scene understanding, feature extraction, localization, and semantic search.