Vector Search at Uber - Implementing and Scaling KNN Search with Apache Lucene
Offered By: The ASF via YouTube
Course Description
Overview
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the implementation and impact of Vector Search at Uber in this 24-minute conference talk. Dive into the revolution of Search and Information Retrieval since the BERT and GPT breakthroughs in 2018. Learn how Uber incorporated Vector Search support using Apache Lucene for their Search platform, enabling critical use cases like Semantic Search and Gen AI support. Discover the process of integrating KNN search capability with the HNSW algorithm from Lucene 9 and extending it with live vector update ingestion. Gain insights into the learnings and optimizations required to scale the vector search solution to meet Uber's demanding requirements. Examine a real-world application through the collaboration between Uber's Search and Maps teams, showcasing how Geo-Semantics search significantly improved search quality by vectorizing semantic signals. Presented by Yupeng Fu, Principal Engineer at Uber, who leads the Real-time Data and Search Platform team.
Syllabus
Vector Search at Uber
Taught by
The ASF
Related Courses
Building Retrieval Augmented Generation (RAG) workflows with Amazon OpenSearch ServiceAmazon Web Services via AWS Skill Builder Build a Knowledge Based System with Vertex AI Vector Search, LangChain and Gemini
Google Cloud via Coursera Prompt Compression and Query Optimization
DeepLearning.AI via Coursera Vector Search and Embeddings - Bahasa Indonesia
Google Cloud via Coursera Vector Search and Embeddings - Deutsch
Google Cloud via Coursera