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Explore dense vector search at scale, balancing cost, speed, and quality. Learn reusable methods for testing index and query performance, gaining insights for integrating vector search into existing pipelines.
Explore CLAP for semantic search on audio data. Learn to build an application generating CLAP embeddings, indexing to Opensearch, and implementing audio-based semantic queries.
Explore significant A/B test results of Vector (Hybrid) Search in e-commerce, revealing revenue increases and improved user engagement. Learn about embedding creation and result blending techniques.
Explore Apache Lucene's 22-year journey from text indexing to AI integration, covering its impact on search technologies and recent innovations in vector indexing and search.
Explore a framework for testing generative AI search results and summaries using subject matter experts, including methodology, metrics, and potential applications.
Explore the benefits and challenges of hybrid search systems, combining vector search with traditional methods for improved accuracy and real-time updates in search applications.
Explore Reddit's journey in implementing Learning to Rank for site-wide search, including challenges faced, lessons learned, and strategies for scaling to high query volumes.
Explore AI's impact on search in 2023, covering challenges, models, and future trends in information retrieval and conversational systems.
Explore fairness in search results with NIST Scientist Emeritus Ellen Voorhees, discussing challenges and potential solutions in information retrieval systems.
Explore methods to evaluate embedding-based retrieval quality using organizational knowledge, improving relevance and expanding recall for e-commerce search beyond historical data.
Explore SetFit for few-shot learning in classification and ranking, with a focus on legal research applications. Discover its potential to outperform GPT-3 in label-scarce settings.
Explore challenges and solutions for integrating inverted and ANN indexes in search engines, optimizing hybrid search systems for efficiency and scalability.
Explore scaling multimodal embeddings, retrieval, and generation using vector databases. Learn cross-modal search techniques and guide large language models with real-time retrieval.
Explore ML-powered search development through hypothesis-driven approaches. Learn from Uzum Market's journey in building a large-scale search system for e-commerce, focusing on metrics, evaluation, and AB testing.
Explore strategies for mitigating zero search results using alternative queries, including query relaxation and term substitution based on Large Language Models and query term statistics.
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