
Explore strategies for integrating generative AI and language models into search applications, balancing accuracy and relevance while leveraging new capabilities in the evolving search landscape.

Learn to construct representative query sets for offline evaluation in AI/ML experimentation, enhancing understanding of algorithm impacts and streamlining A/B testing processes.

Combine BM25, neural embeddings, and customer behavior for ultimate ranking. Compare traditional, LTR, and neural search approaches in e-commerce using Amazon's ESCI dataset.

Explore practical NLP applications using Elasticsearch, including semantic vectorization, semantic search, named entity recognition, multimodal search, and automatic classification.

Explore EBSCO's journey in implementing vector search for clinical decision support, addressing challenges in precision and recall for complex medical queries.

Explore hybrid search techniques combining sparse and dense embedding models to enhance semantic search accuracy and effectiveness in various scenarios.

Explore Box's approach to delivering relevant search results across diverse enterprises using Solr and TensorFlow Ranking, balancing simplicity with privacy and security.

Explore techniques for identifying and mitigating bias in AI systems with NLP expert Arnault Gombert.

Explore AI-powered search engines with Nuclia's API, featuring generative answers and open-source NucliaDB technology.

Explore Reciprocal Rank Fusion (RRF) for combining search results, its advantages over boosting, and implementation in various systems. Learn from Philipp Krenn's expertise in search technology.

Explore strategies to mitigate positional bias in search rankings, improving relevance and fairness in ML-based systems for better user experience and search quality.

Explore innovative strategies for exploratory and diverse search using vector embeddings, enhancing user experiences beyond traditional search paradigms.

Explore neural search, text embedding models, and vector retrieval. Learn to choose models based on language, task alignment, accuracy, and cost-effectiveness. Understand potential pitfalls in embedding retrieval.

Explore hybrid search systems combining classic ranking and vector search. Learn best practices for enhancing relevancy, managing performance-accuracy tradeoffs, and scaling to production.

Explore fairness in search results with NIST Scientist Emeritus Ellen Voorhees, discussing challenges and potential solutions in information retrieval systems.

Explore AI's impact on search in 2023, covering challenges, models, and future trends in information retrieval and conversational systems.

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 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 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 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 hybrid search in RAG systems, combining metadata filtering and re-ranking for scalable LLM recommenders. Learn core concepts of retrieval, filtering, and ranking.