Ankit Vij

Measuring and tuning performance and retrieval quality for Search bars, recommender systems, entity resolution traditionally required quarters of tuning, a team of domain experts, and ongoing maintenance that kept engineers away from building the actual application. Databricks AI Search makes it simple to build a state-of-the-art retrieval system, with quality bespoke to your application. In this talk we’ll show you how easy it is to deploy a production grade Search with the latest advances in hybrid (keyword-plus-semantic) search, reranking, domain-tuned models, evaluate retrieval quality for your corpus, and build feedback loops that continuously improve your retrieval quality. You'll see what's shipped, what's next, and walk away with a production-ready stack and a working playbook for measuring and improving retrieval quality

This session is repeated. Mosaic AI Vector Search is powering high-accuracy retrieval systems in production across a wide range of use cases — including RAG applications, entity resolution, recommendation systems and search. Fully integrated with the Databricks Data Intelligence Platform, it eliminates pipeline maintenance by automatically syncing data from source to index. Over the past year, customers have asked for greater scale, better quality out-of-the-box and cost-efficient performance. This session delivers on those needs — showcasing best practices for implementing high-quality retrieval systems and revealing major product advancements that improve scalability, efficiency and relevance. What you’ll learn: - How to optimize Vector Search with hybrid retrieval and reranking for better out-of-the-box results - Best practices for managing vector indexes with minimal operational overhead - Real-world examples of how organizations have scaled and improved their search and recommendation systems