How Modern Sportsbooks Can Use Embeddings & Reranking to Win with MongoDB and Presidio скачать в хорошем качестве

How Modern Sportsbooks Can Use Embeddings & Reranking to Win with MongoDB and Presidio

In this enablement session, we break down practical, high-impact use cases for embeddings and reranking models within online sportsbook environments. This session covers: • How vector embeddings power smarter search and retrieval • Improving personalization and player experience with contextual intelligence • Where reranking models create measurable lift in engagement and conversion • Architectural considerations when building AI-driven retrieval systems • Why the storage layer matters in modern AI applications Whether you're in product, engineering, data, or platform architecture, this session focuses on real-world patterns sportsbook operators can apply today — not just theory. If you're exploring AI-powered search, personalization, contextual bandits, or RAG-style systems inside high-scale, real-time environments, this walkthrough will give you a clear framework to think about implementation.