How Modern Sportsbooks Can Use Embeddings & Reranking to Win with MongoDB and Presidio
Автор: Zachary Croteau
Загружено: 2026-02-17
Просмотров: 24
Описание:
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.
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