Try MongoDB 8.0 → https://trymongodb.com/3WGNhFQ Subscribe to MongoDB YouTube→ https://mdb.link/subscribe Vector embeddings are poised to power a variety of real-time use cases, from RAG to Semantic Search to Fraud detection. You saw the demos, but do you know what it actually takes to launch vector-powered systems into production? Hopefully, you are not thinking about “adding heuristic filters”, “building complex ranking models” or “fine-tuning LLMs”. You need a way to combine large pre-trained models that process the unstructured parts of your data like text and images with models trained on your structured data - clicks, relationships, timestamps and beyond. You need a way to express your objective when you generate the search vector. And importantly - you need to build feedback loops. In this session, we will talk about the design of production-grade vector-powered systems that are easy to control, easy to deploy and powerful enough to give your users what they really want, and how Superlinked's open-source framework makes Vector Search more accessible to everyone in the MongoDB ecosystem. Visit Mongodb.com → https://www.mongodb.com Read the MongoDB Blog → https://www.mongodb.com/blog Check out the MongoDB Developer Center → https://www.mongodb.com/developer

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