
How-ToMachine Learning
Presentation: Building Embedding Models for Large-Scale Real-World Applications
via InfoQSahil Dua
Sahil Dua discusses the critical role of embedding models in powering search and RAG applications at scale. He explains the transformer-based architecture, contrastive learning techniques, and the process of distilling large language models into production-ready student models. He shares insights on optimizing query latency, handling document indexing, and evaluating retrieval quality. By Sahil Dua
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