Data Scientists Use Embeddings for Semantic Search and Retrieval
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
Episode 68 of The Data Science Podcast with Fexingo dives into how data scientists are using embeddings — dense vector representations of text, images, and other data — to power semantic search and information retrieval. Lucas and Luna explore a concrete case: a mid-sized e-commerce company that replaced its keyword-based search with embeddings and saw a 40% improvement in product discovery. They break down what embeddings are, how models like BERT generate them, and why cosine similarity is the go-to metric for retrieval. The hosts also discuss trade-offs like the cost of generating embeddings at scale and the rise of hybrid search systems that combine keywords with embeddings to get the best of both worlds. If you've ever wondered how modern search engines understand intent rather than just matching words, this episode gives you a clear, example-driven explanation. #DataScience #Embeddings #SemanticSearch #MachineLearning #NLP #VectorSearch #CosineSimilarity #BERT #InformationRetrieval #Ecommerce #RAG #Technology #Podcast #FexingoBusiness #BusinessPodcast #Search #DeepLearning #DataScientists Keep every episode free: buymeacoffee.com/fexingo
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