{"podcast":{"title":"The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations","slug":"the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","podcast_index_feed_id":7871831,"rss_url":"https://feeds.fexingo.com/business/the-data-science-podcast.xml","website_url":"https://www.fexingo.com/","image_url":"https://audio.fexingo.com/business/the-data-science-podcast/cover.png","author":"Fexingo","episode_count":118,"summary":"Lucas and Luna sit at a data-science workstation, two thin laptops open to scatter plots and clustering visualizations, and ask: what can we actually learn from the numbers? Each episode of The Data Science Podcast with Fexingo is a grounded, specific conversation about a single analytics problem or machine-learning method — from regularization in regression to the bias-variance trade-off in random forests. Lucas leads with a journalistic eye for how models are built and tested in the real world, citing actual case studies like how Netflix used matrix factorization for recommendations or how healthcare researchers apply survival analysis to clinical trials. Luna keeps the discussion honest, asking about data quality, feature engineering pitfalls, and whether a model’s accuracy actually translates to business value. They never resort to buzzwords: instead, they walk through the workflow from data collection to deployment, discussing trade-offs like interpretability versus performance. The show serves data scientists, analysts, and engineers who want to stay sharp on methods without the hype. Listeners walk away with a clearer understanding of why one algorithm beats another on a gi…","last_synced_at":"2026-07-19T08:17:23.323447+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831"},"episode":{"title":"How Data Scientists Use SBERT for Semantic Search at Scale","slug":"how-data-scientists-use-sbert-for-semantic-search-at-scale","published_at":"2026-07-10T21:06:17+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-sbert-for-semantic-search-at-scale","show_page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0102.mp3","audio_url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0102.mp3","summary":"In this episode, Lucas and Luna dive into the practical applications of Sentence-BERT (SBERT) for semantic search in production. They discuss how SBERT converts text into dense vector embeddings, enabling similarity search beyond keyword matching. The hosts walk through a real-world case study of a mid-sized e-commerce company that replaced its legacy Elasticsearch-based search with an SBERT-powered semantic search, reducing the number of searches that return zero results by 40 percent, and cutting the cost of maintaining a custom synonym list by $100,000 annually. They also cover trade-offs: the need for GPU infrastructure during embedding generation, the latency vs. accuracy balance using approximate nearest neighbor algorithms, and how fine-tuning on domain-specific data improved relevance by 15 percent. The episode closes with a reflection on when to use SBERT versus newer large language models for search. #DataScience #SemanticSearch #SBERT #SentenceBERT #NLP #VectorEmbeddings #ApproximateNearestNeighbors #Elasticsearch #Ecommerce #MachineLearning #Technology #SearchEngines #FineTuning #BERT #Embeddings #ProductionML #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo","meta_description":"In this episode, Lucas and Luna dive into the practical applications of Sentence-BERT (SBERT) for semantic search in production. They discuss how SBERT co…","key_points":[],"chapters":[],"topics":[],"duration_seconds":524,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-use-sbert-for-semantic-search-at-scale/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-sbert-for-semantic-search-at-scale.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}