# How Data Scientists Use Feature Stores for Reproducible ML

Page: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-feature-stores-for-reproducible-ml
Text version: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-feature-stores-for-reproducible-ml.md
Podcast: [The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations](https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831)
Published: 2026-07-12T21:10:35+00:00
Episode link: https://audio.fexingo.com/business/the-data-science-podcast/episode-0106.mp3
Audio file: https://audio.fexingo.com/business/the-data-science-podcast/episode-0106.mp3
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Duration seconds: 605

## Resource

In episode 106 of The Data Science Podcast, Lucas and Luna dive into the practical world of feature stores—centralized repositories for machine learning features. They explore how companies like Uber and Netflix use feature stores to ensure reproducibility, reduce duplication, and speed up model deployment. Lucas breaks down the architecture of a typical feature store, including offline and online serving, while Luna shares a real-world example from a fintech startup that cut feature engineering time by 60 percent. They also discuss the trade-offs between open-source solutions like Feast and managed offerings from cloud providers. By the end, you'll understand why feature stores are becoming a critical part of the MLOps stack. #FeatureStore #MLOps #DataScience #Reproducibility #FeatureEngineering #Uber #Netflix #Feast #MachineLearning #DataEngineering #OfflineStore #OnlineStore #FeatureServing #DataPipeline #ModelDeployment #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

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