Why Your Data Models Fail in Production
transcript
show notes
In this episode of The Data Science Podcast, Lucas and Luna dissect the critical gap between training environments and real-world deployment. They explore how data teams can implement effective feature stores to ensure consistency, using concrete examples from major fintech companies. The discussion covers monitoring strategies for latency and drift, emphasizing practical steps to bridge the gap between prototype and production. Tune in to learn why most model failures are operational, not algorithmic.
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