Episode 32 of The Data Science Podcast explores the critical difference between deploying a model and keeping it relevant. Lucas and Luna break down why most ML teams treat model retraining as a fire drill instead of a scheduled process. They walk through the real-world case of an e-commerce company whose recommendation engine silently deteriorated over six months because no one monitored feature drift. The hosts explain practical patterns: scheduled retraining vs. trigger-based retraining, why locking training data snapshots prevents reproducibility disasters, and how one team used a simple dashboard to catch a 12 percent accuracy drop before it hit revenue. They also discuss the human side: why data scientists resist automation, and how the industry is slowly moving toward MLOps standards that treat model maintenance like software maintenance. A concrete, actionable episode for anyone who has ever wondered what happens after the model goes into production.

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