How Data Scientists Use MLOps to Keep Models in Production
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
Episode 62 of The Data Science Podcast dives into the operational side of machine learning: MLOps. Lucas and Luna explore why so many models never make it to production, and how tools like feature stores, model registries, and automated pipelines keep deployed models accurate and reliable. They walk through a real case from a mid-sized fintech company that cut model deployment time from weeks to hours using CI/CD for ML. Along the way, they touch on monitoring drift, versioning data, and the cultural shift from research to engineering. If you've ever wondered why building a model is only half the battle, this episode gives you the other half. #DataScience #MLOps #MachineLearning #AIEngineering #ModelDeployment #FeatureStore #ModelRegistry #CICD #DataPipeline #ModelDrift #ProductionML #Fintech #Tech #BusinessPodcast #FexingoBusiness #Podcast #DataEngineering #DevOps Keep every episode free: buymeacoffee.com/fexingo
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