Model Deployment Challenges
Transitioning from a Kaggle-winning model to real-world deployment presents significant challenges, particularly in ensuring long-term model robustness. Key issues include managing data set shifts and addressing fairness and inclusivity, which are often more complex than the technical aspects of getting a model into production.In this clip
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Gradient Dissent - A Machine Learning Podcast
D. Sculley — Technical Debt, Trade-offs, and Kaggle
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