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Post Deployment Data Science with Wojtek Kuberski #56

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Ensuring Model Integrity Post-Deployment

This chapter highlights the necessity of monitoring machine learning models after they are deployed to avoid silent failures that can harm businesses. It covers the integration of models into secure platforms for performance tracking, while addressing challenges like causal relationships and the importance of interpretability in data analysis.

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