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Explore a comprehensive conference talk that addresses the challenges of transitioning data science projects from proof-of-concept to production in industry settings. Examine the reasons behind the difficulties companies face in implementing data science models, and discover a taxonomy of data use cases to assess technical requirements. Learn about three potential solutions to the two-language problem between data scientists and enterprise developers, including migrating to a single language, reimplementation, and using frameworks. Gain insights into organizational issues, quality assurance, and deployment best practices for successful data science implementation. Walk away with a better understanding of common obstacles in the data science to production pipeline and strategies to overcome them.
