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Creating tested, reliable AI applications (Practical AI #295)

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Navigating Data Science Evolution

This chapter explores the evolution of data science practices, particularly the role of Jupyter notebooks in code experimentation. It addresses the challenges of reproducibility in AI workflows and emphasizes the necessity of transitioning to structured, production-ready code. The conversation also reflects on the changing landscape of AI engineering, the limitations of low code/no code tools, and the potential shift from Python to other programming languages like Rust for specific applications.

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