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12min chapter
Creating tested, reliable AI applications
Practical AI
Navigating the Challenges of Interactive Notebooks in Data Science
This chapter explores the difficulties associated with using interactive notebooks like Jupyter for data science model development, emphasizing issues such as unreliable code quality from non-linear workflows. It also discusses the evolution towards more structured coding practices necessary for production-ready AI applications, highlighting the role of low-code and no-code tools. Moreover, the chapter examines the importance of choosing appropriate programming languages and integration strategies for successful deployment in real-world environments.
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Discover
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Listen to the best highlights from the podcasts you love and dive into the full episode