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Episode 49: Why Data and AI Still Break at Scale (and What to Do About It)

Vanishing Gradients

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Enhancing Reproducibility in Data Workflows

This chapter delves into the challenges of maintaining reproducibility in data workflows, particularly within traditional notebook environments like Jupyter. It highlights the significance of a declarative programming approach and introduces new tools that manage memory and variable states to improve collaborative efforts. The discussion covers dependency management, the balance between exploration and production, and innovative solutions for enhancing reproducibility in data science projects.

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