Taming the reproducibility crisis – Lars Albertsson, Scling

In data science, the scientific part is often forgotten – workflows, tools, and practices that are popular tend to yield experiments that cannot be repeated. Experiments are not reliable cannot tell us whether changes improve products or not. What works fine during initial development is inadequate for sustainable development of machine learning products

Key Takeaways

  • Why reproducibility matters for data science.
  • The practices and workflows that cause reproducibility problems.
  • How to build technical environments and processes that enable reproducibility and iterative development of machine learning products.

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