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Machine Learning and Technical Debt with D. Sculley

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“Changing anything changes everything.”

Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems.

D. Sculley is a software engineer at Google, focusing on machine learning, data mining, and information retrieval. He recently co-authored the paper Machine Learning: The High Interest Credit Card of Technical Debt.

Questions
  • How do you define technical debt?
  • Why does technical debt tend to compound like financial debt?
  • Is machine learning the marriage of hard-coded software logic and constantly changing external data?
  • What types of anti-patterns should be avoided by machine learning engineers?
  • What is a decision threshold in a machine learning system?
  • What advice would you give to organizations that are building their prototypes and product systems in different languages?
    • Links
    • Technical Debt
    • Adapter pattern and glue code
    • D’s research page
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