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.
- 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?
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