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Ryan Orban

Ryan Orban

Subject
2 entries

Technical Debt

Bookmarks

  1. Hidden Technical Debt in Machine Learning Systems

    Sculley, Holt, Golovin et al. at Google extend the software engineering concept of technical debt to ML systems, identifying ML-specific forms that accumulate invisibly at the system level: boundary erosion, entanglement, hidden feedback loops, undeclared consumers, and data dependencies. The canonical paper explaining why ML systems are uniquely expensive to maintain.

  2. The Perilous World of ML: Pipeline Jungles and Hidden Feedback Loops

    John Foreman on the hidden technical debt in ML systems — pipeline jungles and feedback loops that make production ML fragile in ways that pure model metrics never reveal. Anticipates the 'Hidden Technical Debt in Machine Learning Systems' Google paper by months.

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