Hall of FameD. Sculley et al.201525 min readpaperintermediate
Hidden Technical Debt in Machine Learning Systems
Summary
This paper introduces the concept of technical debt in machine learning systems, arguing that ML systems accrue unique and significant maintenance costs beyond traditional software engineering. It identifies several ML-specific risk factors like entanglement, hidden feedback loops, and data dependencies that erode system boundaries and increase long-term operational expenses.
- ML systems are prone to "CACE" (Changing Anything Changes Everything) due to signal entanglement, making isolated improvements difficult.
- Correction cascades, where models are built on top of other models, can create fragile dependencies and "improvement deadlocks".
- Undeclared consumers of model outputs create hidden tight coupling and feedback loops, increasing the cost of changes.
- Data dependencies are harder to track than code dependencies and can be unstable (changing over time) or underutilized (legacy/bundled features).
This foundational paper is essential for any engineer designing, building, or maintaining real-world machine learning systems to understand common pitfalls and anticipate long-term costs.
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