Technical Debt Looks Different in ML
Describe the distinctive technical debt patterns that make ML systems hard to maintain.
Small code changes can be large model-system changes. Debt Patterns to Watch CACE means a change in one part can ripple through the learned system. Glue code means orchestration logic grows around the model without durable interfaces. Pipeline jungles mean transformations spread across jobs with unclear lineage. Feedback loops mean model outputs influence the future data the model learns from. Why It Works Naming these patterns helps teams review ML changes as system changes. Instead of asking only "does the code run," reviewers ask "what behavior, data, or consumer could shift because of this?" Common Trap The dangerous review is…
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