Machine learning systems can become difficult to modify over time due to common software engineering practices. Patterns like wrapping third-party ML packages, introducing correction models to handle edge cases, and using shared tables for predictions can inadvertently create dependencies that make future updates challenging. These design choices, while sound in isolation, can lead to systems where the wrapper becomes the main component, correction models become dependent on original errors, or internal scores evolve into unchangeable interfaces. AI
IMPACT Common software engineering practices can inadvertently hinder the long-term maintainability of machine learning systems, requiring careful consideration of design choices.
RANK_REASON The item discusses software design patterns and their negative impact on the maintainability of machine learning systems, offering an opinionated perspective rather than reporting a specific event.
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