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Table of Contents
1. Introduction
2. Complex Models Erode Boundaries
3. Data Dependencies Cost More than Code Dependencies
4. Feedback Loops
5. ML-System Anti-Patterns
Summary
Machine learning offers a powerful toolkit for building complex prediction systems quickly but comes with hidden technical debt. This paper discusses the ongoing maintenance costs incurred in real-world ML systems due to various risk factors like boundary erosion, entanglement, hidden feedback loops, undeclared consumers, and more. It emphasizes the importance of addressing system-level technical debt in ML systems and highlights the challenges in maintaining such systems over time.