Overview

The journey of this book has shown that debugging in machine learning is far more than a technical afterthought, it is a discipline in itself. Debugging is not simply the process of fixing broken code but the deeper act of uncovering why machine learning models fail, understanding the roots of those failures, and ensuring systems behave reliably in the environments where they operate. Machine learning models are built upon layers of design decisions, data assumptions, and performance trade-offs. By the time a system reaches production, it carries with it both the strengths and the hidden weaknesses of those choices. Debugging is the practice of interrogating those assumptions, shining light on vulnerabilities, and building confidence that the system will perform as intended in the real world.

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