This is the first of two articles.
What happens when your machine learning system quietly breaks, and no one notices? Unlike traditional systems that crash loudly and obviously, AI systems tend to fail silently and subtly. They don’t always throw errors, log stack traces or trigger alerts. Instead, they degrade — quietly, over time — until the damage is done.
This silent failure can be devastating.
Imagine a recommendation engine that begins drifting due to stale feature inputs, continuing to serve irrelevant suggestions for weeks. Or…
