Pipelines breaking due to dependencies

Pipelines break when too many assumptions are hidden inside external dependencies. A library updates, a service changes behavior, or one small upstream schema shift propagates into a failure downstream.

This kind of issue is frustrating because the pipeline may have worked perfectly yesterday and fail today without any obvious model-related change. That is why dependency control and version management matter so much in production ML systems.

Resilient pipelines are built to expect change. Pinning versions, testing interfaces, and watching upstream signals closely can prevent many failures before they reach users.