How do TensorFlow systems handle cascading failures in ML inference pipelines?

Cascading failures are mitigated using circuit breakers, fallback models, and request isolation.

In ML inference pipelines, a failure in one service (e.g., feature retrieval) can propagate upstream and overload dependent systems. TensorFlow serving systems mitigate this using circuit breakers to stop repeated calls, fallback models for degraded responses, and isolation to prevent full system collapse.