Deep explanation
Production incident: Hugging Face outage in a compliance review automation system
Senior incident question: recover a compliance pipeline that depends on Hugging Face Hub downloads and Inference Endpoints, covering startup failures, autoscaling cost spikes, cold starts, retries, offline model caching, and safe backlog handling.
Quick answer
First I stop the spiral: cap endpoint autoscaling and worker restarts so cost stops climbing, cut retries, and make sure every failed document stays pending rather than being marked reviewed. Then I check whether the Hub was unreachable or rate limiting downloads, which crashes new workers, and whether the endpoint scaled up replicas or kept cold-starting under load. Long term, models are baked into images or an internal mirror with pinned revisions and offline mode, autoscaling has limits and alerts, and the pipeline queues work instead of failing it.
The interview question
Your compliance review system runs open-weights models on Hugging Face Inference Endpoints, and its worker containers download a classifier and tokenizer from the Hugging Face Hub when they start. At peak traffic, requests start failing, new workers crash on startup, the endpoint bill for the day is double normal, and reviewers report a growing backlog. Describe your first 30 minutes, the likely root causes, and how you would remove these dependencies on the hot path.
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