Describe an ML workflow with massive egress fees and re-architecture to mitigate
Tests whether you recognize egress spikes when storage and compute cross cloud or region boundaries. Great answers sketch a multi-cloud training pipeline, cite per-GB rates, and propose caching or compute placement. Red flag: suggesting compression alone.
Tests whether you recognize egress charges explode when storage and compute live across provider, region, or hybrid boundaries, and whether you can redesign data locality not just discounts. A strong answer outlines a scenario like a 10TB dataset in one cloud being read by GPU clusters in another, quantifies the blast radius at nine cents per GB, and proposes caching replicas near compute, moving compute to the data, or shifting to a billing model that removes per-GB egress line items.
Read the original → akave.com
- #mlops
- #cloud-cost
- #data-egress
- #architecture
- #infrastructure
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