Product Context

RouteSphere is a global logistics platform used by dispatchers, carriers, and warehouse operators to move parcels across air, sea, and ground networks. Design an ML-driven disaster recovery system that recommends fallback routes and recovery actions when disruptions such as port closures, storms, labor strikes, customs delays, or hub outages occur.

Scale

Task

1. Clarify the product goal: what decisions the system should automate vs recommend to human operators during disruptions.
2. Design an end-to-end ML system for disruption detection, candidate recovery-plan generation, ranking, and final re-ranking under operational constraints.
3. Specify the offline and online architecture, including feature pipelines, feature store design, model training cadence, and serving path.
4. Choose models for each stage and explain tradeoffs across accuracy, latency, interpretability, and regional generalization.
5. Define offline and online evaluation, including business guardrails such as SLA adherence, cost-to-serve, and fairness across regions/customers.
6. Identify major failure modes, especially feature drift, training-serving skew, stale network state, and regional outages affecting the ML stack itself.

Constraints

• The system must continue operating during partial regional cloud outages and degraded upstream data feeds.
• Some labels are delayed by hours or days because final delivery outcomes arrive late.
• Certain recovery actions require human approval for regulated lanes or hazardous goods.
• Cost matters: only the highest-value ranking stage may use GPUs; retrieval and most scoring should run on CPUs.
• Recommendations must be auditable: operators need reason codes for why a reroute or hold decision was suggested.